Claude Skills
The open format is called Agent Skills and works in Claude Code, Codex, Cursor and other agents — most people know it as Claude Skills.
Every Agent Skill we could find on GitHub, deduplicated by content. 86 961 files from 2 125 authors, of which 68 678 are unique — the rest is the same skill repackaged into someone else's repository. For each one: what it weighs in tokens, whether it ships runnable scripts, and which MCP servers it needs.
Author, validate, and debug canvas extensions that the agent can open in the GitHub Copilot app's side panel. Use when creating, reviewing, or troubleshooting a canvas extension — including its actions, inputs, rendered content, and the surrounding extension wiring (tools, hooks, lifecycle).
Upgrade gh-aw to latest gh-aw-firewall release and identify follow-up spec tasks.
Internal gh-aw architecture: validation system design, safe output message patterns, schema validation, YAML compatibility notes, and MCP logs guardrail.
Release management, changeset workflow, firewall log parsing, breaking CLI change rules, and Go module summaries for gh-aw.
Code organization patterns, file structure guidelines, WASM build variants, and string processing conventions for gh-aw Go code.
Write consistent, actionable validation error messages in gh-aw.
Core developer rules and coding conventions for gh-aw changes.
Write concise Diataxis docs for gh-aw with Starlight markdown conventions.
Security best practices for gh-aw workflows and Go code: template injection prevention, shell script security, supply chain hardening, and static analysis integration.
Design gh-aw error handling, retry, recovery, and debugging flows.
Apply safe error-pattern matching rules for agentic engines.
Use gh agent-session commands to inspect and manage agent sessions.
Use gh agent-task commands to create, run, and monitor agent tasks.
Query GitHub issues with jq filtering and reusable selectors.
List GitHub repository labels with per_page pagination support.
Query GitHub discussions with jq filtering and reusable selectors.
Practical tips for reviewing and improving Copilot agent PRs.
Reference for GitHub MCP server tools, methods, and usage patterns.
Query GitHub pull requests with jq filtering and reusable selectors.
List GitHub Actions workflows with per_page pagination support.
Add and validate custom Go analysis linters in gh-aw.
Implement secret-safe HTTP headers for MCP transport in gh-aw.
Implement and test Go codemods for the gh aw fix command.
Write robust JavaScript for GitHub Actions github-script steps.
Split large JavaScript files into maintainable modules safely.
Infer JSON structure and types with jq-based schema discovery.
Analyze and reduce token consumption in agentic workflows — guardrail-specific entry points, measurement, and optimization techniques.
Add new safe-output message types and wire validation/rendering.
Analyze gh-aw OpenTelemetry traces from JSONL mirrors or OTLP backends.
Rewrite prompts for minimal tokens, maximal clarity, and low ambiguity for LLM consumption.
Generate a new pkg/linters analyzer from a merged or open PR pattern.
Format reports with HTML details/summary blocks for readable output.
Merge a base ref and safely regenerate compiled workflow lock-file conflicts.
Run and integrate Skillz MCP server with Docker for skill execution.
Add temporary ID support to safe-output jobs end to end.
Normalize SKILL.md artifacts into Scheduling-Structural-Logical (SSL) JSON representations using a conservative multi-pass extraction pipeline.
GitHub Agentic Workflows (`gh-aw`) is a GitHub CLI extension for writing Agentic Workflows in markdown and compiling them to GitHub Actions.
Write clear GitHub Actions step summaries with progressive disclosure.
Build, test, or develop the QNN (Qualcomm AI Engine Direct) backend. Use when working on backends/qualcomm/, building QNN (use backends/qualcomm/scripts/build.sh), adding new ops or passes, running QNN delegate tests, or exporting models for Qualcomm HTP/GPU targets. Also exposes a Buck-vs-CMake parity workflow — invoke as `/qualcomm buck-fix`, `/qualcomm buck-cmake fix`, `/qualcomm buck-parity`, or any user request to fix `test-qnn-buck-build-linux` CI failures or check buck/cmake drift in backends/qualcomm/. Also covers QNN intermediate-output / per-layer accuracy debugging — trigger on phrases like "QNN accuracy issue", "QNN output doesn't match CPU", "debug per-layer for QNN", "find which QNN layer is wrong".
Profile ExecuTorch model execution. Use when measuring performance, analyzing operator timing, or debugging slow models.
Export a PyTorch model to .pte format for ExecuTorch. Use when converting models, lowering to edge, or generating .pte files.
Set up ExecuTorch development environment. Use when installing dependencies, setting up conda environments, or preparing to develop with ExecuTorch.
Build, test, or develop the Cortex-M (CMSIS-NN) backend. Use when working on backends/cortex_m/, running Cortex-M tests, or exporting models for Cortex-M targets.
Search the ExecuTorch tribal knowledge base covering QNN, XNNPACK, Vulkan, CoreML, Arm, and Cadence backends, quantization recipes, export pitfalls, runtime errors, and SoC compatibility. Use when debugging ExecuTorch errors, choosing quantization configs, checking backend op support, or answering questions about Qualcomm HTP / Snapdragon / Apple Neural Engine behavior.
Analyze and reduce ExecuTorch binary size. Use when investigating binary size, running size tests, or optimizing the runtime for size-constrained deployments.
Build ExecuTorch from source — Python package, C++ runtime, runners, cross-compilation, and backend-specific builds. Use when compiling anything in the ExecuTorch repo, diagnosing build failures, or setting up platform-specific builds.
Build and configure ExecuTorch as a Zephyr RTOS module for embedded boards. Use when setting up a Zephyr workspace with ET, adding board support (overlays, confs, memory layout), building with west, or debugging linker memory overflow.
Excel 数据分析多步编排器。覆盖：(1) 读取多 Sheet Excel 文件并统计行数，(2) 大文件检测（≥10k 行自动 Parquet 优化），(3) 数据清洗（缺失值、文本标准化、无效字符），(4) 条件筛选与分类提取，(5) 跨 Sheet 统计聚合，(6) 导出 Excel/CSV 并提供下载链接。覆盖从数据读取到报告生成全流程，按步骤编排 capability 子 skill。**遇到以下任一情况就主动使用本 skill，不要自行写几行 pandas 就回答**：①用户出现触发词：Excel 分析 / 表格分析 / 数据分析 / 数据清洗 / 数据统计 / 数据筛选 / 数据可视化 / 数据导出 / 汇总统计 / 透视表 / 分组统计 / 交叉分析 / 趋势分析 / 对比分析 / 异常值检测 / 去重 / 缺失值处理 / Excel 报告 / 生成报表 / analyze Excel / data analysis / data cleaning / pivot table；②用户上传或指定了 .xlsx / .xls / .csv 文件并要求分析、清洗、统计或可视化；③任务涉及多 Sheet 读取、条件筛选、分类汇总、图表生成中的任意一项；④用户要求导出带格式的 Excel 报告或下载链接。仅不用于：不涉及表格数据的纯文本处理、图片分析（使用 sn-da-image-caption）、单个公式计算的简单问答。
当Excel文件总行数超过1万行时，通过转换为Parquet格式提升读取性能，提取目标指标并计算最大值，最后将结果输出为Excel并对特定行进行高亮标注。
对比Excel多表中的特定系数并对异常值进行颜色标记。
识别 Excel 中的超限数值与错误单元格并进行高亮标注。
根据Excel总行数自动切换Parquet加速读取，计算特定维度的时间序列平均值，并使用openpyxl输出带有条件格式（如低于均值标绿）和自定义样式的分析报告。
根据数据规模动态选择处理策略，对多表数据进行合并、统计筛选，并利用 openpyxl 实现关键指标的自动化样式高亮与格式化导出。
从带单位的字符串列中提取数值并清洗，生成包含直方图、饼图、条形图和累积分布图的多维度综合分布可视化图表，用于展示数据的集中趋势与分布特征。
对两类分类数据进行对比分析，统计数量差异与比例关系并生成可视化图表。
对多 Sheet 的 Excel 文件进行行数统计、大文件 Parquet 转换预处理、数据清洗及分组聚合分析，并生成带样式标记的统计表与可视化图表。
根据数据量自动选择读取策略（大文件转Parquet），提取关键指标进行单位一致性验证与排序分析，并输出可下载的结果表格。
利用交叉表与热力图对分类数据进行多维度占比分析，适用于奖项分布、绩效评估或市场占有率等结构化数据的清洗与可视化。
对时间序列或分类数据进行多维度趋势分析、百分比清洗、绩效分级建模与预测，并生成高分辨率的可视化综合报告，适用于业务指标监控与预测场景。
基于多维度数据进行分级评估与趋势预测，通过设定差异化增长率计算预测值，并生成对比可视化图表，适用于绩效评估、目标设定等场景。
Claude Skills — questions
Answers built from the skills we actually parsed.