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. 79 437 files from 1 744 authors, of which 61 785 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.
> deadlines, values, and common anti-patterns. "context.WithTimeout", "context.WithCancel", "context values", "context propagation". HTTP middleware context (use go-api-design), or error handling (use go-error-handling).
> Example functions, deprecation notices, and doc links. "add examples to docs", "deprecate a function", "package documentation", "improve the docs". guides (plain writing task), or code style rules (use go-coding-standards).
> semantics, aliasing, preallocation), maps (comma-ok, sets, iteration order), arrays, and choosing between them. "map iteration", "use a set in Go", "slice aliasing", "append gotcha", "copy a slice", "sync.Map or mutex". (use go-concurrency-review), allocation profiling (use go-performance-review), or generic container design (use go-design-patterns).
> Go error handling patterns, wrapping, sentinel errors, custom error types, and the errors package. Grounded in Effective Go, Go Code Review Comments, and production-proven idioms. Use when implementing error handling, designing error types, debugging error chains, or reviewing error handling patterns. "sentinel errors", "errors.Is", "errors.As". Do NOT use for panic/recover patterns in middleware (use go-api-design) or test assertion errors (use go-test-quality).
> Modernize Go code to use current language features and standard library additions. Covers generics, log/slog, errors.Join, slices/maps packages, range-over-func, and iterators introduced in Go 1.21-1.23+. "upgrade Go version", "slog", "errors.Join", "range over func", "iterators". error handling philosophy (use go-error-handling), or logging architecture (use go-observability).
> sync primitives, context propagation, and goroutine lifecycle management. Use when writing concurrent code, reviewing async patterns, checking thread safety, debugging race conditions, or designing producer/consumer pipelines. "channel patterns", "sync.Mutex", "context cancellation", "goroutine leak". Do NOT use for general code style (use go-coding-standards) or HTTP handler patterns (use go-api-design).
> migrations, query builders, and ORM usage (sqlc, GORM, ent). "database migration", "sqlc", "GORM", "ent", "prepared statement", "repository pattern". security aspects of SQL (use go-security-audit), or performance profiling of queries (use go-performance-review).
> Structured logging, distributed tracing, metrics, and health checks for Go services. Covers slog, OpenTelemetry, Prometheus, and observability best practices. "add metrics", "Prometheus", "observability", "instrument this code". error handling patterns (use go-error-handling), or health check endpoints (use go-api-design).
> goroutine leaks, memory leaks, OOM kills, race reports, and debugging with delve and pprof. leak", "goroutine count growing", "OOM", "program hangs", "race detector output", "use delve". writing new concurrent code (use go-concurrency-review), or failing test design (use go-test-quality).
> authentication/authorization, secrets management, TLS, OWASP Top 10, and secure coding patterns. Use when performing security reviews, checking for vulnerabilities, hardening Go services, or reviewing auth implementations. "SQL injection", "input validation", "secrets management", "auth review". Do NOT use for dependency CVE scanning (use go-dependency-audit) or concurrency safety (use go-concurrency-review).
> Detect performance anti-patterns and apply optimization techniques in Go. Covers allocations, string handling, slice/map preallocation, sync.Pool, benchmarking, and profiling with pprof. Use when checking performance, finding slow code, reducing allocations, profiling, or reviewing hot paths. "benchmark this", "profile", "optimize Go code". Do NOT use for concurrency correctness (use go-concurrency-review) or general code style (use go-coding-standards).
> struct design, subtest naming, advanced patterns like test matrices and shared setup, and refactoring bloated tables into clean ones. Use when writing table-driven tests, refactoring test tables, reviewing table test structure, or deciding whether table-driven is the right approach. "test matrix", "parametrize tests", "data-driven test", "refactor test table". Do NOT use for general test strategy, mocking, golden files, or fuzz testing (use go-test-quality). Do NOT use for benchmarks (use go-performance-review).
> golden files, httptest, testcontainers, property-based testing, and fuzz testing. Covers mocking strategies, test isolation, coverage analysis, and test design philosophy. Use when writing tests, improving coverage, reviewing test quality, setting up test infrastructure, or choosing a testing approach. "test helpers", "mock this dependency", "integration test", "fuzz test". Do NOT use for performance benchmarking methodology (use go-performance-review), security testing (use go-security-audit), or table-driven test patterns specifically (use go-test-table-driven).
> Structured git commit messages following Conventional Commits format for Go projects. Generates well-scoped, atomic commits with clear descriptions. Use when committing changes, writing commit messages, preparing PRs, or reviewing commit history quality. "prepare PR", "squash commits". Do NOT use for changelog generation (use changelog-generator) or code review (use go-code-review).
> golangci-lint setup, test/coverage gates, vulnerability scanning, build matrices, and Makefile targets. "CI is slow", "coverage gate", "build matrix", "write a Makefile", "run govulncheck in CI". auditing dependencies (use go-dependency-audit), or writing the tests themselves (use go-test-quality).
> vulnerabilities, review go.mod hygiene, identify unused or redundant deps, and evaluate dependency quality. Use when auditing dependencies, checking for CVEs, cleaning up go.mod, upgrading modules, or evaluating third-party packages. "update modules", "vulnerability scan", "govulncheck". Do NOT use for code-level security issues (use go-security-audit) or architecture review (use go-architecture-review).
> renames, extracting packages, breaking circular dependencies, and strangler migrations — always behind green tests. "break this circular dependency", "split this god package", "migrate callers", "clean up without changing behavior". adopting new language features (use go-modernize), or performance rewrites (use go-performance-review).
> Navigate and analyze Go codebases semantically with the toolchain instead of go list for the dependency graph, and go doc for APIs. used", "trace the dependency graph", "explore this codebase", "find usages before changing", "map the module". judging the architecture found (use go-architecture-review), or documentation writing (use go-documentation).
DEFAULT for all research and web queries. Use for any lookup, research, investigation, or question needing current info. Fast and cost-effective. Only use parallel-deep-research if user explicitly requests 'deep' or 'exhaustive' research.
URL content extraction. Use for fetching any URL - webpages, articles, PDFs, JavaScript-heavy sites. Token-efficient: runs in forked context. Prefer over built-in WebFetch.
ONLY use when user explicitly says 'deep research', 'exhaustive', 'comprehensive report', or 'thorough investigation'. Slower and more expensive than parallel-web-search. For normal research/lookup requests, use parallel-web-search instead. Supports multi-turn: pass --previous-interaction-id from a prior research or enrichment to continue with context.
Set up and maintain the Parallel CLI (install, auth, balance, skills install)
Migrate Exa, Tavily, Perplexity, or Firecrawl web-data integrations completely to the appropriate Parallel products while preserving application behavior. Use when replacing these providers' SDKs or REST calls, dependencies, environment variables, request parameters, response parsing, model tools, search-plus-scrape paths, full-content or answer-synthesis paths, tests, and documentation; separating unsupported research-index, crawl, browser, file-parse, monitor, or other non-search capabilities; auditing for leftover provider usage; or finishing and verifying an in-progress provider migration.
Discover entities (companies, people, products, etc.) matching a natural-language description. Use when the user asks to 'find all X' or 'list every Y that…' — e.g., 'Find AI startups that raised Series A in 2026', 'List roofing companies in Charlotte NC', 'Show me YC W24 dev tools companies'. Different from web-search (which returns webpages) and deep-research (which returns a narrative report). Use this when the user wants a structured list of entities.
Bulk data enrichment. Adds web-sourced fields (CEO names, funding, contact info) to lists of companies, people, or products. Use for enriching CSV files or inline data. Supports multi-turn: pass --previous-interaction-id from a prior research task to carry context forward.
Continuously track the web for changes on a recurring cadence. Use when the user asks to 'monitor', 'track changes to', 'watch', or 'alert me when' something on the web changes — e.g., 'Track price changes for iPhone 16', 'Alert me when Tesla files a new 8-K', 'Monitor competitor pricing pages weekly'. Also use to list, inspect, update, or stop existing monitors, including requests to delete them.
Get completed research task result by run ID
Check running research task status by run ID
Audits and optimizes websites for search engine visibility (SEO) and AI search citation (GEO), covering technical health, E-E-A-T content scoring, domain authority, structured data, rich results, and entity signals. Use when running SEO audits, diagnosing traffic drops or ranking losses, generating Schema.org JSON-LD, checking Core Web Vitals, crawlability, robots.txt, sitemaps, hreflang, backlinks, planning content strategy or site migrations, fixing indexing issues, or optimizing for AI Overviews, ChatGPT, and Perplexity. NOT for paid ads (PPC/SEM), social media strategy, email marketing, or general web development unrelated to search.
Batch download academic paper full-text (PDF/XML) from a list of DOIs. Handles 25 DOI prefixes across 19 publisher families via three layered routes: (1) publisher TDM APIs requiring institutional subscription (Elsevier ScienceDirect, Wiley Online, Springer Nature), (2) Open Access sources (Crossref, Unpaywall, OpenAlex), and (3) a browser-based fallback for paywalled publishers without TDM access (ACS, RSC, IEEE, AIP, IOP, APS, Annual Reviews, T&F, Chinese journals). Browser fallback offers two routes — Route A drives the user's logged-in Chrome via the OpenClaw `browser` tool with `profile="user"` (best for interactive sessions), Route B uses the standalone `auto-paper-harvester` CLI with its built-in Playwright (best for unattended bulk runs). Use when the user wants to harvest, scrape, fetch, or bulk-download papers from a DOI list, savedrecs export, or Excel; or wants to fill missing full-text PDFs for an existing literature collection. Triggers on phrases like "批量下载文献", "下载全文", "harvest papers", "scrape full text", "TDM API", "下载 Elsevier 全文", "Wiley 批量下载", "下载 PDF".
> Manages a Zotero academic reference library through both the local API using a structured collection hierarchy (Inbox / Active Projects / Background / Reading Queue / Archive / Meta) plus project, status, priority, and type tags. Handles adding papers with full metadata, deduplication, attaching provenance notes, moving items between collections, updating tags after reading, listing the prioritized reading queue, and setting up the literature scaffold for a new project. Use when the user asks to add / save / file / organize a paper in Zotero, check / list / clean up the reading queue, move papers between collections, tag papers for a project, query their library ("what do I have on X?"), or set up Zotero for a new research project.
> Conducts a systematic related-work / literature-survey / state-of-the-art review for a research question by defining survey dimensions, searching each axis, building a taxonomy of prior work, identifying the gap, and producing a positioning narrative for a paper's Related Work section. Goes beyond a flat paper list to deliver structured analysis. Use when the user is starting a new research project and needs to map the landscape, asks "what's been done on X?" or "how does my idea compare to existing work?", needs to write or revise a Related Work / Background / Prior Art section, wants to identify a research gap or position their contribution, or asks to build a taxonomy of approaches in a research area.
> Reads and analyzes academic papers (arXiv preprints, conference / journal standard read (10 min), or deep analysis (30 min). Produces structured digests covering problem, method, key innovation, results, limitations, reproducibility, hidden assumptions, and connections to the user's other work. Use when the user shares an arXiv link, PDF, or paper title and asks to read / summarize / digest / TL;DR / analyze / review / critique / explain / break down a paper, asks about a paper's contributions / methods / results / equations / figures, wants to compare two papers side by side, or needs a reading note for their records.
> Extracts paper recommendations from social-media posts and online articles (小红书 / Xiaohongshu / RedNote, 微信公众号 / WeChat Official Accounts, Twitter / X threads, Reddit posts, Bilibili videos, blog posts, newsletters, Jina Reader URLs), identifies the underlying academic papers, locates the authoritative original sources (arXiv, conference proceedings, DOI), and triages relevance to the user's research before any library action. Use when the user forwards a social-media link, screenshot, or article that mentions a paper / method / model, asks to "find the original paper" from a blog or thread, shares a 调研贴 / 论文推荐 / paper recommendation post, or wants to evaluate whether a buzz-worthy paper is worth reading before adding it to Zotero.
> Generates publication-quality academic figures (framework diagrams, pipeline illustrations, system architectures, method overviews) from a paper's method text and a target caption, using a local PaperBanana multi-agent pipeline (Retriever → Planner → Stylist → Visualizer → Critic).
> Searches and discovers academic papers across multiple sources (Semantic Scholar, arXiv, Tavily, Exa, Gemini deep research, AMiner, Google Scholar) with adaptive engine selection based on query type. Returns ranked, deduplicated results with metadata (authors, venue, year, citations, abstract, PDF link). Use when the user asks to find papers / literature / publications / preprints / references on a topic, search for related work, look up a specific paper by title or DOI or arXiv ID, find papers by an author, find recent SOTA / state-of-the-art work, survey a research area, or run a deep / comprehensive literature search with synthesis.
Develop EPLAN Electric P8 scripts, API extensions, and remote-control applications. Use when writing C# scripts for EPLAN (actions, event handlers, ribbon), accessing the EPLAN API (parts database, projects, pages), building external apps that drive EPLAN via Remote Client, or debugging EPLAN automation issues (blocking, threading, dispose). Covers EPLAN 2022–2025.
Analyzes security risks in the AI Agent/MCP Skill catalog. Used when users request to inspect, audit, review, or scan the Skill catalog for potential security risks, including command injection, data leakage, prompt word attacks, stealth access, remote execution, or other malicious activities within the SKILL package.
Manage RSS/Atom feed subscriptions and refresh them into the Tapestry knowledge base. Use when the user wants to list, add, remove, or refresh subscribed feed sources by name rather than by URL.
Expose the organized knowledge base through a readable frontend experience. Use when a user wants to browse the knowledge base visually as a lightweight site instead of reading raw Markdown files directly. Supports building viewers for specific data paths (e.g., individual books) or the entire knowledge base.
Primitive web crawling and scraping for one or more URLs. Use when a user shares links, asks to ingest or archive web content, or needs raw source artifacts normalized into reusable local records before feed-building or synthesis.
Automatically triggered when Tapestry is first launched on a new environment or lacks dependencies. Intelligently detects environment and installs Tapestry dependencies with user confirmation.
Export knowledge base content to PDF, Markdown, or HTML files saved under _data/exports/. Use when a user wants to save or share KB content as downloadable files. Supports exporting a single document file, a single feed entry, a single note, or the entire knowledge base.
Turn a crawler-produced Tapestry artifact into a source-appropriate structured feed. Use when a user wants a rigorous feed entry, normalized text output, or source-specific organization rather than a loose free-form summary.
AI-native web intelligence workflow for crawling, organizing, and synthesizing web content from multiple platforms (Zhihu, Reddit, HN, X/Twitter, Xiaohongshu, Weibo). Use when users share URLs, want to archive web content, build knowledge bases, or analyze online discussions.
Convert stored Tapestry artifacts into higher-level synthesis. Use when a user wants interpretation, consolidation, research notes, or analysis built on top of an already-ingested URL or note.
Generate professional visual note cards (视觉笔记卡片/信息图) from knowledge base content as single-page HTML infographics with PNG export. Use when users want to create visual summaries, knowledge cards, infographics, or poster-style visualizations of topics from the knowledge base.
| Use when a user runs, debugs, verifies, or ships a DL experiment on a GPU they OWN or RENT (AutoDL, RunPod, vast.ai, Lambda, Paperspace, 恒源云/矩池云/Featurize/揽睿星舟, bare SSH, Slurm, K8s; spot 抢占/preemption, 断点续训/resumable, 防 SSH 断线/tmux 守护, 多实例 ablation, 关机/销毁/stop-vs-terminate billing, checkpoint 磁盘满, CUDA OOM/显存不足, loss NaN/spike/不收敛, overfit 单 batch, FSDP/DeepSpeed/torchrun, 多卡 hang, 训练太慢/GPU util 低, dataloader/数据增广 bug; 消融结果异常/ablation looks wrong, 复现/reproducibility, 数据泄漏/leakage/test-set tuning, mAP=0/全零指标, 输出恒定/model-ignores-input, train-good/val-collapse, 对比不公平/unfair baseline, 单 seed/no error bars, loss 太好/too-good-to-be-true, 跨文档对账/cross-doc drift; 交付产物/deliverable, 唯一真源/single source of truth, best ckpt 拉回, 结果可视化/论文图脚本, manifest/provenance, 一键复现/repro, EVIDENCE.json. NOT for multi-cloud price-shopping + auto spot-recovery (SkyPilot), BYOC dev environments (dstack), or zero-ops serverless inference (Modal).
Use this skill to review, tighten, draft, and rewrite prose so it does not read like generic LLM output, AI writing, bland marketing, or generic launch copy. Apply it when the user asks to make writing less generic/AI-sounding, tighten a talk intro, sharpen presentation/slide copy, or improve articles, wiki pages, README text, emails, posts, scripts, product/DevRel copy, launch copy, and other important writing with AI tropes, inflated significance language, generic cadence, weak flow, or marketing fog.
> repo quality/adoption/trust, discoverability, contribution readiness, topics, homepage URL, description, issues/wiki/license/CI settings, owner/org-wide repo audits, repo popularity/adoption signals, README/package/GitHub metadata drift, URL/license/topics gaps, and Agent Skill repo packaging/evals. Use when the user asks for repo-level readiness, repo metadata/configuration, or visitor/contributor trust. Do not trigger for README-only writing, one PR descriptions/reviews, function-level code tests, or general implementation/architecture work; defer those to narrower specialists unless the user explicitly asks for repo-level readiness/proof/metadata judgment.
> Enforce testing best practices when writing, reviewing, or improving tests. Covers TDD/red-green-refactor, property-based testing, real objects over mocks, correctness by construction, assertion quality, E2E/contract tests, flaky-test upgrades, mock-reality drift, mutation-style gap analysis, and sabotaged/skipped/weak tests. Use when writing tests, reviewing test quality, fixing flaky tests, improving coverage quality, or when the user mentions TDD, testing, coverage, mocks, invariants, types vs tests, defense in depth, or test quality.
> Help users prepare and review pull requests that maintainers actually want to merge. Use this skill whenever someone asks for help writing a PR description, preparing a contribution to an open-source project, reviewing their own PR before submitting, writing a bug fix PR, drafting a feature PR, or asks anything like "how do I make a good pull request", "review my PR", or "why do my PRs keep getting rejected". Also trigger when a user shares a diff or patch and wants help presenting it, when they mention contributing to a repo they don't maintain, when they're preparing their first contribution to a project, or when they want a pre-submission sanity check. Even if the user doesn't explicitly mention "PR" — if they're packaging a code change for someone else to review, this skill applies.
> Code quality verification gates wired into the agent lifecycle. Use this skill whenever writing, modifying, reviewing, or debugging code — including new features, bug fixes, refactors, troubleshooting, CI/CD setup, or project bootstrapping. Also use when the user mentions "quality", "testing strategy", "CI pipeline", "guardrails", "debugging", or asks how to improve code reliability. If you're writing code or trying to understand why code isn't working, this skill applies. Do not use for README/prose-only edits, copywriting, or documentation wording changes unless the user asks for code/build/CI verification or the docs change affects generated/runtime output.
Create, improve, or audit README.md documents for GitHub projects. Use only when the user explicitly asks for a README, README quality/readability, README accuracy, or README examples. Do not use for full docs sites, API-reference-only work, general repo launch/readiness audits, topics/homepage metadata, or broad repository review.
Apply a Swiss Poster design system using Tailwind CSS. Use when asked to style a webpage with poster-style layouts where one dominant graphic event breaks the grid, overlaps, bleeds/crops at the edge, uses microtype, hard figure/ground contrast, and extreme typographic scale. Implements IBM Plex Sans, stone color palette, opacity hierarchy, and compositional techniques from Weingart, Troxler, Hofmann, and Odermatt & Tissi.
Use when the user asks Codex, Claude Code, ChatGPT-style coding agents, or another AI agent to build, inspect, run, debug, or revise COMSOL Multiphysics / COMSOL Desktop models. Choose the simplest real COMSOL control path for the task: saved `.mph` inspection, local COMSOL documentation, direct COMSOL executables (`comsolbatch`, `comsolcompile`, `comsolmphserver`, `comsol.exe mphclient`), or the sim COMSOL runtime when structured live introspection, shared Desktop collaboration, checkpointing, or plugin diagnostics are useful. Do not use for generic COMSOL theory.
Example for using Deckset to create Markdown presentations
Expert guidance on using PostgreSQL with Swift. Use when developers mention: (1) PostgreSQL or Postgres in Swift, (2) postgres-nio library, (3) SQL queries in Swift, (4) PostgreSQL connection pooling, (5) prepared statements, (6) type-safe database access, (7) bulk loading or COPY FROM, (8) PostgresClient or PostgresConnection.
Expert guidance on writing GitHub Actions workflows. Use when: (1) writing or editing any file under .github/workflows/, (2) choosing between an existing action and a shell script in CI, (3) picking action versions, (4) configuring workflow permissions or cloud authentication from CI, (5) reviewing a workflow in a PR.
Expert guidance on Swift library and framework design. Use when developers mention: (1) designing a Swift library or framework, (2) public API design patterns, (3) protocol-oriented architecture or associated types, (4) result builders or DSL design, (5) performance optimization for libraries, (6) @inlinable or @usableFromInline, (7) noncopyable types for APIs, (8) progressive disclosure in API design, (9) ResponseGenerator or builder patterns.
Claude Skills — questions
Answers built from the skills we actually parsed.