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.
>- (CLAUDE.md/AGENTS.md), authorized agent memory, and workspace residue with what the code and runtime actually do, so the next session or the next person starts from one current answer. Trigger when the user names "neat-freak", "洁癖", or "/neat" — and also on clear knowledge-closeout development ("把文档和记忆整理一下", "收尾时把文档同步掉", "docs 和代码对不上了"), stale or conflicting CLAUDE.md/memory, a clean handoff to a teammate or a fresh session, or auditing whether workspace rules are actually followed. Do not trigger for pure coding/refactoring/debugging tasks, tidying data or prose (JSON, 周报, changelog announcements), or a bare "整理" with no project-knowledge context.
> macOS / Windows 只读存储分析助手（自动识别系统）。扫描整机磁盘占用，找出 占空间大户，把每一项分成 🟢可自动清理 / 🟡需人工判断 / 🔴谨慎清理 三级并给出 可执行处置方案，生成排版精美、可折叠、命令可一键复制的交互式 HTML 报告，并可 起本地服务在网页上一键删除（移废纸篓/直接删）。扫描全程只读。务必在以下场景 使用：用户说"存储分析""磁盘满了""C盘/硬盘满了""空间不够""清理空间" "清理磁盘""占空间""哪些东西占地方""帮我看看存储""看一下电脑存储/空间" "存储空间""电脑空间不够""内存满了/不够/不足""看下内存/存储"（中文口语里 "内存"常指存储空间）"storage analysis""disk cleanup""清缓存""磁盘清理"； 或用户抱怨电脑没空间、想知道什么东西吃硬盘、想要清理建议时。注意：若用户明确 指运行内存/RAM（如"哪个进程吃内存""内存占用高"想看活动监视器），那是 RAM 不是存储，不属于本 skill。
> (1) Adding a brand-new syscall that currently returns ENOSYS or is missing from the table. (2) Adding missing flags/options to an existing partially-supported syscall (e.g., a new prctl option, ioctl command, or socket option). Use when asked to implement a syscall, add a flag, or improve compatibility for a specific syscall.
Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder; dense or static embedding model; for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker; pair scoring for two-stage retrieval / pair classification), and `SparseEncoder` (SPLADE, sparse embedding model; for learned-sparse retrieval). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.
Edit any video by conversation. Transcribe, cut, color grade, generate overlay animations, burn subtitles — for talking heads, montages, tutorials, travel, interviews. No presets, no menus. Ask questions, confirm the plan, execute, iterate, persist. Production-correctness rules are hard; everything else is artistic freedom.
Production pipeline for mathematical and technical animations using Manim Community Edition. Creates 3Blue1Brown-style explainer videos, algorithm visualizations, equation derivations, architecture diagrams, and data stories. Use when users request: animated explanations, math animations, concept visualizations, algorithm walkthroughs, technical explainers, 3Blue1Brown style videos, or any programmatic animation with geometric/mathematical content.
Write and run markdown-driven e2e tests for Relay. Covers fixture format, server/client code patterns, interaction DSL, snapshots, and running tests.
>- Performance best practices for Relay applications. Use when optimizing data fetching, reducing re-renders, configuring caching, or improving time to first meaningful paint. Covers query placement, @defer, pagination, fetch policies, garbage collection, fragment granularity, and server-side filtering. Companion to the relay-best-practices skill which covers correctness and architecture.
>- Best practices for writing idiomatic Relay code. ALWAYS use this skill when writing or modifying React components that use Relay for data fetching. Covers fragments, queries, mutations, pagination, and common anti-patterns. Use when you see `useFragment`, `useLazyLoadQuery`, `usePreloadedQuery`, `useMutation`, `usePaginationFragment`, `graphql` template literals, `react-relay` imports, or `__generated__/*.graphql` files. Also use when asked to explain Relay concepts, debug Relay issues, or review Relay code.
Complete API for Google NotebookLM - full programmatic access including features not in the web UI. Create notebooks, add sources, generate all artifact types, download in multiple formats. Activates on explicit /notebooklm or intent like "create a podcast about X
> Delegation mode for open-code-review (OCR). Instead of OCR calling an LLM endpoint, this skill instructs the host agent to perform the code review rule resolution. Use when the host agent should drive the review with its own LLM capabilities.
> Performs AI-powered code review on Git changes using the `ocr` CLI from alibaba/open-code-review. Use when the user asks to review code, review a pull request, review staged/unstaged changes, review a commit, or compare branches for code quality issues. Produces line-level review comments and can automatically apply fixes when requested. With appropriate review rules, can detect various types of issues including bugs, security vulnerabilities, performance problems, and code quality concerns.
> Delegation mode for open-code-review (OCR). Instead of OCR calling an LLM endpoint, this skill instructs the host agent to perform the code review rule resolution. Use when the host agent should drive the review with its own LLM capabilities.
> Performs AI-powered code review on Git changes using the `ocr` CLI from alibaba/open-code-review. Use when the user asks to review code, review a pull request, review staged/unstaged changes, review a commit, or compare branches for code quality issues. Produces line-level review comments and can automatically apply fixes when requested. With appropriate review rules, can detect various types of issues including bugs, security vulnerabilities, performance problems, and code quality concerns.
Bump the default Redis docker test image (redislabs/client-libs-test) in the shared DEFAULT_DOCKER_CONFIG and the CI matrix, then force-push the bump-test-image branch and open a PR against upstream. User-invoked only.
A comprehensive collection of Agent Skills for context engineering, harness engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, evaluating, or debugging agent systems that require effective context management and reliable operating loops.
This skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment.
This skill should be used when modeling agent mental states with BDI concepts: beliefs, desires, intentions, RDF-to-belief transformations, rational agency traces, cognitive agents, BDI ontologies, and neuro-symbolic AI integration.
This skill should be used when long-running agent sessions need context compression, structured summarization, compaction, token-per-task optimization, or durable handoff summaries that preserve decisions, files, risks, and next actions.
This skill should be used for diagnosing and mitigating context degradation: lost-in-middle failures, context poisoning, context clash, context confusion, attention-pattern issues, and agent performance degradation caused by accumulated or conflicting context.
This skill should be used to explain or reason about the foundational concepts of context engineering: what context is, the anatomy of a context window, how attention mechanics work, the U-shaped attention curve, why context quality matters more than quantity, and the mental models needed to interpret every other context-engineering decision. Use this for conceptual explanation, onboarding, and background reading. Route operational work to the specialized skills: debugging attention failures goes to context-degradation, token-efficiency work goes to context-optimization, conversation summarization goes to context-compression, and project-shape decisions go to project-development.
This skill should be used for improving context efficiency: context budgeting, observation masking, prefix or KV-cache strategy, partitioning, token-cost reduction, retrieval scoping, and extending effective context capacity without lowering answer quality.
This skill should be used when building agent evaluation systems: deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, baseline comparison, and outcome measurement for agent pipelines.
This skill should be used when agent work needs file-backed context: durable scratchpads, tool-output offloading, just-in-time discovery, cross-agent handoff files, filesystem memory, or cleanup policies for context stored outside the prompt.
This skill should be used when designing autonomous agent harnesses: research loops, evaluation scaffolds, locked and editable surfaces, durable logs, novelty gates, pruning, rollback, PR preparation, and human approval boundaries.
This skill should be used when designing hosted or background agent infrastructure: sandboxed execution, remote coding environments, warm pools, session persistence, multiplayer collaboration, self-spawning agents, or Modal-style sandboxes.
This skill should be used when the user asks to \"share memory between agents\", \"KV cache compaction for multi-agent\", \"orchestrator worker context\", \"latent briefing\", \"reduce worker tokens\", \"cross-agent memory without summarization\", or discusses Attention Matching compaction, recursive language models with workers, or token explosion in hierarchical agents.
This skill should be used when writing, enhancing, or evaluating the launch prompt for a long-running autonomous agent or a parallel multi-agent orchestration attacking a hard problem: pseudo-formal task briefs that define terms and an exact success predicate linguistically, enumerate non-counting outcomes, set persistence rules with explicit stop and return conditions and effort floors, manage a diverse portfolio of parallel approaches with an approach registry and blocked-route bookkeeping, and gate the return on adversarial audit. Route agent topology and coordination protocols to multi-agent-patterns, runtime control surfaces and loop governance to harness-engineering, evaluator and quality-gate construction to evaluation, judge design to advanced-evaluation, and compaction or memory mechanics to context-compression and memory-systems.
This skill should be used for persistent semantic memory in agent systems: cross-session knowledge retention, entity tracking, temporal validity, graph or vector retrieval, memory consolidation, and memory benchmark selection. Route file-backed scratchpads to filesystem-context, handoff summaries to context-compression, and token-efficiency tactics to context-optimization.
This skill should be used when designing multi-agent systems that need context isolation, supervisor or swarm coordination, explicit handoffs, parallel execution, or a decision on whether multiple agents are justified.
This skill should be used for project-level decisions about LLM-powered systems: whether an LLM is the right primitive for the task at hand, the shape of a multi-stage batch or agent pipeline, token and cost estimation, choosing between single-agent and multi-agent at the project level, structured output design for downstream parsing, and structuring agent-assisted iteration. Use this when the unit of work is a whole project or a multi-stage pipeline. Route individual tool design to tool-design and individual skill-loading or context-budget tactics to context-optimization.
This skill should be used when the harness, scaffold, workflow, or optimizer itself is the optimization target: recursive self-improvement (RSI) loops, meta-harnesses, self-improving harnesses that mine their own failures and propose bounded edits, evolutionary or population-based search over agent scaffolds, acceptance gates for self-modifying systems, and agentic context evolution where the mechanism that produces context is versioned and evolved. Route governance of a single autonomous loop (locked surfaces, durable logs, rollback, novelty gates, approval boundaries) to harness-engineering, measurement and quality-gate design to evaluation, judge design to advanced-evaluation, and remote sandbox infrastructure to hosted-agents.
This skill should be used for the tool-interface layer of an agent system specifically: writing tool descriptions agents can route on, designing tool schemas and response formats, naming conventions, actionable error recovery messages, MCP server design, tool-set consolidation, and deciding when to add or remove an individual tool. Use this when the unit of work is a single tool or a set of tools. Route project-shape, pipeline architecture, and task-model-fit decisions to project-development; route deciding whether to introduce sub-agents to multi-agent-patterns.
Guide a new user through their first computer-use agent run (env check, safe browser-only task, then open the trajectory viewer).
Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.
Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.
How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.
Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.
Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.
Automate React Native Windows integration with upstream React Native nightly versions. Use when: upgrading RNW to a newer React Native nightly, finding target nightly versions, preparing integration PRs, updating package dependencies.
Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.
Migration guide for moving Megatron Core GPTModel checkpoints, model providers, training commands, and layer mappings to HybridModel.
Bump the NVIDIA PyTorch base image (`nvcr.io/nvidia/pytorch:YY.MM-py3`) used by Megatron-LM CI. Covers the two pin sites (GitHub CI in `docker/.ngc_version.dev` and GitLab CI in `.gitlab/stages/01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs #4611 and #4688.
CI/CD reference for Megatron-LM. Covers CI pipeline structure, PR scope labels, triggering internal GitLab CI (which force-pushes the current branch to a pull-request/BRANCH ref — always dry-run and verify the destination first; never run against shared or protected branches), and CI failure investigation.
Container-based dev environment setup and dependency management for Megatron-LM. Covers acquiring and launching the CI container, uv package management, and updating uv.lock.
Research and draft a response to a GitHub issue or question from an external contributor.
Domain knowledge for the nightly main-to-dev sync workflow. Covers merge strategy, CI architecture, failure investigation, and known issues.
Refresh golden values from a GitHub Actions workflow run (failing-only or all jobs), score the change with average normalized relative differences, and produce a PR-ready summary. Use when the user asks to update goldens for a CI run, refresh golden values from a workflow ID, or generate a golden-value diff summary for a PR description.
Operate FrontendExtension (FE) resources in frontend-forge: create, update, rebuild, inspect package artifacts, download packages, publish, unpublish, delete, and debug package/publish controller behavior. Use this skill whenever the user mentions FE operations, FrontendExtension lifecycle, extension package/download/publish/unpublish, artifact ConfigMaps, package Jobs, publisher Jobs, publish target ConfigMaps or Secrets, rebuild-token, package-state/publish-state labels, or troubleshooting FE status in a Kubernetes cluster.
Operate FrontendIntegration resources and the frontend-forge extension. Use when Codex needs to create a FrontendIntegration from FrontendIntegration YAML, update or patch FI lifecycle state, inspect or troubleshoot FI build output, or create, enable, disable, uninstall, and inspect the frontend-forge extension through its InstallPlan and extension resources.
Generate canonical FrontendIntegration YAML from a simplified single-menu authoring model for frontend-forge.
Use when deploying KubeEye for cluster inspection, creating InspectRule/InspectPlan resources, or retrieving inspection results. Covers InstallPlan-based deployment, OPA/PromQL/FileChange/Sysctl/Systemd/NodeInfo/FileFilter/ServiceConnect/CustomCommand rule types, and report retrieval. Always consult this skill when the user mentions KubeEye, cluster inspection, InspectRule, InspectPlan, or inspection reports.
KubeSphere cluster query Skill (read-only). Use when user requests to view cluster list, cluster status, cluster details, or cluster version info. Do not perform any write operations (create, delete, update). Use KubeSphere Console for management operations.
KubeSphere central controller Skill. Routes to specific Skills based on user requests: multi-cluster management (kubesphere-cluster-management), multi-tenant management (kubesphere-multi-tenant-management), extension management (kubesphere-extension-management). Also provides core architecture, API routing, and API utilities.
Use when configuring ArgoCD in KubeSphere DevOps, including GitOps deployments, application management, SSO setup, or troubleshooting ArgoCD issues
Use when managing credentials in KubeSphere DevOps, including repository credentials, kubeconfig, and API tokens
Use when configuring Jenkins in KubeSphere DevOps, including agent customization, LDAP/OIDC integration, build artifact retrieval, or troubleshooting Jenkins issues
Use when working with KubeSphere DevOps extension, CI/CD pipelines, Jenkins integration, or pipeline troubleshooting
Use when creating, running, or managing CI/CD pipelines in KubeSphere DevOps, including pipeline API operations and run monitoring
Claude Skills — questions
Answers built from the skills we actually parsed.