Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
中文简介正在准备中。当前显示原文说明。
Analyze failed GitHub Action jobs for a pull request.
中文简介正在准备中。当前显示原文说明。
安装前可以查看交给 AI 代理的指令内容。
This skill analyzes logs from failed GitHub Action jobs using Claude.
gh auth token, or set GITHUB_TOKEN env var# Analyze all failed jobs in a PR
uv run skills analyze-ci <pr_url>
# Analyze specific job URLs directly
uv run skills analyze-ci <job_url> [job_url ...]
# Show debug info (tokens and costs)
uv run skills analyze-ci <pr_url> --debug
Output: A concise failure summary with root cause, error messages, test names, and relevant log snippets.
# Analyze CI failures for a PR
uv run skills analyze-ci https://github.com/mlflow/mlflow/pull/19601
# Analyze specific job URLs directly
uv run skills analyze-ci https://github.com/mlflow/mlflow/actions/runs/12345/job/67890
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Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
中文简介正在准备中。当前显示原文说明。
AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching `acopf-math-model.md` and MATPOWER branch fields. Use when computing branch flows in either direction, aggregating bus injections for nodal balance, checking MVA (rateA) limits, computing branch loading %, or debugging sign/units issues in AC power flow.
中文简介正在准备中。当前显示原文说明。
Redact text from PDF documents for blind review anonymization
中文简介正在准备中。当前显示原文说明。
Use when checking simplified ADA-derived plan-view bathroom accessibility constraints such as turning space, door clear width, toilet centerline, grab bars, and lavatory knee/toe clearance.
中文简介正在准备中。当前显示原文说明。
Use when extracting plan-view architectural geometry from DXF files with semantic CAD layers, especially when outputs must normalize rooms, doors, fixtures, clearances, and grab bars into machine-checkable JSON.
中文简介正在准备中。当前显示原文说明。
Implement paper-defined attention variants by extracting mechanism invariants, mapping tensor shapes, preserving module interfaces, validating numerical behavior, and integrating the custom attention into an existing transformer stack.
中文简介正在准备中。当前显示原文说明。