skills/claude-scientific-skills
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dcde063723c06317b858d36e0d0c7ffa5fcc0892
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4 Commits

Author SHA1 Message Date
Clayton Young
dcde063723 chore: remove markdown-mermaid-writing skill from this branch 
This branch was originally created from feat/markdown-mermaid-writing-skill
for development purposes, but the timesfm-forecasting skill should be
independent of PR #50.

- Remove scientific-skills/markdown-mermaid-writing/ directory
- Remove reference to markdown-mermaid-writing from SKILL.md integration section
- This PR now stands alone and does not require PR #50 to be merged first
 2026-02-23 07:43:04 -05:00
Clayton Young
88300014e2 docs(skill): add note that model weights are not stored in repo 
Model weights (~800 MB) download on-demand from HuggingFace when skill
is first used. Preflight checker ensures sufficient resources before
any download begins.
 2026-02-23 07:43:04 -05:00
Clayton Young
c7c5bc21ff feat(example): add working TimesFM forecast example with global temperature data 
- Add NOAA GISTEMP global temperature anomaly dataset (36 months, 2022-2024)
- Run TimesFM 1.0 PyTorch forecast for 2025 (12-month horizon)
- Generate fan chart visualization with 80%/90% confidence intervals
- Create comprehensive markdown report with findings and API notes

API Discovery:
- TimesFM 2.5 PyTorch checkpoint has file format issue (model.safetensors
  vs expected torch_model.ckpt)
- Working API uses TimesFmHparams + TimesFmCheckpoint + TimesFm() constructor
- Documented API in GitHub README differs from actual pip package

Includes:
- temperature_anomaly.csv (input data)
- forecast_output.csv (point forecast + quantiles)
- forecast_output.json (machine-readable output)
- forecast_visualization.png (LFS-tracked)
- run_forecast.py (reusable script)
- visualize_forecast.py (chart generation)
- run_example.sh (one-click runner)
- README.md (full report with findings)
 2026-02-23 07:43:04 -05:00
Clayton Young
98670bcf47 feat(skill): add timesfm-forecasting skill for time series forecasting 
Add comprehensive TimesFM forecasting skill with mandatory system
preflight checks (RAM/GPU/disk), end-to-end CSV forecasting script,
full API reference, data preparation guide, and hardware requirements
documentation. Supports TimesFM 2.5 (200M), 2.0 (500M), and legacy
v1.0 with automatic batch size recommendations based on hardware.
 2026-02-23 07:43:04 -05:00