Introduce a hand-authored, project-local Remotion render path that bypasses
the cut-schema and the stock scene-type registry, for hero/bespoke videos
that must look distinct from one another.
- video_compose: composition_mode="atelier" (or renderer_family="bespoke")
routes to _render_via_atelier, which renders a project-local entry under
remotion-composer/projects/<slug>/ with an optional per-project public_dir
(skips copying the bloated shared public/). No cut-schema, no stock registry.
- skills/meta/bespoke-composition.md: routing skill — art direction
(visual-style) -> motion principles (Disney 12) -> engine mechanics
(remotion-best-practices + stock components read only as a mechanics codex)
-> atelier render. Doctrine: reuse engine knowledge, never creative components.
- AGENT_GUIDE: "Composition Authoring Mode" (templated vs atelier); default
atelier for hero work; scene-type catalog reframed as a mechanics codex.
- animation-runtime-selector + INDEX: authoring-mode-first pointers.
- base_tool.run_command: decode subprocess output as UTF-8/replace (Windows
cp1252 crashed the reader thread on Remotion's Unicode progress output).
- .gitignore: remotion-composer/projects/ (throwaway bespoke compositions).
The GSAP family README described HyperFrames as a 'future' engine ('if the
parallel HyperFrames engine gets wired in', 'becomes a day-1 skill', 'Future
hyperframes_compose tool — if added'). Every other artifact (AGENT_GUIDE.md,
skills/core/hyperframes.md, skills/INDEX.md) treats HyperFrames as a fully
production composition runtime, and hyperframes_compose is a registered tool.
Update the HyperFrames references to present-tense production reality. Also add
a scope note: this directory has no SKILL.md and is not a loadable skill — it
is a navigation map for the sibling gsap-* skills, which removes the ambiguity
about why it is not loadable via agent_skills[].
Closes#61
Three skill directories ship a long-form upstream AGENTS.md alongside their
loadable SKILL.md:
.agents/skills/flux-best-practices/AGENTS.md
.agents/skills/vercel-composition-patterns/AGENTS.md
.agents/skills/vercel-react-best-practices/AGENTS.md
None were referenced by their parent SKILL.md, skills/INDEX.md, AGENT_GUIDE.md,
or any pipeline manifest, leaving their authority scope undefined relative to
the repository-root AGENTS.md (override / extend / ignore?).
Reference each from its SKILL.md with explicit scope: the AGENTS.md is
supplementary upstream reference material for that skill only, SKILL.md is the
loadable entry point and the authority, and it does not override or extend the
root AGENTS.md / AGENT_GUIDE.md. Preserves the vendored content while removing
the ambiguity.
Refs #61
runway (estimate_cost/estimate_runtime/execute) defaulted to gen4_turbo and
higgsfield (execute) defaulted to kling_3.0, while both schemas advertise
seedance_2.0 as model.default. Omitting model under-quoted cost (runway 6x:
$0.25 vs $1.50) and silently generated a different model than advertised,
violating the Decision-Communication / cost-accuracy contract.
Root cause was a default duplicated across schema + 3 methods that drifted.
Collapse it to a single _DEFAULT_MODEL constant referenced everywhere.
Add tests/tools/test_provider_model_defaults.py to lock each tool's
estimate default to its schema default and guard the execute path.
The video-reference-analyst capability audit pre-locked Remotion as the
default composition engine ('Remotion is the default ... Never default to
FFmpeg when Remotion is available'). Because the analyst skill runs first
in any reference-led flow, this silently locked the runtime before the user
was ever offered a choice — violating the 'Present Both Composition Runtimes
(HARD RULE)' in AGENT_GUIDE.md, which forbids silently picking a default and
treats Remotion and HyperFrames as parallel, non-ranked runtimes.
It also omitted HyperFrames from the engine list entirely, so it was never
surfaced as an option in the reference-led path.
Update Step 2 (Capability Audit) to:
- add HyperFrames to the audited runtime list
- remove the 'Remotion is the default / preferred' framing
- defer engine selection to the AGENT_GUIDE 'Present Both' gate
- keep FFmpeg scoped to standalone ops, not composition
Closes#60
tools/video/green_screen_composite.py imports numpy at module level (and
declares "python:numpy" as a dependency), but numpy was missing from
requirements.txt. Because registry.discover() imports every tool module,
a fresh `make setup` followed by the mandatory preflight crashes with
ModuleNotFoundError: No module named 'numpy'. Add numpy>=1.24.
Also gitignore *.onnx/*.onnx.json so Piper TTS voice models fetched at
runtime (e.g. en_US-lessac-medium.onnx, ~60MB) aren't accidentally
committed to the repo root.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The repo had no .github issue or PR templates, so bug reports arrived
with inconsistent detail and questions landed on the tracker instead of
Discussions.
Add GitHub community templates:
- ISSUE_TEMPLATE/bug_report.yml: structured form (OS, pipeline, runtime,
repro, expected vs actual, logs)
- ISSUE_TEMPLATE/feature_request.yml: problem / solution / alternatives
- ISSUE_TEMPLATE/config.yml: disables blank issues and routes questions,
ideas, and show-and-tell to the existing Discussions categories
- PULL_REQUEST_TEMPLATE.md: summary, linked issue, testing, checklist
Additive only; no source changes.
Closes#189
Without a .gitattributes, line-ending normalization depends on each
contributor's local core.autocrlf. On Windows checkouts this can
materialize text files — and shell scripts like render-demo.sh — with
CRLF, which breaks script shebangs and produces noisy cross-platform
diffs.
Add a root .gitattributes that:
- defaults all text files to LF (* text=auto eol=lf)
- pins shell scripts, Makefile and .env.example to LF explicitly
- keeps .bat/.cmd as CRLF
- marks image/video/audio/font assets as binary so Git never
normalizes or diffs them
Only the policy file is added; existing files are intentionally left
unrenormalized to keep this change reviewable.
Closes#187
On Windows, passing --props and the JSON path as two separate CLI
arguments causes Remotion to mis-parse the value due to platform quote
escaping, failing with "neither valid JSON nor a file path to a valid
JSON file". Switch to the --props=<path> equals form, which Remotion
recommends for file paths and which works consistently across
platforms.
Fixes#172
- Add get_torch_device() helper in _shared.py: cuda > mps > cpu
- Guard MPS detection for torch builds lacking torch.backends.mps
- Check both is_built() and is_available() for MPS
- Route load_diffusers_pipeline() to resolved device instead of hardcoded cuda
- Use float32 on CPU (float16 is emulated/unreliable), float16 on MPS, bfloat16 on CUDA
- Guard enable_model_cpu_offload() to CUDA-only; fall back to .to(device) on MPS
- Enable attention slicing for MPS memory safety
- Add inspect-based signature guard for device= arg on RealESRGANer/GFPGANer
- Update install_instructions on all LOCAL_GPU tools to mention MPS/Apple Silicon
tests/qa/test_08_end_to_end.py runs at module import, so pytest fails
collection with CheckpointValidationError: the Stage 5 edit_decisions
fixture omits render_runtime, which edit_decisions.schema.json lists as
required. Per AGENT_GUIDE the runtime is locked at proposal and carried
through edit unchanged, so the fixture now sources it from the same
proposal_packet["production_plan"]["render_runtime"] ("remotion")
instead of hardcoding an unrelated value, keeping the fixture internally
consistent with the proposal it builds on.
Google's TTS and Imagen tools advertised service-account auth
(GOOGLE_APPLICATION_CREDENTIALS) but only ever authenticated with an API
key string, so users with a service-account JSON could not use either tool.
google_tts.get_status() also over-reported availability when the JSON was
set, then failed at execute() — a silent-availability bug.
Separately, both hand-rolled _load_dotenv parsers kept inline comments as
values, so after `cp .env.example .env` every keyed tool falsely reported
"available" with no real credentials.
Changes:
- Add tools/google_credentials.py: lazy google-auth Bearer-token helper.
- google_tts: authenticate via Cloud TTS Bearer token when only a service
account is configured; make get_status() honest.
- google_imagen: route service-account auth to Vertex AI
({location}-aiplatform.googleapis.com) with project/location resolution,
alongside the existing AI Studio API-key path.
- Fix both _load_dotenv parsers to strip inline comments (quote-aware).
- Add google-auth to requirements; document the new env vars in .env.example.
- .gitignore: never commit GCP service-account key files.
Verified locally with a real service account: TTS produced a valid MP3 and
Imagen produced a valid 1408x768 PNG via Vertex AI. Existing test suite
passes (2 unrelated pre-existing failures only).
Closes#131
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
ARCHITECTURE.md said `npx @hyperframes/cli` but that scoped package
is a 404. The published npm package is `hyperframes`, consumed as
`npx hyperframes`. hyperframes_compose.py already documents this
distinction in its _NPM_PACKAGE constant and install_instructions.
calesthio