diff --git a/scientific-skills/markdown-mermaid-writing/SKILL.md b/scientific-skills/markdown-mermaid-writing/SKILL.md index 6b7d4c5..410ac99 100644 --- a/scientific-skills/markdown-mermaid-writing/SKILL.md +++ b/scientific-skills/markdown-mermaid-writing/SKILL.md @@ -1,13 +1,6 @@ --- name: markdown-mermaid-writing -description: > - Comprehensive markdown and Mermaid diagram writing skill that establishes text-based - diagrams as the DEFAULT documentation standard. Use this skill when creating ANY - scientific document, report, analysis, or visualization — it ensures all outputs are - in version-controlled, token-efficient markdown with embedded Mermaid diagrams as the - source of truth, with clear pathways to downstream Python or AI-generated images. - Includes full style guides (markdown + mermaid), 24 diagram type references, and - 9 document templates ready to use. +description: Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates. allowed-tools: Read Write Edit Bash license: Apache-2.0 metadata: diff --git a/scientific-skills/timesfm-forecasting/SKILL.md b/scientific-skills/timesfm-forecasting/SKILL.md index f912323..984dd18 100644 --- a/scientific-skills/timesfm-forecasting/SKILL.md +++ b/scientific-skills/timesfm-forecasting/SKILL.md @@ -1,14 +1,6 @@ --- name: timesfm-forecasting -description: > - Zero-shot time series forecasting with Google's TimesFM foundation model. Use this - skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, - energy demand, patient vitals, weather, or scientific measurements — without training a - custom model. Automatically checks system RAM/GPU before loading the model, supports - CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction - intervals. Includes a preflight system checker script that MUST be run before first use - to verify the machine can load the model. For classical statistical time series models - (ARIMA, SARIMAX, VAR) use statsmodels; for time series classification/clustering use aeon. +description: Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use. allowed-tools: Read Write Edit Bash license: Apache-2.0 license metadata: