feat(skill): add timesfm-forecasting Agent Skill (agentskills.io)

Add a self-contained AI agent skill for TimesFM that teaches coding
agents (Claude Code, OpenCode, Cursor, Codex) how to use the TimesFM
API correctly — safe model loading, zero-shot forecasting, covariate
workflows, anomaly detection, and the most common pitfalls.

Files added:
- AGENTS.md             — auto-loaded skill document (root of repo)
- claude-skill/scripts/check_system.py    — mandatory preflight RAM/GPU/disk checker
- claude-skill/scripts/forecast_csv.py   — CLI wrapper for CSV forecasting
- claude-skill/references/               — ForecastConfig API ref, data prep, HW reqs
- claude-skill/examples/global-temperature/   — basic forecast + PNG/GIF pipeline
- claude-skill/examples/anomaly-detection/    — two-phase detrend+Z-score + quantile PI
- claude-skill/examples/covariates-forecasting/ — forecast_with_covariates() XReg demo
- .gitattributes        — Git LFS rules for PNG/GIF binary outputs

Contributed by Clayton Young / Superior Byte Works LLC (@borealBytes)
Apache 2.0 — same license as this repository

claude-skill/examples/global-temperature/visualize_forecast.py

@@ -0,0 +1,123 @@
+#!/usr/bin/env python3
+"""
+Visualize TimesFM forecast results for global temperature anomaly.
+
+Generates a publication-quality figure showing:
+- Historical data (2022-2024)
+- Point forecast (2025)
+- 80% and 90% confidence intervals (fan chart)
+
+Usage:
+    python visualize_forecast.py
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+# Configuration
+EXAMPLE_DIR = Path(__file__).parent
+INPUT_FILE = EXAMPLE_DIR / "temperature_anomaly.csv"
+FORECAST_FILE = EXAMPLE_DIR / "output" / "forecast_output.json"
+OUTPUT_FILE = EXAMPLE_DIR / "output" / "forecast_visualization.png"
+
+
+def main() -> None:
+    # Load historical data
+    df = pd.read_csv(INPUT_FILE, parse_dates=["date"])
+
+    # Load forecast results
+    with open(FORECAST_FILE) as f:
+        forecast = json.load(f)
+
+    # Extract forecast data
+    dates = pd.to_datetime(forecast["forecast"]["dates"])
+    point = np.array(forecast["forecast"]["point"])
+    q10 = np.array(forecast["forecast"]["quantiles"]["10%"])
+    q20 = np.array(forecast["forecast"]["quantiles"]["20%"])
+    q80 = np.array(forecast["forecast"]["quantiles"]["80%"])
+    q90 = np.array(forecast["forecast"]["quantiles"]["90%"])
+
+    # Create figure
+    fig, ax = plt.subplots(figsize=(12, 6))
+
+    # Plot historical data
+    ax.plot(
+        df["date"],
+        df["anomaly_c"],
+        color="#2563eb",
+        linewidth=1.5,
+        marker="o",
+        markersize=3,
+        label="Historical (NOAA GISTEMP)",
+    )
+
+    # Plot 90% CI (outer band)
+    ax.fill_between(dates, q10, q90, alpha=0.2, color="#dc2626", label="90% CI")
+
+    # Plot 80% CI (inner band)
+    ax.fill_between(dates, q20, q80, alpha=0.3, color="#dc2626", label="80% CI")
+
+    # Plot point forecast
+    ax.plot(
+        dates,
+        point,
+        color="#dc2626",
+        linewidth=2,
+        marker="s",
+        markersize=4,
+        label="TimesFM Forecast",
+    )
+
+    # Add vertical line at forecast boundary
+    ax.axvline(
+        x=df["date"].max(), color="#6b7280", linestyle="--", linewidth=1, alpha=0.7
+    )
+
+    # Formatting
+    ax.set_xlabel("Date", fontsize=12)
+    ax.set_ylabel("Temperature Anomaly (°C)", fontsize=12)
+    ax.set_title(
+        "TimesFM Zero-Shot Forecast Example\n36-month Temperature Anomaly → 12-month Forecast",
+        fontsize=14,
+        fontweight="bold",
+    )
+
+    # Add annotations
+    ax.annotate(
+        f"Mean forecast: {forecast['summary']['forecast_mean_c']:.2f}°C\n"
+        f"vs 2024: {forecast['summary']['vs_last_year_mean']:+.2f}°C",
+        xy=(dates[6], point[6]),
+        xytext=(dates[6], point[6] + 0.15),
+        fontsize=10,
+        arrowprops=dict(arrowstyle="->", color="#6b7280", lw=1),
+        bbox=dict(boxstyle="round,pad=0.3", facecolor="white", edgecolor="#6b7280"),
+    )
+
+    # Grid and legend
+    ax.grid(True, alpha=0.3)
+    ax.legend(loc="upper left", fontsize=10)
+
+    # Set y-axis limits
+    ax.set_ylim(0.7, 1.5)
+
+    # Rotate x-axis labels
+    plt.xticks(rotation=45, ha="right")
+
+    # Tight layout
+    plt.tight_layout()
+
+    # Save
+    fig.savefig(OUTPUT_FILE, dpi=150, bbox_inches="tight")
+    print(f"✅ Saved visualization to: {OUTPUT_FILE}")
+
+    plt.close()
+
+
+if __name__ == "__main__":
+    main()