GITHUB AGENT SKILLS / 技能收录
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我们从 GitHub 持续收录、审核并分类优质的 Agent Skills，按职业与使用场景整理，复制一行命令即可安装到 Claude Code、Cursor 等智能体。
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Microsoft
@microsoft
openclaw
@openclaw
OpenAI
@openai
The Apache Software Foundation
@apache
GitHub
@github
Anthropic
@anthropics
AutoGPT
@Significant-Gravitas
Garry Tan
@garrytan全部 Skills 196856
ort-ci
Trigger, re-run, and unblock ONNX Runtime CI checks on a GitHub pull request. Use this skill when a required check is stuck, missing, failed, or needs re-running. Nearly all ORT CI runs as GitHub Actions workflows; only the "Linux Android Emulator QNN CI Pipeline" remains on Azure Pipelines, plus the bot-driven "license/cla" and "Python format" lint checks. Triggers on: rerun CI, retrigger checks, stuck check, missing pipeline, failed CI, license/cla, Python format failure, Doc Gen CI failure, operator docs out of date, /azp run.
ort-build
Build ONNX Runtime from source. Use this skill when asked to build, compile, or generate CMake files for ONNX Runtime.
cuda-cutlass-fmha-incremental-rebuild
Use when rebuilding ONNX Runtime CUDA after editing CUTLASS fused-MHA headers (onnxruntime/contrib_ops/cuda/bert/cutlass_fmha/*.h such as kernel_forward.h or fmha_launch_template.h), or when a header edit "passed" an incremental build but test behavior did not change. Explains the nvcc depfile gotcha that produces stale Memory-Efficient-Attention (MEA) kernels and binaries, and how to force a correct recompile. Also covers disk-space frugality on shared GPU dev boxes.
cuda-attention-kernel-patterns
Patterns and pitfalls for the ONNX domain Attention operator (opset 23/24) CUDA implementation. Use when modifying the dispatch cascade in core/providers/cuda/llm/attention.cc, writing mask/bias CUDA kernels, debugging attention test routing, or adding features to the ONNX Attention op. NOT for contrib domain MultiHeadAttention/GroupQueryAttention.
contrib-op-shape-inference-memory-safety
Audit and fix out-of-range output writes in ONNX Runtime operator shape-inference functions. Use when reviewing or fixing a contrib (or standard) op TypeAndShapeInference where a getNumOutputs() guard precedes a write to a higher output index - optional trailing outputs make a smaller output count schema-valid, so getOutputType(index) can run one past the declared outputs at Graph::Resolve.
code-review
Review ONNX Runtime pull requests, branches, commits, patches, and working-tree changes for actionable findings and missing tests.
bigquery-graph
Skill for Graph Query Language (GQL) or SQL/PGQ queries against a property graph. Includes path finding, multi-hop traversal, topological connection, shortest path, node reachability, edge connectivity, and semantic graph queries.
bigquery-ai-ml
Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.
code-review-skill
Reviews code with feedback tailored to the developer's profile in session state.
weather-skill
A skill that provides weather information based on reference data.
weather-skill
A skill that provides weather information based on reference data and scripts.
weather-skill
A skill that provides weather information based on reference data.
text-skill
A text processing skill that formats strings using shell scripts.
calc-skill
A math calculation skill that runs Python scripts to perform arithmetic.
text-skill
A text processing skill that formats strings using shell scripts.
calc-skill
A math calculation skill that runs Python scripts to perform arithmetic.
adk-verify-snippets
Checks that every Python code block in a Markdown file actually compiles and runs, by extracting each block to a temporary file, executing it in an isolated subprocess, and writing a pass/fail report with per-snippet coverage. Use when the user asks to verify, test, or validate the code samples in a README, a guide, or a documentation page; wants to know which snippets in a Markdown file are broken or out of date; or asks for a snippet verification report. Don't use for running the project's test suite (run pytest directly), for checking code style or formatting (use `adk-style`), or for authoring a new runnable sample agent (use `adk-sample-creator`).
adk-unit-guide
Writes a hands-on developer guide for one ADK code unit — a minimal runnable example, how it works, a configuration-option table, advanced uses, limitations, and links to related samples — to `docs/guides/{topic}/{unit}/index.md`, then lists it in the index at `docs/guides/README.md`. Its reader is a developer calling the unit from their own application, at more depth than the published adk.dev documentation carries. Use when asked to "write a unit guide for {class}", "document how to use {feature}", "add a guide for {file}", or after shipping a user-facing class, node, or plugin. Don't use for internals documentation aimed at someone changing or extending the unit — that is a design document under `docs/design/` (use `adk-unit-design`). Don't use to write a runnable sample under `contributing/samples/` (use `adk-sample-creator`).