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Refactor descriptions in markdown-mermaid-writing and timesfm-forecasting skills for clarity and conciseness, removing redundant details while maintaining essential information.

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Timothy Kassis
2026-03-05 07:57:07 -08:00
parent cfffe4d166
commit e46b77d962
2 changed files with 2 additions and 17 deletions
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scientific-skills/markdown-mermaid-writing/SKILL.md
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---
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:

10
scientific-skills/timesfm-forecasting/SKILL.md
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---
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: