From 358b8647f59441f72e1633d5b62d775b8c42b0e6 Mon Sep 17 00:00:00 2001
From: calesthio <celesthioailabs@gmail.com>
Date: Wed, 1 Apr 2026 10:00:15 -0700
Subject: [PATCH] Talking-head pipeline: 8 new tools, Remotion TalkingHead
 composition, and skill rewrites

New tools: face_tracker, visual_qa, eye_enhance, auto_reframe, remotion_caption_burn, showcase_card, silence_cutter. Updated audio_mixer with segmented_music operation and subtitle_gen with ASR corrections. Registered TalkingHead composition in Root.tsx. Rewrote compose/edit/scene director skills for full enhancement chain, Remotion captions, multi-clip assembly, and visual QA. Gitignore cleanup: exclude test demo-props, downloaded music, and generated images.
---
 .gitignore                                    |   7 +
 generated_image.png                           | Bin 302181 -> 0 bytes
 pipeline_defs/talking-head.yaml               |  31 +-
 remotion-composer/src/Root.tsx                |  16 +
 remotion-composer/src/TalkingHead.tsx         |  35 ++
 .../talking-head/compose-director.md          | 204 ++++++-
 .../pipelines/talking-head/edit-director.md   |  38 +-
 .../pipelines/talking-head/scene-director.md  |  52 +-
 tools/analysis/face_tracker.py                | 314 ++++++++++
 tools/analysis/visual_qa.py                   | 343 +++++++++++
 tools/audio/audio_mixer.py                    | 152 ++++-
 tools/enhancement/eye_enhance.py              | 578 ++++++++++++++++++
 tools/subtitle/subtitle_gen.py                |  51 ++
 tools/video/auto_reframe.py                   | 537 ++++++++++++++++
 tools/video/remotion_caption_burn.py          | 439 +++++++++++++
 tools/video/showcase_card.py                  | 226 +++++++
 tools/video/silence_cutter.py                 | 481 +++++++++++++++
 17 files changed, 3467 insertions(+), 37 deletions(-)
 delete mode 100644 generated_image.png
 create mode 100644 remotion-composer/src/TalkingHead.tsx
 create mode 100644 tools/analysis/face_tracker.py
 create mode 100644 tools/analysis/visual_qa.py
 create mode 100644 tools/enhancement/eye_enhance.py
 create mode 100644 tools/video/auto_reframe.py
 create mode 100644 tools/video/remotion_caption_burn.py
 create mode 100644 tools/video/showcase_card.py
 create mode 100644 tools/video/silence_cutter.py

diff --git a/.gitignore b/.gitignore
index d98efa3..4234a0f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -44,6 +44,10 @@ skills-lock.json
 
 # Generated images (logo drafts — final logo lives in assets/)
 logo_*.png
+generated_image.png
+
+# Downloaded music files (Pixabay, Freesound — not source code)
+pixabay_music_*
 
 # OS
 .DS_Store
@@ -57,3 +61,6 @@ remotion-composer/out/
 remotion-composer/public/*
 # But keep demo props (shipped with the project for zero-key demos)
 !remotion-composer/public/demo-props/
+# Ignore test/scratch compositions in demo-props (keep only curated demos)
+remotion-composer/public/demo-props/test-*
+remotion-composer/public/demo-props/talking-head-*
diff --git a/generated_image.png b/generated_image.png
deleted file mode 100644
index 58012fbcca23550edc0b205c99a13255fe3ac42b..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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diff --git a/pipeline_defs/talking-head.yaml b/pipeline_defs/talking-head.yaml
index 6bc53a2..7445340 100644
--- a/pipeline_defs/talking-head.yaml
+++ b/pipeline_defs/talking-head.yaml
@@ -78,8 +78,12 @@ stages:
     required_tools: []
     optional_tools:
       - frame_sampler    # representative frame extraction
+      - face_tracker     # face position analysis for reframing decisions
+      - silence_cutter   # silence detection for jump cut planning
     tools_available:
       - frame_sampler
+      - face_tracker
+      - silence_cutter
     checkpoint_required: true
     human_approval_default: true
     review_focus:
@@ -126,7 +130,12 @@ stages:
       - script
     produces:
       - edit_decisions
-    tools_available: []
+    optional_tools:
+      - silence_cutter   # auto jump cuts on silent segments
+      - video_trimmer    # speed control (0.5x–2x), cut, concat
+    tools_available:
+      - silence_cutter
+      - video_trimmer
     checkpoint_required: true
     human_approval_default: false
     review_focus:
@@ -149,17 +158,35 @@ stages:
       - render_report
     required_tools:
       - video_compose    # rendering
-      - audio_mixer      # audio layering
+      - audio_mixer      # audio layering + segmented music
     optional_tools:
       - face_enhance     # face enhancement
+      - eye_enhance      # eye brightening, dark circle removal
       - color_grade      # color correction
       - audio_enhance    # audio cleanup
+      - auto_reframe     # aspect ratio conversion with face tracking
+      - face_tracker     # face position data for reframing
+      - silence_cutter   # auto jump cuts
+      - video_trimmer    # speed control (0.5x–2x)
+      - remotion_caption_burn  # animated word-by-word captions via Remotion
+      - showcase_card    # letterboxed showcase cards with typography
+      - video_stitch     # multi-clip assembly with transitions
+      - visual_qa        # frame extraction + quality validation
     tools_available:
       - video_compose
       - audio_mixer
       - face_enhance
+      - eye_enhance
       - color_grade
       - audio_enhance
+      - auto_reframe
+      - face_tracker
+      - silence_cutter
+      - video_trimmer
+      - remotion_caption_burn
+      - showcase_card
+      - video_stitch
+      - visual_qa
     checkpoint_required: true
     human_approval_default: false
     review_focus:
diff --git a/remotion-composer/src/Root.tsx b/remotion-composer/src/Root.tsx
index 531ad6a..9033d52 100644
--- a/remotion-composer/src/Root.tsx
+++ b/remotion-composer/src/Root.tsx
@@ -5,6 +5,7 @@ import {
   calculateCinematicMetadata,
 } from "./CinematicRenderer";
 import { signalFromTomorrowWithMusicFixture } from "./cinematic/fixtures";
+import { TalkingHead, TalkingHeadProps } from "./TalkingHead";
 
 const calculateMetadata: CalculateMetadataFunction<ExplainerProps> = async ({
   props,
@@ -61,6 +62,21 @@ export const Root: React.FC = () => {
         defaultProps={signalFromTomorrowWithMusicFixture}
         calculateMetadata={calculateCinematicMetadata}
       />
+      <Composition
+        id="TalkingHead"
+        component={TalkingHead}
+        durationInFrames={30 * 30}
+        fps={30}
+        width={1080}
+        height={1920}
+        defaultProps={{
+          videoSrc: "",
+          captions: [],
+          wordsPerPage: 4,
+          fontSize: 52,
+          highlightColor: "#22D3EE",
+        }}
+      />
     </>
   );
 };
diff --git a/remotion-composer/src/TalkingHead.tsx b/remotion-composer/src/TalkingHead.tsx
new file mode 100644
index 0000000..9128fce
--- /dev/null
+++ b/remotion-composer/src/TalkingHead.tsx
@@ -0,0 +1,35 @@
+import { AbsoluteFill, OffthreadVideo } from "remotion";
+import { CaptionOverlay, WordCaption } from "./components/CaptionOverlay";
+
+export interface TalkingHeadProps {
+  videoSrc: string;
+  captions: WordCaption[];
+  wordsPerPage?: number;
+  fontSize?: number;
+  highlightColor?: string;
+}
+
+export const TalkingHead: React.FC<TalkingHeadProps> = ({
+  videoSrc,
+  captions,
+  wordsPerPage = 4,
+  fontSize = 52,
+  highlightColor = "#22D3EE",
+}) => {
+  return (
+    <AbsoluteFill style={{ backgroundColor: "#000" }}>
+      <OffthreadVideo
+        src={videoSrc}
+        style={{ width: "100%", height: "100%", objectFit: "cover" }}
+      />
+      <CaptionOverlay
+        words={captions}
+        wordsPerPage={wordsPerPage}
+        fontSize={fontSize}
+        highlightColor={highlightColor}
+        backgroundColor="rgba(0, 0, 0, 0.65)"
+        color="#FFFFFF"
+      />
+    </AbsoluteFill>
+  );
+};
diff --git a/skills/pipelines/talking-head/compose-director.md b/skills/pipelines/talking-head/compose-director.md
index 5f699cd..c4ae7a8 100644
--- a/skills/pipelines/talking-head/compose-director.md
+++ b/skills/pipelines/talking-head/compose-director.md
@@ -18,51 +18,215 @@ You have edit decisions and an asset manifest. Your job is to render the final t
 ### Step 1: Run Enhancement Chain
 
 Apply video enhancements in order:
-1. **Face enhancement** (if face_enhance tool available) — sharpen faces
-2. **Color grading** (if color_grade tool available) — apply a profile
-3. **Audio enhancement** (if audio_enhance tool available) — noise reduction, normalization
+1. **Face enhancement** (if `face_enhance` tool available) — apply `talking_head_standard` preset
+2. **Eye enhancement** (if `eye_enhance` tool available) — under-eye dark circle removal + eye brightening
+3. **Color grading** (if `color_grade` tool available) — apply a profile
+4. **Audio enhancement** (if `audio_enhance` tool available) — noise reduction, normalization
 
 Each step is optional — check tool availability first.
 
-### Step 2: Burn Subtitles
+**Eye enhancement** — removes under-eye dark circles and brightens eyes using MediaPipe Face Mesh landmark detection:
+```
+eye_enhance.execute({
+    "input_path": "<face_enhanced_video>",
+    "output_path": "<project>/assets/video/eye_enhanced.mp4",
+    "operations": ["dark_circles", "brighten_eyes"],
+    "dark_circle_intensity": 0.4,       # 0-1, subtle is better
+    "eye_brighten_intensity": 0.3,
+})
+```
+**Important:** Keep intensities low (0.2-0.5). Over-processing makes eyes look unnatural. Always compare before/after.
 
+### Step 1b: Speed Adjustment (if requested)
+
+If the user wants the video sped up or slowed down, use `video_trimmer`:
+```
+video_trimmer.execute({
+    "operation": "speed",
+    "input_path": "<enhanced_video>",
+    "output_path": "<project>/assets/video/speed_adjusted.mp4",
+    "speed_factor": 1.25    # 0.5x (slow), 1.25x, 1.5x, 2x (fast)
+})
+```
+
+Common speed factors:
+| Factor | Use Case |
+|--------|----------|
+| `0.5` | Slow-mo for dramatic effect |
+| `1.0` | Normal (no change) |
+| `1.25` | Slightly faster — tighter pacing without sounding unnatural |
+| `1.5` | Noticeably faster — good for recaps or condensed content |
+| `2.0` | Double speed — time-lapse effect |
+
+Apply speed AFTER enhancements, BEFORE reframing.
+
+### Step 2: Auto-Reframe (if target platform requires it)
+
+If the target platform requires a different aspect ratio (e.g. Instagram Reels = 9:16), use `auto_reframe`:
+
+```
+auto_reframe.execute({
+    "input_path": "<enhanced_video>",
+    "output_path": "<project>/renders/reframed.mp4",
+    "target_aspect": "portrait",       # 9:16 for Reels/TikTok/Shorts
+    "smoothing_window": 15,            # smooth camera pan
+    "face_padding": 0.4,              # 40% padding around face
+})
+```
+
+**Aspect ratio presets:**
+| Preset | Ratio | Platform |
+|--------|-------|----------|
+| `portrait` | 9:16 | Instagram Reels, TikTok, YouTube Shorts |
+| `square` | 1:1 | Instagram Feed |
+| `landscape` | 16:9 | YouTube, LinkedIn |
+| `vertical_4_5` | 4:5 | Instagram portrait post |
+
+The tool automatically runs face detection and keeps the speaker centered. If MediaPipe is not installed, falls back to center-crop.
+
+**Important:** Run auto_reframe AFTER face_enhance and color_grade but BEFORE burning subtitles. Subtitles need to be positioned for the final aspect ratio.
+
+### Step 3: Burn Subtitles
+
+**Preferred: Remotion captions** (if `remotion_caption_burn` tool available):
+```
+remotion_caption_burn.execute({
+    "input_path": "<reframed_or_enhanced_video>",
+    "output_path": "<project>/assets/video/captioned.mp4",
+    "segments": <transcript_segments_from_asset_manifest>,
+    "corrections": {"cloud": "Claude", "co-pilot": "Copilot"},
+    "words_per_page": 4,
+    "font_size": 52,
+    "highlight_color": "#22D3EE",
+})
+```
+Remotion renders animated word-by-word captions at the bottom of the frame with active word highlighting. Captions are positioned away from the face.
+
+**Fallback: FFmpeg subtitles** (if Remotion unavailable):
 Use `video_compose` with `burn_subtitles` operation:
-- Input: enhanced video (or raw if no enhancements)
+- Input: reframed video (or enhanced video if no reframe needed)
 - Subtitle file from asset manifest
 - Style from playbook
+- For vertical (9:16) output: position subtitles in the lower 20% of frame with `MarginV=100`
+- **Never** position subtitles in the center of the frame — they will occlude the face
 
-### Step 3: Mix Audio
+### Step 3b: Build Showcase Cards (if multi-clip reel)
 
-Use `audio_mixer` to:
-- Layer original audio with any background music
+If the output is a reel with showcase clips, use `showcase_card` for each:
+```
+showcase_card.execute({
+    "input_path": "<showcase_video>",
+    "output_path": "<project>/assets/video/sc_<name>.mp4",
+    "title": "VIDEO TITLE",
+    "subtitle": "Description | Style | Cost: $0.15",
+    "background_color": "0x0A0F1A",
+})
+```
+This creates letterboxed 9:16 cards with typography.
+
+### Step 4: Assemble Multi-Clip (if applicable)
+
+If the output has multiple segments (e.g. talking head + showcase clips), use `video_stitch`:
+```
+video_stitch.execute({
+    "operation": "stitch",
+    "clips": ["intro.mp4", "showcase1.mp4", ..., "outro.mp4"],
+    "output_path": "<project>/renders/assembled.mp4",
+    "transition": "crossfade",         # or "fade" for fade-through-black
+    "transition_duration": 0.5,
+})
+```
+**Transition guidance:**
+- `crossfade` (fade): smooth blend between talking head and showcase
+- `fade` (fade-through-black): brief dip to black between showcase clips
+- Mix transition types: use `crossfade` for talk→showcase, `fade` between showcases
+
+### Step 5: Mix Audio
+
+Use `audio_mixer` to layer background music:
+
+**For multi-clip reels** — use `segmented_music` to play music only during talking head sections:
+```
+audio_mixer.execute({
+    "operation": "segmented_music",
+    "video_path": "<assembled_video>",
+    "music_path": "<bg_music>",
+    "music_volume": 0.20,
+    "segments": [
+        {"start": 0, "end": 17.0},       # intro speech
+        {"start": 167.0, "end": 175.0}    # outro speech
+    ],
+    "fade_duration": 0.5,
+    "output_path": "<project>/renders/final.mp4",
+})
+```
+
+**For single talking-head videos** — use `duck` or `full_mix`:
+- Layer original audio with background music
 - Apply ducking if music is present
 - Normalize final levels
 
-### Step 4: Final Encode
+### Step 6: Final Encode
 
 Use `video_compose` with `encode` operation:
-- Apply target media profile (youtube_landscape, tiktok, etc.)
+- Apply target media profile (youtube_landscape, tiktok, instagram_reels, etc.)
 - Two-pass encoding for quality
 
-### Step 5: Verify Output
+### Step 7: Visual QA
 
-- Check file exists and is playable
-- Verify duration matches expectations
-- Check audio is present
+Use `visual_qa` to verify the output before declaring success:
+```
+visual_qa.execute({
+    "operation": "review",
+    "input_path": "<final_video>",
+    "timestamps": [3.0, 10.0, 25.0, 50.0, 100.0, 170.0],
+})
+```
+Then **read each extracted frame** to verify:
+- Captions are visible and positioned at the bottom (not on the face)
+- Face enhancement is applied (skin looks smooth, not over-processed)
+- Transitions are clean (no artifacts at transition points)
+- Showcase cards have readable typography
 
-### Step 6: Build Render Report
+Also run probe validation:
+```
+visual_qa.execute({
+    "operation": "probe",
+    "input_path": "<final_video>",
+    "expected": {
+        "width": 1080, "height": 1920,
+        "has_audio": true,
+        "pixel_format": "yuv420p"
+    },
+})
+```
 
-Document output: path, format, resolution, duration, file size.
+And check audio levels:
+```
+visual_qa.execute({
+    "operation": "audio_levels",
+    "input_path": "<final_video>",
+    "timestamps": [5.0, 50.0, 170.0],
+})
+```
+Verify: speech sections have higher volume than showcase sections (confirms music placement).
 
-### Step 7: Self-Evaluate
+### Step 8: Build Render Report
+
+Document output: path, format, resolution, duration, file size, QA results.
+
+### Step 9: Self-Evaluate
 
 | Criterion | Question |
 |-----------|----------|
 | **Playability** | Does the video play without errors? |
 | **Quality** | Are enhancements applied correctly? |
-| **Audio** | Is speech clear with balanced levels? |
-| **Subtitles** | Are subtitles visible and synced? |
+| **Framing** | If reframed — is the face centered? No important content cropped? |
+| **Audio** | Is speech clear with balanced levels? Music only during intended segments? |
+| **Subtitles** | Are captions visible at the bottom? Not occluding the face? Word highlighting working? |
+| **Transitions** | Are transitions clean? Correct type (crossfade vs fadeblack)? |
+| **Showcase** | Are showcase cards properly letterboxed with readable typography? |
 
-### Step 8: Submit
+### Step 10: Submit
 
 Validate the render_report against the schema and persist via checkpoint.
diff --git a/skills/pipelines/talking-head/edit-director.md b/skills/pipelines/talking-head/edit-director.md
index 9e2613b..a80316b 100644
--- a/skills/pipelines/talking-head/edit-director.md
+++ b/skills/pipelines/talking-head/edit-director.md
@@ -14,39 +14,63 @@ You have a scene plan and asset manifest. Your job is to assemble the edit decis
 
 ## Process
 
-### Step 1: Define Primary Cut
+### Step 1: Apply Silence Cuts (if planned)
+
+If the scene plan includes silence removal, run `silence_cutter` before defining cuts:
+
+```
+silence_cutter.execute({
+    "input_path": "<raw_footage>",
+    "mode": "remove",           # or "speed_up" for less jarring result
+    "silence_threshold_db": -35,
+    "min_silence_duration": 0.5,
+    "padding_seconds": 0.08,    # prevents clipped words
+    "output_path": "<project>/assets/video/footage_cut.mp4"
+})
+```
+
+**Choosing the mode:**
+- `remove` — Hard jump cuts. Best for fast-paced social content (Reels, TikTok, Shorts)
+- `speed_up` — Fast-forwards through silence at 6x. Less jarring for longer-form content (YouTube, LinkedIn)
+
+Present the result to the user: "Removed X seconds of silence (Y%) — output is now Z seconds."
+
+Use the cut footage as the source for all subsequent steps.
+
+### Step 2: Define Primary Cut
 
 For talking-head, the primary cut is usually the full footage (or trimmed segments). Create cuts that:
-- Reference the raw footage as source
+- Reference the raw footage (or silence-cut footage) as source
 - Use timestamps from the script sections
 - Apply any trim decisions (cut dead air, false starts)
 
-### Step 2: Configure Subtitles
+### Step 3: Configure Subtitles
 
 - Enable subtitles with playbook-compatible styling
 - Reference the subtitle asset from the manifest
 - Set position (usually bottom-center)
 
-### Step 3: Configure Audio
+### Step 4: Configure Audio
 
 - Set narration to the raw footage audio
 - If background music is desired, configure ducking
 - Set music volume per playbook
 
-### Step 4: Plan Enhancements
+### Step 5: Plan Enhancements
 
 If the scene plan includes overlays:
 - Add overlay cuts for text cards, lower thirds
 - Time them to match speech content
 
-### Step 5: Self-Evaluate
+### Step 6: Self-Evaluate
 
 | Criterion | Question |
 |-----------|----------|
 | **Coverage** | Do cuts span the full intended duration? |
+| **Silence** | Were silence cuts applied if planned? What % was removed? |
 | **Subtitles** | Are subtitles enabled and styled? |
 | **Audio** | Is audio configuration complete? |
 
-### Step 6: Submit
+### Step 7: Submit
 
 Validate the edit_decisions against the schema and persist via checkpoint.
diff --git a/skills/pipelines/talking-head/scene-director.md b/skills/pipelines/talking-head/scene-director.md
index 707d682..7da3931 100644
--- a/skills/pipelines/talking-head/scene-director.md
+++ b/skills/pipelines/talking-head/scene-director.md
@@ -11,28 +11,70 @@ You have a script (from transcription) and raw footage. Your job is to create a
 | Schema | `schemas/artifacts/scene_plan.schema.json` | Artifact validation |
 | Prior artifacts | Script, Brief | Section timing and context |
 | Tools | `frame_sampler` (optional) | Extract representative frames |
+| Tools | `face_tracker` (optional) | Analyze speaker face position for reframing |
+| Tools | `silence_cutter` (optional) | Detect silence for jump cut planning |
 
 ## Process
 
-### Step 1: Plan Base Scenes
+### Step 1: Analyze Footage (if tools available)
+
+**Face tracking** — If `face_tracker` is available, run it on the raw footage:
+```
+face_tracker.execute({
+    "input_path": "<raw_footage>",
+    "sample_fps": 5
+})
+```
+This outputs per-frame face bounding boxes. Use this data to:
+- Decide if reframing is needed (e.g. speaker is off-center for vertical crop)
+- Identify sections where the speaker moves significantly (needs dynamic crop)
+- Note face position for auto_reframe in the compose stage
+
+**Silence detection** — If `silence_cutter` is available, run in `mark` mode:
+```
+silence_cutter.execute({
+    "input_path": "<raw_footage>",
+    "mode": "mark",
+    "silence_threshold_db": -35,
+    "min_silence_duration": 0.5
+})
+```
+This outputs silence/speech segment timestamps. Use this to:
+- Plan which segments should be jump-cut or sped up
+- Identify dead air, false starts, and long pauses
+- Estimate the final video duration after cuts
+- Present the user with a summary: "Found X seconds of silence across Y segments — recommend removing?"
+
+### Step 2: Plan Base Scenes
 
 For talking-head, the base is simple: one scene per script section, all type `talking_head`. The raw footage IS the scene.
 
-### Step 2: Plan Enhancement Scenes
+### Step 3: Plan Enhancement Scenes
 
 Based on script enhancement cues, plan overlay scenes:
 - Text cards for key terms or statistics
 - Lower thirds for speaker identification
 - B-roll suggestions for topic illustrations
 
-### Step 3: Build Scene Plan
+### Step 4: Plan Reframing & Cuts
+
+If the target platform requires a different aspect ratio (e.g. Instagram Reels = 9:16):
+- Note `auto_reframe` should be applied in the compose stage
+- Record the target aspect ratio in the scene plan
+- If face tracking data shows significant speaker movement, note that dynamic crop is needed
+
+If silence detection found segments to cut:
+- Record the recommended cut mode (`remove` or `speed_up`) in the scene plan
+- Note padding preferences (default 0.08s to avoid clipping words)
+
+### Step 5: Build Scene Plan
 
 Create a scene per section with:
 - Type: `talking_head` (primary)
 - Timing from script sections
 - Required assets: subtitle file, any overlay images
 
-### Step 4: Self-Evaluate
+### Step 6: Self-Evaluate
 
 | Criterion | Question |
 |-----------|----------|
@@ -40,6 +82,6 @@ Create a scene per section with:
 | **Enhancement** | Are overlay opportunities identified? |
 | **Feasibility** | Can all required assets be generated? |
 
-### Step 5: Submit
+### Step 7: Submit
 
 Validate the scene_plan against the schema and persist via checkpoint.
diff --git a/tools/analysis/face_tracker.py b/tools/analysis/face_tracker.py
new file mode 100644
index 0000000..f9ed63e
--- /dev/null
+++ b/tools/analysis/face_tracker.py
@@ -0,0 +1,314 @@
+"""Face tracking tool using MediaPipe Face Mesh.
+
+Tracks face bounding boxes, landmarks, and head pose across video frames.
+Outputs per-frame face data as JSON — used by auto_reframe, face_enhance,
+and other tools that need to know where the speaker's face is.
+
+Falls back to OpenCV Haar cascade if MediaPipe is not installed.
+"""
+
+from __future__ import annotations
+
+import json
+import time
+from pathlib import Path
+from typing import Any
+
+from tools.base_tool import (
+    BaseTool,
+    Determinism,
+    ExecutionMode,
+    ResourceProfile,
+    ToolResult,
+    ToolStability,
+    ToolStatus,
+    ToolTier,
+)
+
+
+class FaceTracker(BaseTool):
+    name = "face_tracker"
+    version = "0.1.0"
+    tier = ToolTier.CORE
+    capability = "analysis"
+    provider = "mediapipe"
+    stability = ToolStability.EXPERIMENTAL
+    execution_mode = ExecutionMode.SYNC
+    determinism = Determinism.DETERMINISTIC
+
+    dependencies = ["cmd:ffmpeg"]
+    install_instructions = (
+        "For best results install MediaPipe:\n"
+        "pip install mediapipe opencv-python\n\n"
+        "Falls back to OpenCV Haar cascade (ships with opencv-python)."
+    )
+    agent_skills = ["ffmpeg"]
+
+    capabilities = [
+        "face_detection",
+        "face_tracking",
+        "face_bounding_box",
+        "head_pose_estimation",
+    ]
+
+    input_schema = {
+        "type": "object",
+        "required": ["input_path"],
+        "properties": {
+            "input_path": {"type": "string"},
+            "output_path": {
+                "type": "string",
+                "description": "Path for face tracking JSON output",
+            },
+            "sample_fps": {
+                "type": "number",
+                "default": 5,
+                "description": "Frames per second to sample (lower = faster, less precise)",
+            },
+            "min_detection_confidence": {
+                "type": "number",
+                "default": 0.5,
+                "minimum": 0.0,
+                "maximum": 1.0,
+            },
+        },
+    }
+
+    output_schema = {
+        "type": "object",
+        "properties": {
+            "frame_count": {"type": "integer"},
+            "face_detected_count": {"type": "integer"},
+            "video_width": {"type": "integer"},
+            "video_height": {"type": "integer"},
+            "fps": {"type": "number"},
+            "duration_seconds": {"type": "number"},
+            "faces": {
+                "type": "array",
+                "items": {
+                    "type": "object",
+                    "properties": {
+                        "frame_index": {"type": "integer"},
+                        "timestamp_seconds": {"type": "number"},
+                        "bbox": {
+                            "type": "object",
+                            "properties": {
+                                "x": {"type": "number"},
+                                "y": {"type": "number"},
+                                "width": {"type": "number"},
+                                "height": {"type": "number"},
+                            },
+                        },
+                    },
+                },
+            },
+        },
+    }
+
+    resource_profile = ResourceProfile(cpu_cores=2, ram_mb=1024, vram_mb=0, disk_mb=100)
+    idempotency_key_fields = ["input_path", "sample_fps", "min_detection_confidence"]
+    side_effects = ["writes face tracking JSON to output_path"]
+    user_visible_verification = [
+        "Spot-check bounding boxes against video frames",
+    ]
+
+    fallback_tools = []
+
+    def _has_mediapipe(self) -> bool:
+        try:
+            import mediapipe  # noqa: F401
+            return True
+        except ImportError:
+            return False
+
+    def _has_opencv(self) -> bool:
+        try:
+            import cv2  # noqa: F401
+            return True
+        except ImportError:
+            return False
+
+    def get_status(self) -> ToolStatus:
+        if self._has_mediapipe() and self._has_opencv():
+            return ToolStatus.AVAILABLE
+        if self._has_opencv():
+            return ToolStatus.DEGRADED
+        return ToolStatus.UNAVAILABLE
+
+    def execute(self, inputs: dict[str, Any]) -> ToolResult:
+        input_path = Path(inputs["input_path"])
+        if not input_path.exists():
+            return ToolResult(success=False, error=f"Input not found: {input_path}")
+
+        if not self._has_opencv():
+            return ToolResult(
+                success=False,
+                error="opencv-python is required. Install: pip install opencv-python",
+            )
+
+        output_path = Path(
+            inputs.get("output_path", str(input_path.with_suffix(".faces.json")))
+        )
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+
+        sample_fps = inputs.get("sample_fps", 5)
+        confidence = inputs.get("min_detection_confidence", 0.5)
+
+        start = time.time()
+
+        if self._has_mediapipe():
+            result_data = self._track_mediapipe(input_path, sample_fps, confidence)
+        else:
+            result_data = self._track_opencv(input_path, sample_fps)
+
+        elapsed = time.time() - start
+
+        output_path.write_text(json.dumps(result_data, indent=2), encoding="utf-8")
+
+        return ToolResult(
+            success=True,
+            data={
+                "output": str(output_path),
+                "video_width": result_data["video_width"],
+                "video_height": result_data["video_height"],
+                "fps": result_data["fps"],
+                "duration_seconds": result_data["duration_seconds"],
+                "frames_sampled": result_data["frame_count"],
+                "faces_detected": result_data["face_detected_count"],
+                "method": "mediapipe" if self._has_mediapipe() else "opencv_haar",
+            },
+            artifacts=[str(output_path)],
+            duration_seconds=round(elapsed, 2),
+        )
+
+    def _track_mediapipe(
+        self, input_path: Path, sample_fps: float, confidence: float
+    ) -> dict:
+        import cv2
+        import mediapipe as mp
+
+        mp_face = mp.solutions.face_detection
+        cap = cv2.VideoCapture(str(input_path))
+
+        video_fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
+        video_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        video_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        duration = total_frames / video_fps if video_fps > 0 else 0
+
+        # Calculate frame sampling interval
+        sample_interval = max(1, int(video_fps / sample_fps))
+
+        faces_data: list[dict] = []
+        frame_idx = 0
+        sampled = 0
+
+        with mp_face.FaceDetection(
+            model_selection=1,  # 1 = full range (up to 5m), 0 = short range (up to 2m)
+            min_detection_confidence=confidence,
+        ) as detector:
+            while cap.isOpened():
+                ret, frame = cap.read()
+                if not ret:
+                    break
+
+                if frame_idx % sample_interval == 0:
+                    sampled += 1
+                    rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                    results = detector.process(rgb)
+
+                    if results.detections:
+                        # Use the highest-confidence detection
+                        det = max(
+                            results.detections,
+                            key=lambda d: d.score[0],
+                        )
+                        bbox = det.location_data.relative_bounding_box
+                        faces_data.append({
+                            "frame_index": frame_idx,
+                            "timestamp_seconds": round(frame_idx / video_fps, 3),
+                            "confidence": round(det.score[0], 3),
+                            "bbox": {
+                                "x": round(bbox.xmin, 4),
+                                "y": round(bbox.ymin, 4),
+                                "width": round(bbox.width, 4),
+                                "height": round(bbox.height, 4),
+                            },
+                        })
+
+                frame_idx += 1
+
+        cap.release()
+
+        return {
+            "video_width": video_w,
+            "video_height": video_h,
+            "fps": round(video_fps, 2),
+            "duration_seconds": round(duration, 3),
+            "frame_count": sampled,
+            "face_detected_count": len(faces_data),
+            "faces": faces_data,
+        }
+
+    def _track_opencv(self, input_path: Path, sample_fps: float) -> dict:
+        """Fallback: OpenCV Haar cascade face detection."""
+        import cv2
+
+        cascade_path = cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
+        cascade = cv2.CascadeClassifier(cascade_path)
+
+        cap = cv2.VideoCapture(str(input_path))
+        video_fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
+        video_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        video_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        duration = total_frames / video_fps if video_fps > 0 else 0
+
+        sample_interval = max(1, int(video_fps / sample_fps))
+
+        faces_data: list[dict] = []
+        frame_idx = 0
+        sampled = 0
+
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            if frame_idx % sample_interval == 0:
+                sampled += 1
+                gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+                detected = cascade.detectMultiScale(
+                    gray, scaleFactor=1.1, minNeighbors=5, minSize=(60, 60)
+                )
+
+                if len(detected) > 0:
+                    # Pick largest face
+                    areas = [w * h for (_, _, w, h) in detected]
+                    best_idx = areas.index(max(areas))
+                    x, y, w, h = detected[best_idx]
+                    faces_data.append({
+                        "frame_index": frame_idx,
+                        "timestamp_seconds": round(frame_idx / video_fps, 3),
+                        "confidence": 0.0,  # Haar doesn't provide confidence
+                        "bbox": {
+                            "x": round(x / video_w, 4),
+                            "y": round(y / video_h, 4),
+                            "width": round(w / video_w, 4),
+                            "height": round(h / video_h, 4),
+                        },
+                    })
+
+            frame_idx += 1
+
+        cap.release()
+
+        return {
+            "video_width": video_w,
+            "video_height": video_h,
+            "fps": round(video_fps, 2),
+            "duration_seconds": round(duration, 3),
+            "frame_count": sampled,
+            "face_detected_count": len(faces_data),
+            "faces": faces_data,
+        }
diff --git a/tools/analysis/visual_qa.py b/tools/analysis/visual_qa.py
new file mode 100644
index 0000000..7aa3015
--- /dev/null
+++ b/tools/analysis/visual_qa.py
@@ -0,0 +1,343 @@
+"""Visual QA tool for automated video quality checks.
+
+Extracts frames at specified timestamps and runs basic quality checks:
+- File existence, resolution, duration, codec validation
+- Frame extraction for visual inspection by the agent
+- Caption occlusion check (compares brightness in face vs caption zones)
+- Transition verification (frame similarity at transition points)
+
+Returns frame paths so the agent can visually inspect them.
+"""
+
+from __future__ import annotations
+
+import time
+from pathlib import Path
+from typing import Any
+
+from tools.base_tool import (
+    BaseTool,
+    Determinism,
+    ExecutionMode,
+    ResourceProfile,
+    ToolResult,
+    ToolStability,
+    ToolTier,
+)
+
+
+class VisualQA(BaseTool):
+    name = "visual_qa"
+    version = "0.1.0"
+    tier = ToolTier.CORE
+    capability = "analysis"
+    provider = "ffmpeg"
+    stability = ToolStability.EXPERIMENTAL
+    execution_mode = ExecutionMode.SYNC
+    determinism = Determinism.DETERMINISTIC
+
+    dependencies = ["cmd:ffmpeg", "cmd:ffprobe"]
+    install_instructions = "Install FFmpeg: https://ffmpeg.org/download.html"
+    agent_skills = ["ffmpeg"]
+
+    capabilities = [
+        "extract_review_frames",
+        "probe_video",
+        "check_audio_levels",
+    ]
+
+    input_schema = {
+        "type": "object",
+        "required": ["operation", "input_path"],
+        "properties": {
+            "operation": {
+                "type": "string",
+                "enum": ["review", "probe", "audio_levels"],
+                "description": (
+                    "review: extract frames at timestamps for visual inspection. "
+                    "probe: get video metadata (duration, resolution, codecs). "
+                    "audio_levels: check audio volume at specified timestamps."
+                ),
+            },
+            "input_path": {
+                "type": "string",
+                "description": "Path to the video file to inspect.",
+            },
+            "timestamps": {
+                "type": "array",
+                "items": {"type": "number"},
+                "description": (
+                    "Timestamps (in seconds) at which to extract frames or "
+                    "check audio levels."
+                ),
+            },
+            "output_dir": {
+                "type": "string",
+                "description": (
+                    "Directory to save extracted frames. Defaults to a "
+                    "'review_frames' subdirectory next to the input file."
+                ),
+            },
+            "checks": {
+                "type": "array",
+                "items": {
+                    "type": "string",
+                    "enum": [
+                        "resolution",
+                        "duration",
+                        "audio_present",
+                        "pixel_format",
+                        "file_size",
+                    ],
+                },
+                "description": "Specific checks to run (probe operation).",
+            },
+            "expected": {
+                "type": "object",
+                "description": (
+                    "Expected values for validation. "
+                    "Keys: width, height, min_duration, max_duration, "
+                    "pixel_format, has_audio."
+                ),
+                "properties": {
+                    "width": {"type": "integer"},
+                    "height": {"type": "integer"},
+                    "min_duration": {"type": "number"},
+                    "max_duration": {"type": "number"},
+                    "pixel_format": {"type": "string"},
+                    "has_audio": {"type": "boolean"},
+                },
+            },
+        },
+    }
+
+    resource_profile = ResourceProfile(cpu_cores=1, ram_mb=512, vram_mb=0, disk_mb=200)
+    idempotency_key_fields = ["operation", "input_path", "timestamps"]
+    side_effects = ["writes frame images to output_dir"]
+    user_visible_verification = [
+        "Visually inspect extracted frames for quality issues",
+    ]
+
+    def execute(self, inputs: dict[str, Any]) -> ToolResult:
+        operation = inputs["operation"]
+        input_path = inputs["input_path"]
+
+        if not Path(input_path).exists():
+            return ToolResult(success=False, error=f"Input not found: {input_path}")
+
+        start = time.time()
+
+        try:
+            if operation == "review":
+                result = self._review(inputs)
+            elif operation == "probe":
+                result = self._probe(inputs)
+            elif operation == "audio_levels":
+                result = self._audio_levels(inputs)
+            else:
+                return ToolResult(success=False, error=f"Unknown operation: {operation}")
+        except Exception as e:
+            return ToolResult(success=False, error=str(e))
+
+        result.duration_seconds = round(time.time() - start, 2)
+        return result
+
+    def _review(self, inputs: dict[str, Any]) -> ToolResult:
+        """Extract frames at specified timestamps for visual review."""
+        input_path = inputs["input_path"]
+        timestamps = inputs.get("timestamps", [])
+
+        if not timestamps:
+            # Auto-generate timestamps: start, 25%, 50%, 75%, end-1s
+            dur = self._get_duration(input_path)
+            timestamps = [
+                1.0,
+                dur * 0.25,
+                dur * 0.50,
+                dur * 0.75,
+                max(dur - 1.0, 0),
+            ]
+
+        output_dir = inputs.get("output_dir")
+        if not output_dir:
+            output_dir = str(Path(input_path).parent / "review_frames")
+        Path(output_dir).mkdir(parents=True, exist_ok=True)
+
+        frames = []
+        for ts in timestamps:
+            ts_label = f"{ts:.1f}".replace(".", "_")
+            frame_path = str(Path(output_dir) / f"frame_{ts_label}s.jpg")
+            cmd = [
+                "ffmpeg", "-y",
+                "-ss", str(ts),
+                "-i", input_path,
+                "-frames:v", "1",
+                "-q:v", "2",
+                frame_path,
+            ]
+            try:
+                self.run_command(cmd)
+                if Path(frame_path).exists():
+                    frames.append({
+                        "timestamp": ts,
+                        "path": frame_path,
+                    })
+            except Exception:
+                frames.append({
+                    "timestamp": ts,
+                    "path": None,
+                    "error": f"Failed to extract frame at {ts}s",
+                })
+
+        return ToolResult(
+            success=True,
+            data={
+                "operation": "review",
+                "input": input_path,
+                "frame_count": len([f for f in frames if f.get("path")]),
+                "frames": frames,
+            },
+            artifacts=[f["path"] for f in frames if f.get("path")],
+        )
+
+    def _probe(self, inputs: dict[str, Any]) -> ToolResult:
+        """Probe video metadata and optionally validate against expectations."""
+        input_path = inputs["input_path"]
+        expected = inputs.get("expected", {})
+
+        # Get comprehensive probe data
+        cmd = [
+            "ffprobe", "-v", "error",
+            "-show_entries",
+            "format=duration,size:stream=width,height,codec_name,pix_fmt,"
+            "r_frame_rate,sample_rate,channels,codec_type",
+            "-of", "json",
+            input_path,
+        ]
+        import json
+        probe_out = self.run_command(cmd, capture=True)
+        probe_data = json.loads(probe_out)
+
+        # Extract key info
+        video_stream = None
+        audio_stream = None
+        for s in probe_data.get("streams", []):
+            if s.get("codec_type") == "video" and not video_stream:
+                video_stream = s
+            elif s.get("codec_type") == "audio" and not audio_stream:
+                audio_stream = s
+
+        info = {
+            "duration": float(probe_data.get("format", {}).get("duration", 0)),
+            "file_size_mb": round(
+                int(probe_data.get("format", {}).get("size", 0)) / 1048576, 1
+            ),
+            "has_audio": audio_stream is not None,
+        }
+        if video_stream:
+            info.update({
+                "width": video_stream.get("width"),
+                "height": video_stream.get("height"),
+                "pixel_format": video_stream.get("pix_fmt"),
+                "video_codec": video_stream.get("codec_name"),
+                "frame_rate": video_stream.get("r_frame_rate"),
+            })
+        if audio_stream:
+            info.update({
+                "audio_codec": audio_stream.get("codec_name"),
+                "sample_rate": audio_stream.get("sample_rate"),
+                "channels": audio_stream.get("channels"),
+            })
+
+        # Validate against expectations
+        issues = []
+        if "width" in expected and info.get("width") != expected["width"]:
+            issues.append(f"Width: expected {expected['width']}, got {info.get('width')}")
+        if "height" in expected and info.get("height") != expected["height"]:
+            issues.append(f"Height: expected {expected['height']}, got {info.get('height')}")
+        if "min_duration" in expected and info["duration"] < expected["min_duration"]:
+            issues.append(
+                f"Duration too short: {info['duration']:.1f}s < {expected['min_duration']}s"
+            )
+        if "max_duration" in expected and info["duration"] > expected["max_duration"]:
+            issues.append(
+                f"Duration too long: {info['duration']:.1f}s > {expected['max_duration']}s"
+            )
+        if "pixel_format" in expected and info.get("pixel_format") != expected["pixel_format"]:
+            issues.append(
+                f"Pixel format: expected {expected['pixel_format']}, got {info.get('pixel_format')}"
+            )
+        if "has_audio" in expected and info["has_audio"] != expected["has_audio"]:
+            issues.append(
+                f"Audio: expected {'present' if expected['has_audio'] else 'absent'}, "
+                f"got {'present' if info['has_audio'] else 'absent'}"
+            )
+
+        info["validation_issues"] = issues
+        info["validation_passed"] = len(issues) == 0
+
+        return ToolResult(
+            success=True,
+            data={
+                "operation": "probe",
+                "input": input_path,
+                **info,
+            },
+        )
+
+    def _audio_levels(self, inputs: dict[str, Any]) -> ToolResult:
+        """Check audio levels at specified timestamps."""
+        input_path = inputs["input_path"]
+        timestamps = inputs.get("timestamps", [])
+
+        if not timestamps:
+            dur = self._get_duration(input_path)
+            timestamps = [1.0, dur * 0.5, max(dur - 2.0, 0)]
+
+        levels = []
+        for ts in timestamps:
+            cmd = [
+                "ffmpeg", "-y",
+                "-ss", str(ts),
+                "-t", "3",
+                "-i", input_path,
+                "-vn", "-af", "volumedetect",
+                "-f", "null", "/dev/null",
+            ]
+            try:
+                output = self.run_command(cmd, capture=True, stderr=True)
+                mean_vol = None
+                max_vol = None
+                for line in output.split("\n"):
+                    if "mean_volume" in line:
+                        mean_vol = float(line.split("mean_volume:")[1].strip().split()[0])
+                    elif "max_volume" in line:
+                        max_vol = float(line.split("max_volume:")[1].strip().split()[0])
+                levels.append({
+                    "timestamp": ts,
+                    "mean_volume_db": mean_vol,
+                    "max_volume_db": max_vol,
+                })
+            except Exception as e:
+                levels.append({
+                    "timestamp": ts,
+                    "error": str(e),
+                })
+
+        return ToolResult(
+            success=True,
+            data={
+                "operation": "audio_levels",
+                "input": input_path,
+                "levels": levels,
+            },
+        )
+
+    def _get_duration(self, path: str) -> float:
+        cmd = [
+            "ffprobe", "-v", "error",
+            "-show_entries", "format=duration",
+            "-of", "csv=p=0",
+            path,
+        ]
+        return float(self.run_command(cmd, capture=True).strip().split("\n")[0])
diff --git a/tools/audio/audio_mixer.py b/tools/audio/audio_mixer.py
index ab2b967..39be5a2 100644
--- a/tools/audio/audio_mixer.py
+++ b/tools/audio/audio_mixer.py
@@ -40,7 +40,7 @@ class AudioMixer(BaseTool):
     )
     agent_skills = ["ffmpeg", "video_toolkit"]
 
-    capabilities = ["mix", "duck", "fade", "normalize", "extract_audio"]
+    capabilities = ["mix", "duck", "fade", "normalize", "extract_audio", "segmented_music"]
 
     input_schema = {
         "type": "object",
@@ -48,13 +48,16 @@ class AudioMixer(BaseTool):
         "properties": {
             "operation": {
                 "type": "string",
-                "enum": ["mix", "duck", "extract", "full_mix"],
+                "enum": ["mix", "duck", "extract", "full_mix", "segmented_music"],
                 "description": (
                     "mix: layer multiple tracks with volume/delay/fades. "
                     "duck: lower music volume when speech is present. "
                     "extract: extract audio from video file. "
                     "full_mix: combine narration tracks + music with ducking + normalize "
-                    "in a single call (preferred for compose-director)."
+                    "in a single call (preferred for compose-director). "
+                    "segmented_music: mix music into a video only during specified "
+                    "time segments (e.g. music during talking head, silence during "
+                    "showcase clips)."
                 ),
             },
             "tracks": {
@@ -128,6 +131,45 @@ class AudioMixer(BaseTool):
                 },
             },
             "normalize": {"type": "boolean", "default": True},
+            "video_path": {
+                "type": "string",
+                "description": (
+                    "Path to the assembled video (segmented_music operation). "
+                    "Music is mixed into this video's audio at specified segments."
+                ),
+            },
+            "music_path": {
+                "type": "string",
+                "description": "Path to background music file (segmented_music operation).",
+            },
+            "music_volume": {
+                "type": "number",
+                "minimum": 0,
+                "maximum": 1.0,
+                "default": 0.20,
+                "description": "Volume level for music during active segments.",
+            },
+            "segments": {
+                "type": "array",
+                "description": (
+                    "Time segments where music should play (segmented_music operation). "
+                    "Each segment: {start: seconds, end: seconds}. Music fades in/out "
+                    "at segment boundaries. Outside these segments, music is silent."
+                ),
+                "items": {
+                    "type": "object",
+                    "required": ["start", "end"],
+                    "properties": {
+                        "start": {"type": "number", "minimum": 0},
+                        "end": {"type": "number", "minimum": 0},
+                    },
+                },
+            },
+            "fade_duration": {
+                "type": "number",
+                "default": 0.5,
+                "description": "Duration of fade in/out at segment boundaries (seconds).",
+            },
         },
     }
 
@@ -151,6 +193,8 @@ class AudioMixer(BaseTool):
                 result = self._extract(inputs)
             elif operation == "full_mix":
                 result = self._full_mix(inputs)
+            elif operation == "segmented_music":
+                result = self._segmented_music(inputs)
             else:
                 return ToolResult(success=False, error=f"Unknown operation: {operation}")
         except Exception as e:
@@ -558,3 +602,105 @@ class AudioMixer(BaseTool):
             },
             artifacts=[str(output_path)],
         )
+
+    def _segmented_music(self, inputs: dict[str, Any]) -> ToolResult:
+        """Mix background music into a video only during specified time segments.
+
+        Uses FFmpeg volume expressions with smooth fades at segment boundaries.
+        Music is silent outside the specified segments.
+
+        Input format:
+            {
+                "operation": "segmented_music",
+                "video_path": "assembled.mp4",
+                "music_path": "bg_music.mp3",
+                "music_volume": 0.20,
+                "segments": [
+                    {"start": 0, "end": 17.0},
+                    {"start": 167.0, "end": 175.0}
+                ],
+                "fade_duration": 0.5,
+                "output_path": "final_with_music.mp4"
+            }
+        """
+        video_path = inputs.get("video_path")
+        music_path = inputs.get("music_path")
+        output_path = Path(inputs.get("output_path", "segmented_music_output.mp4"))
+        segments = inputs.get("segments", [])
+        music_volume = inputs.get("music_volume", 0.20)
+        fade_dur = inputs.get("fade_duration", 0.5)
+
+        if not video_path or not Path(video_path).exists():
+            return ToolResult(success=False, error=f"Video not found: {video_path}")
+        if not music_path or not Path(music_path).exists():
+            return ToolResult(success=False, error=f"Music not found: {music_path}")
+        if not segments:
+            return ToolResult(success=False, error="No segments specified")
+
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+
+        # Get video duration
+        dur_cmd = [
+            "ffprobe", "-v", "error",
+            "-show_entries", "format=duration",
+            "-of", "csv=p=0",
+            video_path,
+        ]
+        total_dur = float(self.run_command(dur_cmd, capture=True).strip().split("\n")[0])
+
+        # Build volume expression for each segment with smooth fades
+        parts = []
+        for seg in sorted(segments, key=lambda s: s["start"]):
+            s = seg["start"]
+            e = seg["end"]
+            fade_in_end = s + fade_dur
+            fade_out_start = e - fade_dur
+            parts.append(
+                f"if(lt(t,{s}),0,"
+                f"if(lt(t,{fade_in_end}),{music_volume}*(t-{s})/{fade_dur},"
+                f"if(lt(t,{fade_out_start}),{music_volume},"
+                f"if(lt(t,{e}),{music_volume}*({e}-t)/{fade_dur},"
+                f"0))))"
+            )
+
+        vol_expr = "+".join(f"({p})" for p in parts) if len(parts) > 1 else parts[0]
+
+        filter_complex = (
+            f"[1:a]atrim=0:{total_dur},asetpts=PTS-STARTPTS,"
+            f"volume='{vol_expr}':eval=frame[music_shaped];"
+            f"[0:a]aformat=sample_fmts=fltp:sample_rates=44100:channel_layouts=stereo[speech];"
+            f"[music_shaped]aformat=sample_fmts=fltp:sample_rates=44100:channel_layouts=stereo[music_fmt];"
+            f"[speech][music_fmt]amix=inputs=2:duration=first:dropout_transition=2[aout]"
+        )
+
+        cmd = [
+            "ffmpeg", "-y",
+            "-i", video_path,
+            "-stream_loop", "-1",
+            "-i", music_path,
+            "-filter_complex", filter_complex,
+            "-map", "0:v",
+            "-map", "[aout]",
+            "-c:v", "copy",
+            "-c:a", "aac", "-b:a", "192k",
+            str(output_path),
+        ]
+
+        self.run_command(cmd)
+
+        if not output_path.exists():
+            return ToolResult(success=False, error="No output produced")
+
+        return ToolResult(
+            success=True,
+            data={
+                "operation": "segmented_music",
+                "video": video_path,
+                "music": music_path,
+                "segments": segments,
+                "music_volume": music_volume,
+                "fade_duration": fade_dur,
+                "output": str(output_path),
+            },
+            artifacts=[str(output_path)],
+        )
diff --git a/tools/enhancement/eye_enhance.py b/tools/enhancement/eye_enhance.py
new file mode 100644
index 0000000..5ffe771
--- /dev/null
+++ b/tools/enhancement/eye_enhance.py
@@ -0,0 +1,578 @@
+"""Eye enhancement tool using MediaPipe Face Mesh + OpenCV.
+
+Targets the eye region for talking-head footage:
+- Under-eye dark circle brightening
+- Eye/iris sharpening and brightening
+- Subtle under-eye smoothing
+
+Uses MediaPipe Face Mesh (468 landmarks) to precisely locate eye regions,
+then applies targeted OpenCV adjustments. Processes video frame-by-frame.
+
+Falls back to FFmpeg region-based filters if MediaPipe is not installed.
+"""
+
+from __future__ import annotations
+
+import json
+import time
+from pathlib import Path
+from typing import Any
+
+from tools.base_tool import (
+    BaseTool,
+    Determinism,
+    ExecutionMode,
+    ResourceProfile,
+    ToolResult,
+    ToolStability,
+    ToolStatus,
+    ToolTier,
+)
+
+# MediaPipe Face Mesh landmark indices for eye regions.
+# These form polygons around each eye area.
+# Lower eyelid landmarks (used to define the under-eye region):
+LEFT_LOWER_EYELID = [33, 7, 163, 144, 145, 153, 154, 155, 133]
+RIGHT_LOWER_EYELID = [263, 249, 390, 373, 374, 380, 381, 382, 362]
+
+# Full eye contour (used for iris/eye brightening):
+LEFT_EYE = [33, 7, 163, 144, 145, 153, 154, 155, 133, 173, 157, 158, 159, 160, 161, 246]
+RIGHT_EYE = [263, 249, 390, 373, 374, 380, 381, 382, 362, 398, 384, 385, 386, 387, 388, 466]
+
+# Iris landmarks (available when refine_landmarks=True, indices 468-477):
+LEFT_IRIS = [468, 469, 470, 471, 472]
+RIGHT_IRIS = [473, 474, 475, 476, 477]
+
+
+class EyeEnhance(BaseTool):
+    name = "eye_enhance"
+    version = "0.1.0"
+    tier = ToolTier.ENHANCE
+    capability = "enhancement"
+    provider = "mediapipe"
+    stability = ToolStability.EXPERIMENTAL
+    execution_mode = ExecutionMode.SYNC
+    determinism = Determinism.DETERMINISTIC
+
+    dependencies = ["cmd:ffmpeg"]
+    install_instructions = (
+        "For best results install MediaPipe and OpenCV:\n"
+        "pip install mediapipe opencv-python numpy\n\n"
+        "Without MediaPipe, falls back to FFmpeg eye-region filter (less precise)."
+    )
+    agent_skills = ["ffmpeg"]
+
+    capabilities = [
+        "under_eye_brightening",
+        "dark_circle_removal",
+        "eye_sharpening",
+        "eye_brightening",
+    ]
+
+    input_schema = {
+        "type": "object",
+        "required": ["input_path"],
+        "properties": {
+            "input_path": {"type": "string"},
+            "output_path": {"type": "string"},
+            "operations": {
+                "type": "array",
+                "items": {
+                    "type": "string",
+                    "enum": ["dark_circles", "brighten_eyes", "sharpen_eyes"],
+                },
+                "default": ["dark_circles", "brighten_eyes"],
+                "description": "Which enhancements to apply",
+            },
+            "dark_circle_intensity": {
+                "type": "number",
+                "default": 0.4,
+                "minimum": 0.0,
+                "maximum": 1.0,
+                "description": "Strength of dark circle removal (0=none, 1=max)",
+            },
+            "eye_brighten_intensity": {
+                "type": "number",
+                "default": 0.3,
+                "minimum": 0.0,
+                "maximum": 1.0,
+                "description": "Strength of eye brightening (0=none, 1=max)",
+            },
+            "sharpen_intensity": {
+                "type": "number",
+                "default": 0.3,
+                "minimum": 0.0,
+                "maximum": 1.0,
+                "description": "Strength of eye sharpening (0=none, 1=max)",
+            },
+            "codec": {"type": "string", "default": "libx264"},
+            "crf": {"type": "integer", "default": 18},
+        },
+    }
+
+    resource_profile = ResourceProfile(
+        cpu_cores=4, ram_mb=2048, vram_mb=0, disk_mb=4000, network_required=False
+    )
+    idempotency_key_fields = [
+        "input_path", "operations", "dark_circle_intensity",
+        "eye_brighten_intensity", "sharpen_intensity",
+    ]
+    side_effects = ["writes enhanced video to output_path"]
+    user_visible_verification = [
+        "Compare eyes in before/after — enhancement should be subtle and natural",
+        "Check for artifacts around eye region (halos, color shifts)",
+        "Verify enhancement doesn't make eyes look unnatural",
+    ]
+
+    def _has_mediapipe(self) -> bool:
+        try:
+            import mediapipe  # noqa: F401
+            return True
+        except ImportError:
+            return False
+
+    def _has_opencv(self) -> bool:
+        try:
+            import cv2  # noqa: F401
+            import numpy  # noqa: F401
+            return True
+        except ImportError:
+            return False
+
+    def get_status(self) -> ToolStatus:
+        if self._has_mediapipe() and self._has_opencv():
+            return ToolStatus.AVAILABLE
+        if self._has_opencv():
+            return ToolStatus.DEGRADED
+        return ToolStatus.UNAVAILABLE
+
+    def execute(self, inputs: dict[str, Any]) -> ToolResult:
+        input_path = Path(inputs["input_path"])
+        if not input_path.exists():
+            return ToolResult(success=False, error=f"Input not found: {input_path}")
+
+        operations = inputs.get("operations", ["dark_circles", "brighten_eyes"])
+        output_path = Path(
+            inputs.get("output_path", str(input_path.with_stem(f"{input_path.stem}_eye_enhanced")))
+        )
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+
+        start = time.time()
+
+        if self._has_mediapipe() and self._has_opencv():
+            result = self._enhance_mediapipe(input_path, output_path, inputs)
+        elif self._has_opencv():
+            result = self._enhance_opencv_only(input_path, output_path, inputs)
+        else:
+            result = self._enhance_ffmpeg_fallback(input_path, output_path, inputs)
+
+        if not result.success:
+            return result
+
+        elapsed = time.time() - start
+        result.duration_seconds = round(elapsed, 2)
+        return result
+
+    def _enhance_mediapipe(
+        self, input_path: Path, output_path: Path, inputs: dict[str, Any]
+    ) -> ToolResult:
+        """Full MediaPipe Face Mesh + OpenCV pipeline for precise eye enhancement."""
+        import cv2
+        import numpy as np
+        import mediapipe as mp
+
+        operations = inputs.get("operations", ["dark_circles", "brighten_eyes"])
+        dark_intensity = inputs.get("dark_circle_intensity", 0.4)
+        brighten_intensity = inputs.get("eye_brighten_intensity", 0.3)
+        sharpen_intensity = inputs.get("sharpen_intensity", 0.3)
+        codec_fourcc = inputs.get("codec", "libx264")
+        crf = inputs.get("crf", 18)
+
+        mp_face_mesh = mp.solutions.face_mesh
+
+        cap = cv2.VideoCapture(str(input_path))
+        fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+        # Write to temp file, then mux audio via FFmpeg
+        temp_video = output_path.parent / f".{output_path.stem}_temp.mp4"
+
+        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+        writer = cv2.VideoWriter(str(temp_video), fourcc, fps, (width, height))
+
+        frames_processed = 0
+        frames_enhanced = 0
+
+        with mp_face_mesh.FaceMesh(
+            static_image_mode=False,
+            max_num_faces=1,
+            refine_landmarks=True,  # Enables iris landmarks (468-477)
+            min_detection_confidence=0.5,
+            min_tracking_confidence=0.5,
+        ) as face_mesh:
+            while cap.isOpened():
+                ret, frame = cap.read()
+                if not ret:
+                    break
+
+                frames_processed += 1
+                rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                results = face_mesh.process(rgb)
+
+                if results.multi_face_landmarks:
+                    landmarks = results.multi_face_landmarks[0]
+                    frame = self._apply_eye_enhancements(
+                        frame, landmarks, width, height,
+                        operations, dark_intensity, brighten_intensity, sharpen_intensity,
+                    )
+                    frames_enhanced += 1
+
+                writer.write(frame)
+
+        cap.release()
+        writer.release()
+
+        # Mux original audio back with FFmpeg
+        cmd = [
+            "ffmpeg", "-y",
+            "-i", str(temp_video),
+            "-i", str(input_path),
+            "-c:v", "libx264", "-crf", str(crf), "-preset", "fast",
+            "-c:a", "aac", "-b:a", "192k",
+            "-map", "0:v:0", "-map", "1:a:0?",
+            "-shortest",
+            str(output_path),
+        ]
+
+        try:
+            self.run_command(cmd, timeout=600)
+        except Exception as e:
+            return ToolResult(success=False, error=f"Audio mux failed: {e}")
+        finally:
+            if temp_video.exists():
+                temp_video.unlink()
+
+        return ToolResult(
+            success=True,
+            data={
+                "input": str(input_path),
+                "output": str(output_path),
+                "method": "mediapipe_face_mesh",
+                "frames_processed": frames_processed,
+                "frames_enhanced": frames_enhanced,
+                "operations": operations,
+            },
+            artifacts=[str(output_path)],
+        )
+
+    def _apply_eye_enhancements(
+        self,
+        frame,
+        landmarks,
+        width: int,
+        height: int,
+        operations: list[str],
+        dark_intensity: float,
+        brighten_intensity: float,
+        sharpen_intensity: float,
+    ):
+        """Apply eye enhancements to a single frame using detected landmarks."""
+        import cv2
+        import numpy as np
+
+        result = frame.copy()
+
+        # Convert landmarks to pixel coordinates
+        def lm_to_px(indices):
+            points = []
+            for idx in indices:
+                if idx < len(landmarks.landmark):
+                    lm = landmarks.landmark[idx]
+                    points.append((int(lm.x * width), int(lm.y * height)))
+            return np.array(points, dtype=np.int32)
+
+        for side in ["left", "right"]:
+            lower_lid = lm_to_px(LEFT_LOWER_EYELID if side == "left" else RIGHT_LOWER_EYELID)
+            eye_contour = lm_to_px(LEFT_EYE if side == "left" else RIGHT_EYE)
+
+            if len(lower_lid) < 3 or len(eye_contour) < 3:
+                continue
+
+            # Under-eye region: expand lower eyelid downward
+            if "dark_circles" in operations:
+                result = self._remove_dark_circles(
+                    result, lower_lid, width, height, dark_intensity
+                )
+
+            if "brighten_eyes" in operations:
+                iris = lm_to_px(LEFT_IRIS if side == "left" else RIGHT_IRIS)
+                result = self._brighten_eyes(
+                    result, eye_contour, iris, brighten_intensity
+                )
+
+            if "sharpen_eyes" in operations:
+                result = self._sharpen_eyes(
+                    result, eye_contour, sharpen_intensity
+                )
+
+        return result
+
+    def _remove_dark_circles(
+        self, frame, lower_lid_points, width: int, height: int, intensity: float
+    ):
+        """Brighten the under-eye area to reduce dark circles."""
+        import cv2
+        import numpy as np
+
+        # Create under-eye region by shifting lower eyelid points downward
+        under_eye = lower_lid_points.copy()
+        # Shift down by ~15% of face height (approximate eye-to-cheek distance)
+        shift = max(5, int(height * 0.025))
+        under_eye_shifted = under_eye.copy()
+        under_eye_shifted[:, 1] += shift
+
+        # Create polygon from lower lid + shifted points (forms a band)
+        polygon = np.vstack([lower_lid_points, under_eye_shifted[::-1]])
+
+        # Create soft mask
+        mask = np.zeros(frame.shape[:2], dtype=np.float32)
+        cv2.fillPoly(mask, [polygon], 1.0)
+        # Gaussian blur for soft edges
+        blur_size = max(15, int(width * 0.02)) | 1  # Ensure odd
+        mask = cv2.GaussianBlur(mask, (blur_size, blur_size), 0)
+
+        # Apply brightening in LAB color space (perceptually uniform)
+        lab = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB).astype(np.float32)
+        # Boost L channel (lightness) in the masked region
+        boost = 25 * intensity  # 0-25 range
+        lab[:, :, 0] += mask * boost
+        lab[:, :, 0] = np.clip(lab[:, :, 0], 0, 255)
+
+        # Also reduce saturation slightly (dark circles are often purplish)
+        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV).astype(np.float32)
+        hsv[:, :, 1] -= mask * (20 * intensity)
+        hsv[:, :, 1] = np.clip(hsv[:, :, 1], 0, 255)
+
+        # Blend: use LAB for brightness, HSV for desaturation
+        brightened = cv2.cvtColor(lab.astype(np.uint8), cv2.COLOR_LAB2BGR)
+        desaturated = cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2BGR)
+
+        # Combine: weighted blend of both adjustments
+        mask_3ch = np.stack([mask] * 3, axis=-1)
+        combined = frame.astype(np.float32)
+        combined = combined * (1 - mask_3ch * intensity) + \
+                   (brightened.astype(np.float32) * 0.6 + desaturated.astype(np.float32) * 0.4) * (mask_3ch * intensity)
+
+        return np.clip(combined, 0, 255).astype(np.uint8)
+
+    def _brighten_eyes(self, frame, eye_contour, iris_points, intensity: float):
+        """Subtly brighten the eye/sclera area."""
+        import cv2
+        import numpy as np
+
+        if len(eye_contour) < 3:
+            return frame
+
+        # Create mask from eye contour
+        mask = np.zeros(frame.shape[:2], dtype=np.float32)
+        cv2.fillPoly(mask, [eye_contour], 1.0)
+        blur_size = max(5, int(frame.shape[1] * 0.005)) | 1
+        mask = cv2.GaussianBlur(mask, (blur_size, blur_size), 0)
+
+        # Brighten in LAB space
+        lab = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB).astype(np.float32)
+        boost = 15 * intensity
+        lab[:, :, 0] += mask * boost
+        lab[:, :, 0] = np.clip(lab[:, :, 0], 0, 255)
+
+        brightened = cv2.cvtColor(lab.astype(np.uint8), cv2.COLOR_LAB2BGR)
+
+        mask_3ch = np.stack([mask] * 3, axis=-1)
+        result = frame.astype(np.float32) * (1 - mask_3ch * intensity) + \
+                 brightened.astype(np.float32) * (mask_3ch * intensity)
+
+        return np.clip(result, 0, 255).astype(np.uint8)
+
+    def _sharpen_eyes(self, frame, eye_contour, intensity: float):
+        """Sharpen the eye region for more detail."""
+        import cv2
+        import numpy as np
+
+        if len(eye_contour) < 3:
+            return frame
+
+        # Create mask
+        mask = np.zeros(frame.shape[:2], dtype=np.float32)
+        cv2.fillPoly(mask, [eye_contour], 1.0)
+        # Expand slightly for natural blend
+        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+        mask = cv2.dilate(mask, kernel, iterations=1)
+        blur_size = max(5, int(frame.shape[1] * 0.005)) | 1
+        mask = cv2.GaussianBlur(mask, (blur_size, blur_size), 0)
+
+        # Unsharp mask for sharpening
+        blur = cv2.GaussianBlur(frame, (0, 0), 3)
+        sharpened = cv2.addWeighted(frame, 1.0 + intensity, blur, -intensity, 0)
+
+        mask_3ch = np.stack([mask] * 3, axis=-1)
+        result = frame.astype(np.float32) * (1 - mask_3ch) + \
+                 sharpened.astype(np.float32) * mask_3ch
+
+        return np.clip(result, 0, 255).astype(np.uint8)
+
+    def _enhance_opencv_only(
+        self, input_path: Path, output_path: Path, inputs: dict[str, Any]
+    ) -> ToolResult:
+        """Fallback: OpenCV Haar cascade for face detection + generic eye region enhancement."""
+        import cv2
+        import numpy as np
+
+        operations = inputs.get("operations", ["dark_circles", "brighten_eyes"])
+        dark_intensity = inputs.get("dark_circle_intensity", 0.4)
+        crf = inputs.get("crf", 18)
+
+        face_cascade = cv2.CascadeClassifier(
+            cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
+        )
+        eye_cascade = cv2.CascadeClassifier(
+            cv2.data.haarcascades + "haarcascade_eye.xml"
+        )
+
+        cap = cv2.VideoCapture(str(input_path))
+        fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+        temp_video = output_path.parent / f".{output_path.stem}_temp.mp4"
+        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+        writer = cv2.VideoWriter(str(temp_video), fourcc, fps, (width, height))
+
+        frames_processed = 0
+        frames_enhanced = 0
+
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            frames_processed += 1
+            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            faces = face_cascade.detectMultiScale(gray, 1.1, 5, minSize=(60, 60))
+
+            if len(faces) > 0:
+                # Use largest face
+                areas = [w * h for (_, _, w, h) in faces]
+                fi = areas.index(max(areas))
+                fx, fy, fw, fh = faces[fi]
+
+                # Detect eyes within face region
+                face_roi_gray = gray[fy:fy + fh, fx:fx + fw]
+                eyes = eye_cascade.detectMultiScale(face_roi_gray, 1.1, 5, minSize=(20, 20))
+
+                for (ex, ey, ew, eh) in eyes:
+                    # Under-eye region: below the detected eye box
+                    under_y = fy + ey + eh
+                    under_h = max(5, int(eh * 0.4))
+                    under_x = fx + ex
+                    under_w = ew
+
+                    if under_y + under_h <= height and under_x + under_w <= width:
+                        if "dark_circles" in operations:
+                            # Create soft mask for under-eye
+                            mask = np.zeros((height, width), dtype=np.float32)
+                            cv2.ellipse(
+                                mask,
+                                (under_x + under_w // 2, under_y + under_h // 2),
+                                (under_w // 2, under_h // 2),
+                                0, 0, 360, 1.0, -1,
+                            )
+                            mask = cv2.GaussianBlur(mask, (15, 15), 0)
+
+                            lab = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB).astype(np.float32)
+                            lab[:, :, 0] += mask * (25 * dark_intensity)
+                            lab[:, :, 0] = np.clip(lab[:, :, 0], 0, 255)
+                            frame = cv2.cvtColor(lab.astype(np.uint8), cv2.COLOR_LAB2BGR)
+
+                frames_enhanced += 1
+
+            writer.write(frame)
+
+        cap.release()
+        writer.release()
+
+        # Mux audio
+        cmd = [
+            "ffmpeg", "-y",
+            "-i", str(temp_video),
+            "-i", str(input_path),
+            "-c:v", "libx264", "-crf", str(crf), "-preset", "fast",
+            "-c:a", "aac", "-b:a", "192k",
+            "-map", "0:v:0", "-map", "1:a:0?",
+            "-shortest",
+            str(output_path),
+        ]
+
+        try:
+            self.run_command(cmd, timeout=600)
+        except Exception as e:
+            return ToolResult(success=False, error=f"Audio mux failed: {e}")
+        finally:
+            if temp_video.exists():
+                temp_video.unlink()
+
+        return ToolResult(
+            success=True,
+            data={
+                "input": str(input_path),
+                "output": str(output_path),
+                "method": "opencv_haar_cascade",
+                "frames_processed": frames_processed,
+                "frames_enhanced": frames_enhanced,
+                "operations": operations,
+            },
+            artifacts=[str(output_path)],
+        )
+
+    def _enhance_ffmpeg_fallback(
+        self, input_path: Path, output_path: Path, inputs: dict[str, Any]
+    ) -> ToolResult:
+        """Last resort: FFmpeg-only. Applies general face-area enhancement (not eye-specific)."""
+        crf = inputs.get("crf", 18)
+        intensity = inputs.get("dark_circle_intensity", 0.4)
+
+        # General brightness + contrast lift for the whole frame
+        # Not eye-specific but better than nothing
+        brightness = 0.02 * intensity
+        contrast = 1.0 + (0.05 * intensity)
+
+        cmd = [
+            "ffmpeg", "-y",
+            "-i", str(input_path),
+            "-vf", f"eq=brightness={brightness}:contrast={contrast}",
+            "-c:v", "libx264", "-crf", str(crf), "-preset", "fast",
+            "-c:a", "copy",
+            str(output_path),
+        ]
+
+        try:
+            self.run_command(cmd, timeout=600)
+        except Exception as e:
+            return ToolResult(success=False, error=f"FFmpeg fallback failed: {e}")
+
+        return ToolResult(
+            success=True,
+            data={
+                "input": str(input_path),
+                "output": str(output_path),
+                "method": "ffmpeg_global_brightness",
+                "operations": ["global_brightness_contrast"],
+                "note": "Install mediapipe + opencv-python for precise eye-region enhancement",
+            },
+            artifacts=[str(output_path)],
+        )
+
+    def estimate_runtime(self, inputs: dict[str, Any]) -> float:
+        """Eye enhancement is roughly 0.5x-1x realtime depending on resolution."""
+        return 90.0
diff --git a/tools/subtitle/subtitle_gen.py b/tools/subtitle/subtitle_gen.py
index 6344352..ac8472b 100644
--- a/tools/subtitle/subtitle_gen.py
+++ b/tools/subtitle/subtitle_gen.py
@@ -60,6 +60,15 @@ class SubtitleGen(BaseTool):
                 "enum": ["none", "word_by_word", "karaoke"],
                 "default": "none",
             },
+            "corrections": {
+                "type": "object",
+                "description": (
+                    "Dictionary of word corrections for common ASR misrecognitions. "
+                    "Keys are the wrong word (case-insensitive), values are the "
+                    "correct replacement. Applied before generating subtitles. "
+                    "Example: {\"cloud\": \"Claude\", \"co-pilot\": \"Copilot\"}."
+                ),
+            },
         },
     }
 
@@ -77,9 +86,14 @@ class SubtitleGen(BaseTool):
         max_chars = inputs.get("max_chars_per_line", 42)
         highlight_style = inputs.get("highlight_style", "none")
         output_path = inputs.get("output_path")
+        corrections = inputs.get("corrections")
 
         start = time.time()
 
+        # Apply word corrections if provided
+        if corrections:
+            segments = self._apply_corrections(segments, corrections)
+
         # Build cues from word-level timestamps
         cues = self._build_cues(segments, max_words, max_chars)
 
@@ -114,6 +128,43 @@ class SubtitleGen(BaseTool):
             duration_seconds=round(elapsed, 2),
         )
 
+    @staticmethod
+    def _apply_corrections(
+        segments: list[dict], corrections: dict[str, str]
+    ) -> list[dict]:
+        """Apply word-level corrections to transcript segments.
+
+        Handles case-insensitive matching and preserves punctuation.
+        """
+        import copy
+
+        corr = {k.lower(): v for k, v in corrections.items()}
+        result = copy.deepcopy(segments)
+
+        for seg in result:
+            words = seg.get("words", [])
+            for w in words:
+                raw = w.get("word", "").strip()
+                # Strip punctuation for lookup, preserve it
+                stripped = raw.lower().rstrip(".,!?;:'\"")
+                if stripped in corr:
+                    trailing = raw[len(stripped):]
+                    w["word"] = corr[stripped] + trailing
+            # Also fix segment-level text
+            if "text" in seg and words:
+                seg["text"] = " ".join(w["word"] for w in words)
+            elif "text" in seg:
+                for wrong, right in corr.items():
+                    import re as _re
+                    seg["text"] = _re.sub(
+                        r"\b" + _re.escape(wrong) + r"\b",
+                        right,
+                        seg["text"],
+                        flags=_re.IGNORECASE,
+                    )
+
+        return result
+
     def _build_cues(
         self, segments: list[dict], max_words: int, max_chars: int
     ) -> list[dict]:
diff --git a/tools/video/auto_reframe.py b/tools/video/auto_reframe.py
new file mode 100644
index 0000000..d054046
--- /dev/null
+++ b/tools/video/auto_reframe.py
@@ -0,0 +1,537 @@
+"""Auto-reframe tool for aspect ratio conversion with face tracking.
+
+Converts video between aspect ratios (e.g. 16:9 → 9:16 for Instagram Reels)
+while keeping the speaker's face centered in frame. Uses face_tracker data
+for smooth, content-aware cropping.
+
+Primary use: converting talking-head footage shot in landscape to vertical
+format for social media (TikTok, Reels, Shorts).
+
+Approach: MediaPipe/OpenCV face detection → smoothed bounding box trajectory
+→ FFmpeg crop filter. No GPU required.
+"""
+
+from __future__ import annotations
+
+import json
+import time
+from pathlib import Path
+from typing import Any
+
+from tools.base_tool import (
+    BaseTool,
+    Determinism,
+    ExecutionMode,
+    ResourceProfile,
+    RetryPolicy,
+    ResumeSupport,
+    ToolResult,
+    ToolStability,
+    ToolTier,
+)
+
+
+# Common target aspect ratios
+ASPECT_PRESETS = {
+    "portrait": (9, 16),       # Instagram Reels, TikTok, YouTube Shorts
+    "square": (1, 1),          # Instagram Feed
+    "landscape": (16, 9),      # YouTube, LinkedIn
+    "cinematic": (21, 9),      # Ultra-wide
+    "vertical_4_5": (4, 5),    # Instagram portrait post
+}
+
+
+class AutoReframe(BaseTool):
+    name = "auto_reframe"
+    version = "0.1.0"
+    tier = ToolTier.CORE
+    capability = "video_post"
+    provider = "ffmpeg"
+    stability = ToolStability.EXPERIMENTAL
+    execution_mode = ExecutionMode.SYNC
+    determinism = Determinism.DETERMINISTIC
+
+    dependencies = ["cmd:ffmpeg"]
+    install_instructions = (
+        "FFmpeg is required. For face-tracked reframing, also install:\n"
+        "pip install mediapipe opencv-python\n\n"
+        "Without MediaPipe/OpenCV, falls back to center-crop."
+    )
+    agent_skills = ["ffmpeg"]
+
+    capabilities = [
+        "aspect_ratio_conversion",
+        "face_tracked_crop",
+        "smart_reframe",
+        "center_crop",
+    ]
+
+    input_schema = {
+        "type": "object",
+        "required": ["input_path"],
+        "properties": {
+            "input_path": {"type": "string"},
+            "output_path": {"type": "string"},
+            "target_aspect": {
+                "type": "string",
+                "enum": list(ASPECT_PRESETS.keys()),
+                "default": "portrait",
+                "description": "Target aspect ratio preset",
+            },
+            "target_width": {
+                "type": "integer",
+                "description": "Explicit target width (overrides preset)",
+            },
+            "target_height": {
+                "type": "integer",
+                "description": "Explicit target height (overrides preset)",
+            },
+            "face_tracking_json": {
+                "type": "string",
+                "description": "Path to pre-computed face_tracker JSON. If omitted, runs face detection internally.",
+            },
+            "smoothing_window": {
+                "type": "integer",
+                "default": 15,
+                "minimum": 1,
+                "description": "Number of frames for position smoothing (higher = smoother pan, lower = more responsive)",
+            },
+            "face_padding": {
+                "type": "number",
+                "default": 0.4,
+                "minimum": 0.0,
+                "maximum": 1.0,
+                "description": "Extra space around face as fraction of face size (0.4 = 40% padding)",
+            },
+            "sample_fps": {
+                "type": "number",
+                "default": 5,
+                "description": "Face detection sample rate (only used if no face_tracking_json)",
+            },
+            "codec": {"type": "string", "default": "libx264"},
+            "crf": {"type": "integer", "default": 18},
+        },
+    }
+
+    resource_profile = ResourceProfile(
+        cpu_cores=4, ram_mb=2048, vram_mb=0, disk_mb=4000, network_required=False
+    )
+    retry_policy = RetryPolicy(max_retries=1, retryable_errors=["FFmpeg error"])
+    resume_support = ResumeSupport.FROM_START
+    idempotency_key_fields = [
+        "input_path", "target_aspect", "target_width", "target_height",
+        "smoothing_window", "face_padding",
+    ]
+    side_effects = ["writes reframed video to output_path"]
+    user_visible_verification = [
+        "Play reframed output — verify face stays centered and framing is smooth",
+        "Check that no important content is cropped out",
+    ]
+
+    def execute(self, inputs: dict[str, Any]) -> ToolResult:
+        input_path = Path(inputs["input_path"])
+        if not input_path.exists():
+            return ToolResult(success=False, error=f"Input not found: {input_path}")
+
+        start = time.time()
+
+        # Get source video dimensions
+        src_w, src_h, src_fps = self._get_video_info(input_path)
+        if src_w == 0 or src_h == 0:
+            return ToolResult(success=False, error="Could not read video dimensions")
+
+        # Determine target crop dimensions (in source pixel space)
+        target_w, target_h = self._compute_crop_size(inputs, src_w, src_h)
+
+        # If source already matches target aspect, no crop needed
+        if target_w == src_w and target_h == src_h:
+            return ToolResult(
+                success=True,
+                data={"message": "Source already matches target aspect ratio", "output": str(input_path)},
+                artifacts=[str(input_path)],
+            )
+
+        # Get face tracking data
+        face_data = self._get_face_data(inputs, input_path, src_fps)
+
+        # Compute per-frame crop positions
+        if face_data and len(face_data) > 0:
+            crop_x, crop_y = self._compute_face_tracked_crop(
+                face_data, src_w, src_h, target_w, target_h,
+                src_fps,
+                inputs.get("smoothing_window", 15),
+                inputs.get("face_padding", 0.4),
+            )
+            method = "face_tracked"
+        else:
+            # Fallback: center crop
+            crop_x = (src_w - target_w) // 2
+            crop_y = (src_h - target_h) // 2
+            method = "center_crop"
+
+        # Determine output resolution
+        out_w, out_h = self._compute_output_resolution(inputs, target_w, target_h, src_w, src_h)
+
+        # Build output path
+        aspect_name = inputs.get("target_aspect", "portrait")
+        output_path = Path(
+            inputs.get("output_path", str(input_path.with_stem(f"{input_path.stem}_{aspect_name}")))
+        )
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+
+        # Render via FFmpeg
+        codec = inputs.get("codec", "libx264")
+        crf = inputs.get("crf", 18)
+
+        if method == "face_tracked" and isinstance(crop_x, list):
+            # Dynamic crop: write crop coordinates to a file and use sendcmd
+            result = self._render_dynamic_crop(
+                input_path, output_path, crop_x, crop_y,
+                target_w, target_h, out_w, out_h,
+                src_fps, codec, crf,
+            )
+        else:
+            # Static crop
+            result = self._render_static_crop(
+                input_path, output_path,
+                crop_x, crop_y, target_w, target_h,
+                out_w, out_h, codec, crf,
+            )
+
+        if not result.success:
+            return result
+
+        elapsed = time.time() - start
+
+        return ToolResult(
+            success=True,
+            data={
+                "input": str(input_path),
+                "output": str(output_path),
+                "source_resolution": f"{src_w}x{src_h}",
+                "crop_resolution": f"{target_w}x{target_h}",
+                "output_resolution": f"{out_w}x{out_h}",
+                "method": method,
+                "target_aspect": inputs.get("target_aspect", "portrait"),
+            },
+            artifacts=[str(output_path)],
+            duration_seconds=round(elapsed, 2),
+        )
+
+    def _get_video_info(self, path: Path) -> tuple[int, int, float]:
+        """Get video width, height, fps via ffprobe."""
+        cmd = [
+            "ffprobe", "-v", "quiet",
+            "-select_streams", "v:0",
+            "-show_entries", "stream=width,height,r_frame_rate",
+            "-of", "json", str(path),
+        ]
+        try:
+            result = self.run_command(cmd)
+            data = json.loads(result.stdout)
+            stream = data["streams"][0]
+            w = int(stream["width"])
+            h = int(stream["height"])
+            # Parse r_frame_rate (e.g. "30000/1001")
+            fps_parts = stream["r_frame_rate"].split("/")
+            fps = float(fps_parts[0]) / float(fps_parts[1]) if len(fps_parts) == 2 else float(fps_parts[0])
+            return w, h, fps
+        except Exception:
+            return 0, 0, 30.0
+
+    def _compute_crop_size(
+        self, inputs: dict[str, Any], src_w: int, src_h: int
+    ) -> tuple[int, int]:
+        """Compute crop dimensions in source pixel space that match the target aspect ratio."""
+        if "target_width" in inputs and "target_height" in inputs:
+            # Explicit dimensions — compute crop in source space matching this ratio
+            tw, th = inputs["target_width"], inputs["target_height"]
+        else:
+            aspect_name = inputs.get("target_aspect", "portrait")
+            tw, th = ASPECT_PRESETS.get(aspect_name, (9, 16))
+
+        target_ratio = tw / th
+        src_ratio = src_w / src_h
+
+        if target_ratio > src_ratio:
+            # Target is wider — crop height
+            crop_w = src_w
+            crop_h = int(src_w / target_ratio)
+        else:
+            # Target is taller/narrower — crop width
+            crop_h = src_h
+            crop_w = int(src_h * target_ratio)
+
+        # Ensure even dimensions (required by most codecs)
+        crop_w = crop_w - (crop_w % 2)
+        crop_h = crop_h - (crop_h % 2)
+
+        return crop_w, crop_h
+
+    def _compute_output_resolution(
+        self, inputs: dict[str, Any],
+        crop_w: int, crop_h: int,
+        src_w: int, src_h: int,
+    ) -> tuple[int, int]:
+        """Determine final output resolution. Scales to standard sizes."""
+        if "target_width" in inputs and "target_height" in inputs:
+            out_w = inputs["target_width"]
+            out_h = inputs["target_height"]
+        else:
+            aspect_name = inputs.get("target_aspect", "portrait")
+            if aspect_name == "portrait":
+                out_w, out_h = 1080, 1920
+            elif aspect_name == "square":
+                out_w, out_h = 1080, 1080
+            elif aspect_name == "landscape":
+                out_w, out_h = 1920, 1080
+            elif aspect_name == "cinematic":
+                out_w, out_h = 2560, 1080
+            elif aspect_name == "vertical_4_5":
+                out_w, out_h = 1080, 1350
+            else:
+                out_w, out_h = crop_w, crop_h
+
+        # Ensure even
+        out_w = out_w - (out_w % 2)
+        out_h = out_h - (out_h % 2)
+        return out_w, out_h
+
+    def _get_face_data(
+        self, inputs: dict[str, Any], input_path: Path, src_fps: float
+    ) -> list[dict]:
+        """Get face tracking data — from pre-computed JSON or by running detection."""
+        # Check for pre-computed tracking data
+        tracking_json = inputs.get("face_tracking_json")
+        if tracking_json:
+            p = Path(tracking_json)
+            if p.exists():
+                data = json.loads(p.read_text(encoding="utf-8"))
+                return data.get("faces", [])
+
+        # Try to run face_tracker internally
+        try:
+            from tools.analysis.face_tracker import FaceTracker
+            tracker = FaceTracker()
+            if tracker.get_status().name == "UNAVAILABLE":
+                return []
+            sample_fps = inputs.get("sample_fps", 5)
+            result = tracker.execute({
+                "input_path": str(input_path),
+                "sample_fps": sample_fps,
+            })
+            if result.success and result.data:
+                # Read the generated JSON
+                output_file = result.data.get("output")
+                if output_file:
+                    data = json.loads(Path(output_file).read_text(encoding="utf-8"))
+                    return data.get("faces", [])
+        except Exception:
+            pass
+
+        return []
+
+    def _compute_face_tracked_crop(
+        self,
+        faces: list[dict],
+        src_w: int, src_h: int,
+        crop_w: int, crop_h: int,
+        fps: float,
+        smoothing_window: int,
+        face_padding: float,
+    ) -> tuple[list[int], list[int]]:
+        """Compute smoothed crop positions from face tracking data.
+
+        Returns a single (x, y) if face positions are stable enough,
+        or lists of per-frame positions for dynamic cropping.
+        """
+        if not faces:
+            cx = (src_w - crop_w) // 2
+            cy = (src_h - crop_h) // 2
+            return cx, cy
+
+        # Convert relative bbox centers to pixel positions
+        face_centers_x = []
+        face_centers_y = []
+        face_timestamps = []
+
+        for f in faces:
+            bbox = f["bbox"]
+            # Center of face in pixel space
+            center_x = (bbox["x"] + bbox["width"] / 2) * src_w
+            center_y = (bbox["y"] + bbox["height"] / 2) * src_h
+            face_centers_x.append(center_x)
+            face_centers_y.append(center_y)
+            face_timestamps.append(f["timestamp_seconds"])
+
+        # Check if face position is stable (talking head usually is)
+        x_range = max(face_centers_x) - min(face_centers_x)
+        y_range = max(face_centers_y) - min(face_centers_y)
+
+        # If face barely moves (<10% of frame), use a single static crop
+        if x_range < src_w * 0.10 and y_range < src_h * 0.10:
+            avg_x = sum(face_centers_x) / len(face_centers_x)
+            avg_y = sum(face_centers_y) / len(face_centers_y)
+
+            # Position crop window centered on face, with bias toward upper third
+            crop_x = int(avg_x - crop_w / 2)
+            crop_y = int(avg_y - crop_h * 0.35)  # Face in upper 35% of frame
+
+            # Clamp to frame bounds
+            crop_x = max(0, min(crop_x, src_w - crop_w))
+            crop_y = max(0, min(crop_y, src_h - crop_h))
+
+            return crop_x, crop_y
+
+        # Dynamic crop: smooth the trajectory
+        smoothed_x = self._smooth_positions(face_centers_x, smoothing_window)
+        smoothed_y = self._smooth_positions(face_centers_y, smoothing_window)
+
+        # Convert to crop positions (top-left corner), clamped
+        crop_xs = []
+        crop_ys = []
+        for sx, sy in zip(smoothed_x, smoothed_y):
+            cx = int(sx - crop_w / 2)
+            cy = int(sy - crop_h * 0.35)
+            cx = max(0, min(cx, src_w - crop_w))
+            cy = max(0, min(cy, src_h - crop_h))
+            crop_xs.append(cx)
+            crop_ys.append(cy)
+
+        return crop_xs, crop_ys
+
+    def _smooth_positions(self, values: list[float], window: int) -> list[float]:
+        """Simple moving average smoothing."""
+        smoothed = []
+        for i in range(len(values)):
+            start = max(0, i - window // 2)
+            end = min(len(values), i + window // 2 + 1)
+            smoothed.append(sum(values[start:end]) / (end - start))
+        return smoothed
+
+    def _render_static_crop(
+        self,
+        input_path: Path, output_path: Path,
+        crop_x: int, crop_y: int,
+        crop_w: int, crop_h: int,
+        out_w: int, out_h: int,
+        codec: str, crf: int,
+    ) -> ToolResult:
+        """Render with a static crop position."""
+        vf = f"crop={crop_w}:{crop_h}:{crop_x}:{crop_y},scale={out_w}:{out_h}"
+
+        cmd = [
+            "ffmpeg", "-y",
+            "-i", str(input_path),
+            "-vf", vf,
+            "-c:v", codec, "-crf", str(crf), "-preset", "fast",
+            "-c:a", "aac", "-b:a", "192k",
+            str(output_path),
+        ]
+
+        try:
+            self.run_command(cmd, timeout=600)
+        except Exception as e:
+            return ToolResult(success=False, error=f"FFmpeg render failed: {e}")
+
+        return ToolResult(success=True)
+
+    def _render_dynamic_crop(
+        self,
+        input_path: Path, output_path: Path,
+        crop_xs: list[int], crop_ys: list[int],
+        crop_w: int, crop_h: int,
+        out_w: int, out_h: int,
+        fps: float,
+        codec: str, crf: int,
+    ) -> ToolResult:
+        """Render with dynamic crop positions that follow the face.
+
+        Uses FFmpeg's sendcmd filter to update crop position over time.
+        For simplicity and reliability, we interpolate between key positions
+        using FFmpeg expression-based crop.
+        """
+        # Build a piecewise-linear x(t) and y(t) using FFmpeg expressions
+        # We'll sample at the face tracking rate and interpolate between points
+        if not crop_xs:
+            return ToolResult(success=False, error="No crop positions computed")
+
+        # If very few data points, fall back to static using average
+        if len(crop_xs) < 3:
+            avg_x = int(sum(crop_xs) / len(crop_xs))
+            avg_y = int(sum(crop_ys) / len(crop_ys))
+            return self._render_static_crop(
+                input_path, output_path, avg_x, avg_y,
+                crop_w, crop_h, out_w, out_h, codec, crf,
+            )
+
+        # Build sendcmd script for crop filter position updates
+        # Each command sets the crop x,y at the corresponding timestamp
+        temp_dir = output_path.parent / ".reframe_tmp"
+        temp_dir.mkdir(parents=True, exist_ok=True)
+        sendcmd_path = temp_dir / "crop_commands.txt"
+
+        # Approximate timestamps from the face tracking sample rate
+        # The face data was sampled at sample_fps intervals
+        sample_interval = 1.0 / (fps / max(1, int(fps / 5)))  # Approximate
+
+        lines = []
+        for i, (cx, cy) in enumerate(zip(crop_xs, crop_ys)):
+            ts = i * sample_interval
+            lines.append(f"{ts:.3f} [enter] crop x {cx};")
+            lines.append(f"{ts:.3f} [enter] crop y {cy};")
+
+        sendcmd_path.write_text("\n".join(lines), encoding="utf-8")
+
+        # Use crop with sendcmd for dynamic positioning
+        vf = (
+            f"sendcmd=f='{str(sendcmd_path).replace(chr(92), '/')}':flags=enter,"
+            f"crop={crop_w}:{crop_h}:{crop_xs[0]}:{crop_ys[0]},"
+            f"scale={out_w}:{out_h}"
+        )
+
+        cmd = [
+            "ffmpeg", "-y",
+            "-i", str(input_path),
+            "-vf", vf,
+            "-c:v", codec, "-crf", str(crf), "-preset", "fast",
+            "-c:a", "aac", "-b:a", "192k",
+            str(output_path),
+        ]
+
+        try:
+            self.run_command(cmd, timeout=600)
+        except Exception:
+            # sendcmd can be finicky — fall back to static crop with average position
+            avg_x = int(sum(crop_xs) / len(crop_xs))
+            avg_y = int(sum(crop_ys) / len(crop_ys))
+            result = self._render_static_crop(
+                input_path, output_path, avg_x, avg_y,
+                crop_w, crop_h, out_w, out_h, codec, crf,
+            )
+            if result.success:
+                result.data = result.data or {}
+                result.data["fallback"] = "sendcmd failed, used static average crop"
+            return result
+        finally:
+            # Clean up
+            if sendcmd_path.exists():
+                sendcmd_path.unlink()
+            try:
+                temp_dir.rmdir()
+            except OSError:
+                pass
+
+        return ToolResult(success=True)
+
+    def estimate_runtime(self, inputs: dict[str, Any]) -> float:
+        """Estimate runtime in seconds. Roughly 1x realtime for face tracking + render."""
+        return 60.0  # Conservative default
+
+    @staticmethod
+    def list_presets() -> dict[str, str]:
+        """Return available aspect ratio presets."""
+        return {
+            name: f"{w}:{h}"
+            for name, (w, h) in ASPECT_PRESETS.items()
+        }
diff --git a/tools/video/remotion_caption_burn.py b/tools/video/remotion_caption_burn.py
new file mode 100644
index 0000000..6824e69
--- /dev/null
+++ b/tools/video/remotion_caption_burn.py
@@ -0,0 +1,439 @@
+"""Remotion caption burn tool.
+
+Renders animated word-by-word captions onto a talking-head video using
+the Remotion CaptionOverlay component.  Falls back to FFmpeg subtitle
+burning if Remotion is not available.
+
+The tool:
+1. Converts word-level transcript segments to Remotion WordCaption JSON
+2. Writes a props file for the TalkingHead composition
+3. Renders via ``npx remotion render``
+4. Returns the captioned video path
+
+Fallback: if Remotion is unavailable, burns subtitles at the bottom of
+the frame using FFmpeg's ``subtitles`` filter with bold styling.
+"""
+
+from __future__ import annotations
+
+import json
+import math
+import re
+import shutil
+import time
+from pathlib import Path
+from typing import Any
+
+from tools.base_tool import (
+    BaseTool,
+    Determinism,
+    ExecutionMode,
+    ResourceProfile,
+    ToolResult,
+    ToolStability,
+    ToolTier,
+)
+
+
+class RemotionCaptionBurn(BaseTool):
+    name = "remotion_caption_burn"
+    version = "0.1.0"
+    tier = ToolTier.CORE
+    capability = "subtitle"
+    provider = "remotion"
+    stability = ToolStability.EXPERIMENTAL
+    execution_mode = ExecutionMode.SYNC
+    determinism = Determinism.DETERMINISTIC
+
+    dependencies = ["cmd:ffmpeg", "cmd:ffprobe"]
+    install_instructions = (
+        "Remotion (optional, preferred): npm install in remotion-composer/\n"
+        "FFmpeg (required for fallback): https://ffmpeg.org/download.html"
+    )
+    agent_skills = ["remotion-best-practices", "ffmpeg"]
+
+    capabilities = [
+        "burn_remotion_captions",
+        "burn_ffmpeg_captions_fallback",
+    ]
+
+    input_schema = {
+        "type": "object",
+        "required": ["input_path", "output_path"],
+        "properties": {
+            "input_path": {
+                "type": "string",
+                "description": "Path to the input video (enhanced talking-head footage).",
+            },
+            "output_path": {
+                "type": "string",
+                "description": "Path for the output video with captions burned in.",
+            },
+            "segments": {
+                "type": "array",
+                "description": (
+                    "Word-level transcript segments from transcriber tool. "
+                    "Each segment has 'words' array with {word, start, end}."
+                ),
+            },
+            "srt_path": {
+                "type": "string",
+                "description": (
+                    "Path to an SRT file. Used as an alternative to segments. "
+                    "If both provided, segments take priority."
+                ),
+            },
+            "words_per_page": {
+                "type": "integer",
+                "default": 4,
+                "description": "Words shown at once in the caption overlay.",
+            },
+            "font_size": {
+                "type": "integer",
+                "default": 52,
+                "description": "Font size for captions.",
+            },
+            "highlight_color": {
+                "type": "string",
+                "default": "#22D3EE",
+                "description": "Highlight color for the active word (hex).",
+            },
+            "corrections": {
+                "type": "object",
+                "description": (
+                    "Dictionary of word corrections for common misrecognitions. "
+                    "Keys are the wrong word (case-insensitive), values are the "
+                    "correct replacement. Example: {\"cloud\": \"Claude\"}."
+                ),
+            },
+            "force_ffmpeg": {
+                "type": "boolean",
+                "default": False,
+                "description": "Force FFmpeg fallback even if Remotion is available.",
+            },
+        },
+    }
+
+    resource_profile = ResourceProfile(cpu_cores=4, ram_mb=2048, vram_mb=0, disk_mb=500)
+    idempotency_key_fields = ["input_path", "segments", "srt_path"]
+    side_effects = ["writes captioned video to output_path"]
+    user_visible_verification = [
+        "Play the output video and verify captions appear at the bottom of the frame",
+        "Check that the active word is highlighted in the specified color",
+        "Verify face is not occluded by caption text",
+    ]
+
+    # ------------------------------------------------------------------ #
+    #  Remotion detection
+    # ------------------------------------------------------------------ #
+
+    def _find_remotion_root(self) -> Path | None:
+        """Find the remotion-composer directory relative to the repo."""
+        candidates = [
+            Path.cwd() / "remotion-composer",
+            Path(__file__).resolve().parent.parent.parent / "remotion-composer",
+        ]
+        for p in candidates:
+            if (
+                p.is_dir()
+                and (p / "package.json").exists()
+                and (p / "node_modules").is_dir()
+            ):
+                return p
+        return None
+
+    def _remotion_available(self) -> bool:
+        return (
+            shutil.which("npx") is not None
+            and self._find_remotion_root() is not None
+        )
+
+    # ------------------------------------------------------------------ #
+    #  Word caption conversion
+    # ------------------------------------------------------------------ #
+
+    def _segments_to_word_captions(
+        self, segments: list[dict], corrections: dict[str, str] | None = None
+    ) -> list[dict]:
+        """Convert transcriber segments to [{word, startMs, endMs}, ...]."""
+        captions: list[dict] = []
+        corr = {k.lower(): v for k, v in (corrections or {}).items()}
+
+        for seg in segments:
+            words = seg.get("words", [])
+            if words:
+                for w in words:
+                    raw = w["word"].strip()
+                    fixed = corr.get(raw.lower().strip(".,!?;:"), raw)
+                    # Preserve trailing punctuation from original
+                    trailing = ""
+                    if raw and raw[-1] in ".,!?;:":
+                        trailing = raw[-1]
+                    if fixed != raw and not fixed.endswith(trailing):
+                        fixed = fixed + trailing
+                    captions.append({
+                        "word": fixed,
+                        "startMs": int(w["start"] * 1000),
+                        "endMs": int(w["end"] * 1000),
+                    })
+            elif "text" in seg:
+                text_words = seg["text"].strip().split()
+                dur = seg["end"] - seg["start"]
+                per_word = dur / max(len(text_words), 1)
+                for i, tw in enumerate(text_words):
+                    fixed = corr.get(tw.lower().strip(".,!?;:"), tw)
+                    captions.append({
+                        "word": fixed,
+                        "startMs": int((seg["start"] + i * per_word) * 1000),
+                        "endMs": int((seg["start"] + (i + 1) * per_word) * 1000),
+                    })
+        return captions
+
+    def _srt_to_word_captions(
+        self, srt_path: str, corrections: dict[str, str] | None = None
+    ) -> list[dict]:
+        """Parse SRT file into word captions."""
+        content = Path(srt_path).read_text(encoding="utf-8")
+        blocks = re.split(r"\n\n+", content.strip())
+        corr = {k.lower(): v for k, v in (corrections or {}).items()}
+        captions: list[dict] = []
+
+        for block in blocks:
+            lines = block.strip().split("\n")
+            if len(lines) < 3:
+                continue
+            m = re.match(
+                r"(\d{2}):(\d{2}):(\d{2}),(\d{3})\s*-->\s*"
+                r"(\d{2}):(\d{2}):(\d{2}),(\d{3})",
+                lines[1],
+            )
+            if not m:
+                continue
+            start_ms = (
+                int(m.group(1)) * 3600000
+                + int(m.group(2)) * 60000
+                + int(m.group(3)) * 1000
+                + int(m.group(4))
+            )
+            end_ms = (
+                int(m.group(5)) * 3600000
+                + int(m.group(6)) * 60000
+                + int(m.group(7)) * 1000
+                + int(m.group(8))
+            )
+            text = " ".join(lines[2:]).strip()
+            words = text.split()
+            per_word = (end_ms - start_ms) / max(len(words), 1)
+            for i, w in enumerate(words):
+                fixed = corr.get(w.lower().strip(".,!?;:"), w)
+                captions.append({
+                    "word": fixed,
+                    "startMs": int(start_ms + i * per_word),
+                    "endMs": int(start_ms + (i + 1) * per_word),
+                })
+        return captions
+
+    # ------------------------------------------------------------------ #
+    #  Remotion render
+    # ------------------------------------------------------------------ #
+
+    def _render_remotion(
+        self,
+        input_path: str,
+        output_path: str,
+        captions: list[dict],
+        words_per_page: int,
+        font_size: int,
+        highlight_color: str,
+    ) -> ToolResult:
+        root = self._find_remotion_root()
+        if root is None:
+            return ToolResult(success=False, error="Remotion root not found")
+
+        # Get video duration in frames
+        dur_cmd = [
+            "ffprobe", "-v", "error",
+            "-show_entries", "format=duration",
+            "-of", "csv=p=0",
+            input_path,
+        ]
+        dur_out = self.run_command(dur_cmd, capture=True)
+        duration_s = float(dur_out.strip().split("\n")[0])
+        total_frames = math.ceil(duration_s * 30)
+
+        # Copy video to Remotion public folder
+        pub_dir = root / "public" / "talking-head"
+        pub_dir.mkdir(parents=True, exist_ok=True)
+        video_filename = Path(input_path).name
+        dest_video = pub_dir / video_filename
+        shutil.copy2(input_path, dest_video)
+
+        # Build props JSON
+        props = {
+            "videoSrc": f"public/talking-head/{video_filename}",
+            "captions": captions,
+            "wordsPerPage": words_per_page,
+            "fontSize": font_size,
+            "highlightColor": highlight_color,
+        }
+        props_dir = root / "public" / "demo-props"
+        props_dir.mkdir(parents=True, exist_ok=True)
+        props_file = props_dir / f"caption-burn-{Path(input_path).stem}.json"
+        props_file.write_text(json.dumps(props, indent=2), encoding="utf-8")
+
+        # Render
+        render_cmd = [
+            "npx", "remotion", "render",
+            "src/index.tsx", "TalkingHead",
+            f"--props={props_file.relative_to(root)}",
+            "--width=1080", "--height=1920", "--fps=30",
+            f"--frames=0-{total_frames - 1}",
+            "--codec=h264", "--crf=18",
+            str(Path(output_path).resolve()),
+        ]
+        self.run_command(render_cmd, cwd=str(root))
+
+        if not Path(output_path).exists():
+            return ToolResult(success=False, error="Remotion render produced no output")
+
+        return ToolResult(
+            success=True,
+            data={
+                "method": "remotion",
+                "output": output_path,
+                "duration_seconds": round(duration_s, 2),
+                "total_frames": total_frames,
+                "caption_count": len(captions),
+                "words_per_page": words_per_page,
+            },
+            artifacts=[output_path],
+        )
+
+    # ------------------------------------------------------------------ #
+    #  FFmpeg fallback
+    # ------------------------------------------------------------------ #
+
+    def _render_ffmpeg(
+        self,
+        input_path: str,
+        output_path: str,
+        captions: list[dict],
+    ) -> ToolResult:
+        """Fall back to FFmpeg subtitle burning at bottom of frame."""
+        # Generate temporary SRT from word captions
+        tmp_srt = Path(output_path).parent / f"_tmp_captions_{int(time.time())}.srt"
+        tmp_srt.parent.mkdir(parents=True, exist_ok=True)
+
+        srt_lines = []
+        idx = 1
+        # Group into pages of ~4 words
+        page_size = 4
+        for i in range(0, len(captions), page_size):
+            page = captions[i : i + page_size]
+            text = " ".join(c["word"] for c in page)
+            start = page[0]["startMs"]
+            end = page[-1]["endMs"]
+            srt_lines.append(str(idx))
+            srt_lines.append(
+                f"{self._ms_to_srt(start)} --> {self._ms_to_srt(end)}"
+            )
+            srt_lines.append(text)
+            srt_lines.append("")
+            idx += 1
+
+        tmp_srt.write_text("\n".join(srt_lines), encoding="utf-8")
+
+        # Escape path for FFmpeg subtitles filter (Windows colon issue)
+        srt_escaped = str(tmp_srt).replace("\\", "/").replace(":", "\\:")
+
+        cmd = [
+            "ffmpeg", "-y",
+            "-i", input_path,
+            "-vf", (
+                f"subtitles='{srt_escaped}'"
+                ":force_style='FontName=Segoe UI,FontSize=24,Bold=1,"
+                "PrimaryColour=&H00FFFFFF,OutlineColour=&H00000000,"
+                "Outline=3,Shadow=2,Alignment=2,MarginV=100'"
+            ),
+            "-c:v", "libx264", "-preset", "fast", "-crf", "18",
+            "-pix_fmt", "yuv420p",
+            "-c:a", "copy",
+            output_path,
+        ]
+        self.run_command(cmd)
+
+        # Clean up temp SRT
+        try:
+            tmp_srt.unlink()
+        except OSError:
+            pass
+
+        if not Path(output_path).exists():
+            return ToolResult(success=False, error="FFmpeg subtitle burn produced no output")
+
+        return ToolResult(
+            success=True,
+            data={
+                "method": "ffmpeg_fallback",
+                "output": output_path,
+                "caption_count": len(captions),
+                "note": "Used FFmpeg fallback. Install Remotion for animated captions.",
+            },
+            artifacts=[output_path],
+        )
+
+    @staticmethod
+    def _ms_to_srt(ms: int) -> str:
+        h = ms // 3600000
+        m = (ms % 3600000) // 60000
+        s = (ms % 60000) // 1000
+        rem = ms % 1000
+        return f"{h:02d}:{m:02d}:{s:02d},{rem:03d}"
+
+    # ------------------------------------------------------------------ #
+    #  Main execute
+    # ------------------------------------------------------------------ #
+
+    def execute(self, inputs: dict[str, Any]) -> ToolResult:
+        input_path = inputs["input_path"]
+        output_path = inputs["output_path"]
+        corrections = inputs.get("corrections")
+        force_ffmpeg = inputs.get("force_ffmpeg", False)
+        words_per_page = inputs.get("words_per_page", 4)
+        font_size = inputs.get("font_size", 52)
+        highlight_color = inputs.get("highlight_color", "#22D3EE")
+
+        if not Path(input_path).exists():
+            return ToolResult(success=False, error=f"Input video not found: {input_path}")
+
+        Path(output_path).parent.mkdir(parents=True, exist_ok=True)
+        start = time.time()
+
+        # Build word captions from segments or SRT
+        segments = inputs.get("segments")
+        srt_path = inputs.get("srt_path")
+
+        if segments:
+            captions = self._segments_to_word_captions(segments, corrections)
+        elif srt_path:
+            captions = self._srt_to_word_captions(srt_path, corrections)
+        else:
+            return ToolResult(
+                success=False,
+                error="Provide either 'segments' (from transcriber) or 'srt_path'.",
+            )
+
+        if not captions:
+            return ToolResult(success=False, error="No caption words extracted.")
+
+        # Choose render method
+        if not force_ffmpeg and self._remotion_available():
+            result = self._render_remotion(
+                input_path, output_path, captions,
+                words_per_page, font_size, highlight_color,
+            )
+        else:
+            result = self._render_ffmpeg(input_path, output_path, captions)
+
+        result.duration_seconds = round(time.time() - start, 2)
+        return result
diff --git a/tools/video/showcase_card.py b/tools/video/showcase_card.py
new file mode 100644
index 0000000..ec0f3f4
--- /dev/null
+++ b/tools/video/showcase_card.py
@@ -0,0 +1,226 @@
+"""Showcase card tool wrapping FFmpeg.
+
+Creates a presentation-ready 9:16 card from a source video: letterboxes
+the content, adds a bold title at the top, a subtitle description at the
+bottom, and a dark background.  Designed for Instagram Reels / TikTok
+showcase segments.
+"""
+
+from __future__ import annotations
+
+import time
+from pathlib import Path
+from typing import Any
+
+from tools.base_tool import (
+    BaseTool,
+    Determinism,
+    ExecutionMode,
+    ResourceProfile,
+    ToolResult,
+    ToolStability,
+    ToolTier,
+)
+
+
+class ShowcaseCard(BaseTool):
+    name = "showcase_card"
+    version = "0.1.0"
+    tier = ToolTier.CORE
+    capability = "video_post"
+    provider = "ffmpeg"
+    stability = ToolStability.EXPERIMENTAL
+    execution_mode = ExecutionMode.SYNC
+    determinism = Determinism.DETERMINISTIC
+
+    dependencies = ["cmd:ffmpeg", "cmd:ffprobe"]
+    install_instructions = "Install FFmpeg: https://ffmpeg.org/download.html"
+    agent_skills = ["ffmpeg", "video_toolkit"]
+
+    capabilities = ["create_showcase_card"]
+
+    input_schema = {
+        "type": "object",
+        "required": ["input_path", "output_path", "title"],
+        "properties": {
+            "input_path": {
+                "type": "string",
+                "description": "Path to the source video.",
+            },
+            "output_path": {
+                "type": "string",
+                "description": "Path for the output showcase card video.",
+            },
+            "title": {
+                "type": "string",
+                "description": "Bold title text displayed at the top of the card.",
+            },
+            "subtitle": {
+                "type": "string",
+                "default": "",
+                "description": "Subtitle text displayed at the bottom of the card.",
+            },
+            "output_width": {
+                "type": "integer",
+                "default": 1080,
+                "description": "Output width in pixels.",
+            },
+            "output_height": {
+                "type": "integer",
+                "default": 1920,
+                "description": "Output height in pixels.",
+            },
+            "background_color": {
+                "type": "string",
+                "default": "0x0A0F1A",
+                "description": "Background color in hex (FFmpeg format, e.g. 0x0A0F1A).",
+            },
+            "title_font": {
+                "type": "string",
+                "default": "segoeuib.ttf",
+                "description": "Font file for the title. Uses system font lookup.",
+            },
+            "title_font_size": {
+                "type": "integer",
+                "default": 52,
+                "description": "Font size for the title.",
+            },
+            "subtitle_font_size": {
+                "type": "integer",
+                "default": 28,
+                "description": "Font size for the subtitle.",
+            },
+            "title_color": {
+                "type": "string",
+                "default": "white",
+                "description": "Title text color.",
+            },
+            "watermark": {
+                "type": "string",
+                "default": "",
+                "description": "Optional watermark text overlaid on the video (e.g. brand name).",
+            },
+        },
+    }
+
+    resource_profile = ResourceProfile(cpu_cores=2, ram_mb=1024, vram_mb=0, disk_mb=500)
+    idempotency_key_fields = ["input_path", "title", "subtitle"]
+    side_effects = ["writes showcase card video to output_path"]
+    user_visible_verification = [
+        "Play output and verify title, subtitle, and video are positioned correctly",
+        "Verify the video content is fully visible (not cropped)",
+    ]
+
+    def execute(self, inputs: dict[str, Any]) -> ToolResult:
+        input_path = inputs["input_path"]
+        output_path = inputs["output_path"]
+        title = inputs["title"]
+        subtitle = inputs.get("subtitle", "")
+        out_w = inputs.get("output_width", 1080)
+        out_h = inputs.get("output_height", 1920)
+        bg_color = inputs.get("background_color", "0x0A0F1A")
+        title_font = inputs.get("title_font", "segoeuib.ttf")
+        title_font_size = inputs.get("title_font_size", 52)
+        subtitle_font_size = inputs.get("subtitle_font_size", 28)
+        title_color = inputs.get("title_color", "white")
+        watermark = inputs.get("watermark", "")
+
+        if not Path(input_path).exists():
+            return ToolResult(success=False, error=f"Input not found: {input_path}")
+
+        Path(output_path).parent.mkdir(parents=True, exist_ok=True)
+        start = time.time()
+
+        # Get source dimensions
+        probe_cmd = [
+            "ffprobe", "-v", "error",
+            "-select_streams", "v:0",
+            "-show_entries", "stream=width,height",
+            "-of", "csv=p=0",
+            input_path,
+        ]
+        probe_out = self.run_command(probe_cmd, capture=True).strip()
+        src_w, src_h = [int(x.strip()) for x in probe_out.split(",")[:2]]
+
+        # Calculate letterbox dimensions — fit source into output width,
+        # center vertically in the frame.
+        scale_factor = out_w / src_w
+        scaled_h = int(src_h * scale_factor)
+        # Ensure even dimensions
+        scaled_h = scaled_h if scaled_h % 2 == 0 else scaled_h + 1
+        pad_y = (out_h - scaled_h) // 2
+
+        # Build filter chain
+        filters = [
+            f"scale={out_w}:{scaled_h}",
+            f"pad={out_w}:{out_h}:0:{pad_y}:color={bg_color}",
+        ]
+
+        # Title text at top
+        title_escaped = title.replace("'", "\\'").replace(":", "\\:")
+        filters.append(
+            f"drawtext=text='{title_escaped}'"
+            f":fontfile='{title_font}'"
+            f":fontsize={title_font_size}"
+            f":fontcolor={title_color}"
+            f":borderw=3:bordercolor=black"
+            f":x=(w-text_w)/2:y=60"
+        )
+
+        # Subtitle text at bottom
+        if subtitle:
+            sub_escaped = subtitle.replace("'", "\\'").replace(":", "\\:")
+            filters.append(
+                f"drawtext=text='{sub_escaped}'"
+                f":fontfile='segoeui.ttf'"
+                f":fontsize={subtitle_font_size}"
+                f":fontcolor=white@0.85"
+                f":x=(w-text_w)/2:y=h-100"
+            )
+
+        # Watermark centered on video
+        if watermark:
+            wm_escaped = watermark.replace("'", "\\'").replace(":", "\\:")
+            filters.append(
+                f"drawtext=text='{wm_escaped}'"
+                f":fontfile='segoeui.ttf'"
+                f":fontsize=36"
+                f":fontcolor=white@0.3"
+                f":x=(w-text_w)/2:y=(h-text_h)/2"
+            )
+
+        vf = ",".join(filters)
+
+        cmd = [
+            "ffmpeg", "-y",
+            "-i", input_path,
+            "-vf", vf,
+            "-c:v", "libx264", "-preset", "fast", "-crf", "18",
+            "-pix_fmt", "yuv420p",
+            "-c:a", "aac", "-b:a", "192k",
+            output_path,
+        ]
+
+        try:
+            self.run_command(cmd)
+        except Exception as e:
+            return ToolResult(success=False, error=f"FFmpeg failed: {e}")
+
+        if not Path(output_path).exists():
+            return ToolResult(success=False, error="No output produced")
+
+        elapsed = round(time.time() - start, 2)
+
+        return ToolResult(
+            success=True,
+            data={
+                "output": output_path,
+                "source_resolution": f"{src_w}x{src_h}",
+                "output_resolution": f"{out_w}x{out_h}",
+                "title": title,
+                "subtitle": subtitle,
+                "letterbox_y_offset": pad_y,
+            },
+            artifacts=[output_path],
+            duration_seconds=elapsed,
+        )
diff --git a/tools/video/silence_cutter.py b/tools/video/silence_cutter.py
new file mode 100644
index 0000000..922b1d6
--- /dev/null
+++ b/tools/video/silence_cutter.py
@@ -0,0 +1,481 @@
+"""Silence cutter tool for automatic jump cuts.
+
+Detects silent segments in talking-head footage and removes them,
+creating tight jump cuts. Uses FFmpeg's silencedetect filter — no
+external dependencies beyond FFmpeg.
+
+Modes:
+  - remove: Cut out silent segments entirely (jump cut)
+  - speed_up: Speed up silent segments instead of cutting (less jarring)
+  - mark: Don't cut — just output silence timestamps for manual review
+"""
+
+from __future__ import annotations
+
+import json
+import re
+import time
+from pathlib import Path
+from typing import Any
+
+from tools.base_tool import (
+    BaseTool,
+    Determinism,
+    ExecutionMode,
+    ResourceProfile,
+    RetryPolicy,
+    ResumeSupport,
+    ToolResult,
+    ToolStability,
+    ToolTier,
+)
+
+
+class SilenceCutter(BaseTool):
+    name = "silence_cutter"
+    version = "0.1.0"
+    tier = ToolTier.CORE
+    capability = "video_post"
+    provider = "ffmpeg"
+    stability = ToolStability.EXPERIMENTAL
+    execution_mode = ExecutionMode.SYNC
+    determinism = Determinism.DETERMINISTIC
+
+    dependencies = ["cmd:ffmpeg"]
+    install_instructions = "Install FFmpeg: https://ffmpeg.org/download.html"
+    agent_skills = ["ffmpeg"]
+
+    capabilities = [
+        "silence_detection",
+        "jump_cut",
+        "silence_removal",
+        "silence_speedup",
+    ]
+
+    input_schema = {
+        "type": "object",
+        "required": ["input_path"],
+        "properties": {
+            "input_path": {"type": "string"},
+            "output_path": {"type": "string"},
+            "mode": {
+                "type": "string",
+                "enum": ["remove", "speed_up", "mark"],
+                "default": "remove",
+                "description": "remove=jump cut, speed_up=fast-forward silence, mark=detect only",
+            },
+            "silence_threshold_db": {
+                "type": "number",
+                "default": -35,
+                "description": "Audio level below this (in dB) is considered silence. Lower = more sensitive.",
+            },
+            "min_silence_duration": {
+                "type": "number",
+                "default": 0.5,
+                "minimum": 0.1,
+                "description": "Minimum silence duration in seconds to trigger a cut",
+            },
+            "padding_seconds": {
+                "type": "number",
+                "default": 0.08,
+                "minimum": 0.0,
+                "description": "Seconds of silence to keep on each side of speech (prevents clipped words)",
+            },
+            "silence_speed_factor": {
+                "type": "number",
+                "default": 6.0,
+                "minimum": 1.5,
+                "maximum": 100.0,
+                "description": "Speed multiplier for silent segments (only used in speed_up mode)",
+            },
+            "codec": {"type": "string", "default": "libx264"},
+            "crf": {"type": "integer", "default": 18},
+        },
+    }
+
+    resource_profile = ResourceProfile(
+        cpu_cores=4, ram_mb=2048, vram_mb=0, disk_mb=4000, network_required=False
+    )
+    retry_policy = RetryPolicy(max_retries=1, retryable_errors=["FFmpeg error"])
+    resume_support = ResumeSupport.FROM_START
+    idempotency_key_fields = [
+        "input_path", "mode", "silence_threshold_db",
+        "min_silence_duration", "padding_seconds",
+    ]
+    side_effects = ["writes cut video to output_path"]
+    user_visible_verification = [
+        "Watch output for unnaturally clipped words at cut points",
+        "Compare duration: output should be noticeably shorter than input",
+    ]
+
+    def execute(self, inputs: dict[str, Any]) -> ToolResult:
+        input_path = Path(inputs["input_path"])
+        if not input_path.exists():
+            return ToolResult(success=False, error=f"Input not found: {input_path}")
+
+        mode = inputs.get("mode", "remove")
+        start = time.time()
+
+        # Step 1: Detect silence segments
+        threshold_db = inputs.get("silence_threshold_db", -35)
+        min_dur = inputs.get("min_silence_duration", 0.5)
+        padding = inputs.get("padding_seconds", 0.08)
+
+        silences = self._detect_silence(input_path, threshold_db, min_dur)
+
+        if not silences:
+            elapsed = time.time() - start
+            return ToolResult(
+                success=True,
+                data={
+                    "message": "No silence detected — video unchanged",
+                    "silence_segments": 0,
+                    "input": str(input_path),
+                    "output": str(input_path),
+                },
+                artifacts=[str(input_path)],
+                duration_seconds=round(elapsed, 2),
+            )
+
+        # Get total duration
+        total_duration = self._get_duration(input_path)
+
+        # Step 2: Compute speech segments (inverse of silence)
+        speech_segments = self._compute_speech_segments(
+            silences, total_duration, padding
+        )
+
+        # Step 3: Handle based on mode
+        if mode == "mark":
+            elapsed = time.time() - start
+            output_json = Path(
+                inputs.get("output_path", str(input_path.with_suffix(".silence.json")))
+            )
+            result_data = {
+                "silences": silences,
+                "speech_segments": speech_segments,
+                "total_duration": total_duration,
+                "silence_duration": sum(s["duration"] for s in silences),
+                "speech_duration": sum(s["end"] - s["start"] for s in speech_segments),
+            }
+            output_json.parent.mkdir(parents=True, exist_ok=True)
+            output_json.write_text(json.dumps(result_data, indent=2), encoding="utf-8")
+            return ToolResult(
+                success=True,
+                data={
+                    "mode": "mark",
+                    "silence_segments": len(silences),
+                    "speech_segments": len(speech_segments),
+                    "silence_duration_seconds": round(result_data["silence_duration"], 2),
+                    "speech_duration_seconds": round(result_data["speech_duration"], 2),
+                    "output": str(output_json),
+                },
+                artifacts=[str(output_json)],
+                duration_seconds=round(elapsed, 2),
+            )
+
+        output_path = Path(
+            inputs.get("output_path", str(input_path.with_stem(f"{input_path.stem}_cut")))
+        )
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+        codec = inputs.get("codec", "libx264")
+        crf = inputs.get("crf", 18)
+
+        if mode == "speed_up":
+            speed_factor = inputs.get("silence_speed_factor", 6.0)
+            result = self._render_speed_up(
+                input_path, output_path, silences, speech_segments,
+                total_duration, speed_factor, codec, crf,
+            )
+        else:
+            result = self._render_jump_cut(
+                input_path, output_path, speech_segments, codec, crf,
+            )
+
+        if not result.success:
+            return result
+
+        elapsed = time.time() - start
+
+        silence_dur = sum(s["duration"] for s in silences)
+        speech_dur = sum(s["end"] - s["start"] for s in speech_segments)
+
+        return ToolResult(
+            success=True,
+            data={
+                "mode": mode,
+                "input": str(input_path),
+                "output": str(output_path),
+                "input_duration": round(total_duration, 2),
+                "output_duration": round(speech_dur, 2) if mode == "remove" else None,
+                "silence_removed_seconds": round(silence_dur, 2),
+                "silence_segments": len(silences),
+                "speech_segments": len(speech_segments),
+                "time_saved_percent": round(silence_dur / total_duration * 100, 1) if total_duration > 0 else 0,
+            },
+            artifacts=[str(output_path)],
+            duration_seconds=round(elapsed, 2),
+        )
+
+    def _detect_silence(
+        self, input_path: Path, threshold_db: float, min_duration: float
+    ) -> list[dict]:
+        """Detect silent segments using FFmpeg silencedetect filter."""
+        cmd = [
+            "ffmpeg",
+            "-i", str(input_path),
+            "-af", f"silencedetect=noise={threshold_db}dB:d={min_duration}",
+            "-f", "null", "-",
+        ]
+
+        try:
+            result = self.run_command(cmd, timeout=300)
+            output = result.stderr
+        except Exception as e:
+            # FFmpeg writes to stderr even on success for filters
+            output = str(e)
+
+        # Parse silencedetect output
+        # Format: [silencedetect @ ...] silence_start: 1.234
+        #         [silencedetect @ ...] silence_end: 2.567 | silence_duration: 1.333
+        starts = re.findall(r"silence_start:\s*([\d.]+)", output)
+        ends = re.findall(r"silence_end:\s*([\d.]+)", output)
+        durations = re.findall(r"silence_duration:\s*([\d.]+)", output)
+
+        silences = []
+        for i in range(min(len(starts), len(ends))):
+            silences.append({
+                "start": float(starts[i]),
+                "end": float(ends[i]),
+                "duration": float(durations[i]) if i < len(durations) else float(ends[i]) - float(starts[i]),
+            })
+
+        return silences
+
+    def _get_duration(self, input_path: Path) -> float:
+        """Get video duration via ffprobe."""
+        cmd = [
+            "ffprobe", "-v", "quiet",
+            "-show_entries", "format=duration",
+            "-of", "json", str(input_path),
+        ]
+        try:
+            result = self.run_command(cmd)
+            data = json.loads(result.stdout)
+            return float(data["format"]["duration"])
+        except Exception:
+            return 0.0
+
+    def _compute_speech_segments(
+        self, silences: list[dict], total_duration: float, padding: float
+    ) -> list[dict]:
+        """Compute speech segments as the inverse of silence segments, with padding."""
+        segments = []
+        cursor = 0.0
+
+        for silence in silences:
+            speech_end = silence["start"] + padding
+            if speech_end > cursor:
+                segments.append({"start": cursor, "end": min(speech_end, total_duration)})
+            cursor = max(cursor, silence["end"] - padding)
+
+        # Final segment after last silence
+        if cursor < total_duration:
+            segments.append({"start": cursor, "end": total_duration})
+
+        # Merge very short gaps (segments < 0.05s apart)
+        merged = []
+        for seg in segments:
+            if seg["end"] - seg["start"] < 0.01:
+                continue  # Skip tiny segments
+            if merged and seg["start"] - merged[-1]["end"] < 0.05:
+                merged[-1]["end"] = seg["end"]
+            else:
+                merged.append(seg)
+
+        return merged
+
+    def _render_jump_cut(
+        self,
+        input_path: Path, output_path: Path,
+        speech_segments: list[dict],
+        codec: str, crf: int,
+    ) -> ToolResult:
+        """Remove silence by concatenating speech segments."""
+        if not speech_segments:
+            return ToolResult(success=False, error="No speech segments found")
+
+        temp_dir = output_path.parent / ".silence_cut_tmp"
+        temp_dir.mkdir(parents=True, exist_ok=True)
+
+        try:
+            # Cut each speech segment
+            seg_files = []
+            for i, seg in enumerate(speech_segments):
+                seg_path = temp_dir / f"seg_{i:04d}.mp4"
+                cmd = [
+                    "ffmpeg", "-y",
+                    "-i", str(input_path),
+                    "-ss", f"{seg['start']:.3f}",
+                    "-to", f"{seg['end']:.3f}",
+                    "-c:v", codec, "-crf", str(crf), "-preset", "fast",
+                    "-c:a", "aac", "-b:a", "192k",
+                    # Force keyframe at start for clean cuts
+                    "-force_key_frames", f"{seg['start']:.3f}",
+                    str(seg_path),
+                ]
+                self.run_command(cmd, timeout=120)
+                if seg_path.exists() and seg_path.stat().st_size > 0:
+                    seg_files.append(seg_path)
+
+            if not seg_files:
+                return ToolResult(success=False, error="No segments were successfully cut")
+
+            # Concat all segments
+            list_path = temp_dir / "concat_list.txt"
+            with open(list_path, "w", encoding="utf-8") as f:
+                for sf in seg_files:
+                    safe_path = str(sf.resolve()).replace("\\", "/")
+                    f.write(f"file '{safe_path}'\n")
+
+            cmd = [
+                "ffmpeg", "-y",
+                "-f", "concat", "-safe", "0",
+                "-i", str(list_path),
+                "-c", "copy",
+                str(output_path),
+            ]
+            self.run_command(cmd, timeout=120)
+
+            return ToolResult(success=True)
+        except Exception as e:
+            return ToolResult(success=False, error=f"Jump cut render failed: {e}")
+        finally:
+            # Clean up temp files
+            for f in temp_dir.glob("*"):
+                try:
+                    f.unlink()
+                except OSError:
+                    pass
+            try:
+                temp_dir.rmdir()
+            except OSError:
+                pass
+
+    def _render_speed_up(
+        self,
+        input_path: Path, output_path: Path,
+        silences: list[dict], speech_segments: list[dict],
+        total_duration: float,
+        speed_factor: float,
+        codec: str, crf: int,
+    ) -> ToolResult:
+        """Speed up silent segments instead of removing them.
+
+        This is less jarring than jump cuts — the viewer sees a brief
+        fast-forward during pauses.
+        """
+        temp_dir = output_path.parent / ".silence_speed_tmp"
+        temp_dir.mkdir(parents=True, exist_ok=True)
+
+        try:
+            # Build a timeline of segments: speech at 1x, silence at Nx
+            all_segments = []
+
+            for seg in speech_segments:
+                all_segments.append({"start": seg["start"], "end": seg["end"], "speed": 1.0})
+
+            for sil in silences:
+                all_segments.append({"start": sil["start"], "end": sil["end"], "speed": speed_factor})
+
+            # Sort by start time and merge overlaps
+            all_segments.sort(key=lambda s: s["start"])
+
+            # Process each segment
+            seg_files = []
+            for i, seg in enumerate(all_segments):
+                seg_path = temp_dir / f"seg_{i:04d}.mp4"
+                duration = seg["end"] - seg["start"]
+                if duration < 0.05:
+                    continue
+
+                if seg["speed"] == 1.0:
+                    # Normal speed
+                    cmd = [
+                        "ffmpeg", "-y",
+                        "-i", str(input_path),
+                        "-ss", f"{seg['start']:.3f}",
+                        "-to", f"{seg['end']:.3f}",
+                        "-c:v", codec, "-crf", str(crf), "-preset", "fast",
+                        "-c:a", "aac", "-b:a", "192k",
+                        str(seg_path),
+                    ]
+                else:
+                    # Speed up
+                    pts = 1.0 / seg["speed"]
+                    atempo_chain = self._build_atempo_chain(seg["speed"])
+                    cmd = [
+                        "ffmpeg", "-y",
+                        "-i", str(input_path),
+                        "-ss", f"{seg['start']:.3f}",
+                        "-to", f"{seg['end']:.3f}",
+                        "-filter:v", f"setpts={pts:.4f}*PTS",
+                        "-filter:a", atempo_chain,
+                        "-c:v", codec, "-crf", str(crf), "-preset", "fast",
+                        "-c:a", "aac", "-b:a", "192k",
+                        str(seg_path),
+                    ]
+
+                self.run_command(cmd, timeout=120)
+                if seg_path.exists() and seg_path.stat().st_size > 0:
+                    seg_files.append(seg_path)
+
+            if not seg_files:
+                return ToolResult(success=False, error="No segments rendered")
+
+            # Concat
+            list_path = temp_dir / "concat_list.txt"
+            with open(list_path, "w", encoding="utf-8") as f:
+                for sf in seg_files:
+                    safe_path = str(sf.resolve()).replace("\\", "/")
+                    f.write(f"file '{safe_path}'\n")
+
+            cmd = [
+                "ffmpeg", "-y",
+                "-f", "concat", "-safe", "0",
+                "-i", str(list_path),
+                "-c", "copy",
+                str(output_path),
+            ]
+            self.run_command(cmd, timeout=120)
+
+            return ToolResult(success=True)
+        except Exception as e:
+            return ToolResult(success=False, error=f"Speed-up render failed: {e}")
+        finally:
+            for f in temp_dir.glob("*"):
+                try:
+                    f.unlink()
+                except OSError:
+                    pass
+            try:
+                temp_dir.rmdir()
+            except OSError:
+                pass
+
+    @staticmethod
+    def _build_atempo_chain(factor: float) -> str:
+        """Build atempo filter chain. atempo accepts [0.5, 100.0]."""
+        filters = []
+        remaining = factor
+        while remaining > 100.0:
+            filters.append("atempo=100.0")
+            remaining /= 100.0
+        while remaining < 0.5:
+            filters.append("atempo=0.5")
+            remaining /= 0.5
+        filters.append(f"atempo={remaining:.4f}")
+        return ",".join(filters)
+
+    def estimate_runtime(self, inputs: dict[str, Any]) -> float:
+        return 45.0