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.
Advisory guidance for using the experimental Unity CLI (the official `unity` command-line tool) alongside UnitySkills — cold-start a bound project without Unity Hub, probe editor liveness, launch with arguments, run headless tests, run one-shot batch automation, and build headlessly. Only applies when the project has been bound in the UnitySkills panel (Library/UnitySkills/cli_config.json exists with enabled:true). 实验性 Unity CLI(官方 unity 命令行工具)与 UnitySkills 协同的指导文档——免 Unity Hub 冷启动已绑定项目、探测编辑器存活、传参启动、无头测试、批处理运行、无头构建;仅当项目已在 UnitySkills 面板完成绑定(存在 Library/UnitySkills/cli_config.json 且 enabled:true)时适用。
Source-anchored design rules for Cysharp UniTask 2.5.10 (Unity 2018.4+) — struct semantics, PlayerLoop timing, cancellation, composition, conversion, async enumerables, triggers, and pitfalls. Use when writing or reviewing async UniTask code, choosing PlayerLoopTiming, handling CancellationToken, or composing WhenAll/WhenAny, even if the user just says "异步" or "零分配async". 为 Cysharp UniTask 2.5.10(Unity 2018.4+)提供源码锚定的设计规则(struct 语义、PlayerLoop 时机、取消、组合、转换、异步流、触发器、陷阱);当用户要编写或审查 async UniTask 代码、选择 PlayerLoopTiming、处理 CancellationToken、或组合 WhenAll/WhenAny 时使用。
Manage the Universal Render Pipeline (URP) — URP assets, the renderer, and renderer features. Use when configuring the URP asset, adding or editing renderer features, or adjusting URP rendering settings, even if the user just says "URP配置" or "渲染特性". 管理通用渲染管线(URP:URP 资产、渲染器、渲染器特性);当用户要配置 URP 资产、添加或编辑渲染器特性、或调整 URP 渲染设置时使用。
Work with the SRP Volume framework — create/load VolumeProfile assets and create global/local Volume GameObjects with components. Use when setting up volumes, creating or loading a VolumeProfile, or adding global/local volumes to a scene, even if the user just says "Volume" or "体积". 使用 SRP Volume 框架(创建/加载 VolumeProfile 资产、创建全局/局部 Volume GameObject 及组件);当用户要搭建 Volume、创建或加载 VolumeProfile、或向场景添加全局/局部 Volume 时使用。
Validate project and scene health plus cleanup — find broken references, missing scripts, and other integrity issues. Use when checking for broken or missing references, validating scene/project integrity, or cleaning up issues before a build, even if the user just says "检查引用" or "有没有丢失". 校验项目与场景健康度并清理(查找断裂引用、丢失脚本及其他完整性问题);当用户要检查断裂或丢失引用、校验场景/项目完整性、或在构建前清理问题时使用。
Set up XR Interaction Toolkit (XRI) for VR/AR — XR rigs and grab/socket/ray interactors. Use when building VR/AR interaction, setting up an XR rig, or configuring grab/socket/ray interactors, even if the user just says "VR" or "XR交互". 搭建用于 VR/AR 的 XR Interaction Toolkit(XRI:XR rig、抓取/插槽/射线交互器);当用户要构建 VR/AR 交互、搭建 XR rig、或配置抓取/插槽/射线交互器时使用。
Persistent operation history and orchestration — snapshots, task/session undo, bookmarks, and batch planning/retry/rollback. Use when undoing a whole task or session, snapshotting before risky changes, planning or previewing batch operations, or rolling back, even if the user just says "撤销整个操作" or "回滚". 持久化操作历史与编排(快照、任务/会话级撤销、书签、批量规划/重试/回滚);当用户要撤销整个任务或会话、在高危改动前快照、规划或预览批量操作、或回滚时使用。
Last-resort guidance for safely hand-editing Unity serialized YAML (.unity/.prefab/.asset/.meta/ProjectSettings) — reference/fileID repair, GUID safety, and merge-conflict fixes. Use when REST cannot reach the change and YAML must be hand-edited — fixing m_Script GUIDs, broken fileID references, .meta files, or merge conflicts, even if the user just says "场景文件打不开" or "引用丢了". 安全手编 Unity 序列化 YAML(.unity/.prefab/.asset/.meta/ProjectSettings)的最后手段(引用/fileID 修复、GUID 安全、合并冲突修复);当 REST 无法触达、必须手编 YAML 时使用——修复 m_Script GUID、断裂 fileID 引用、.meta 文件或合并冲突。
Source-anchored design rules for YooAsset v2.3.18 — initialization, default-package shortcuts, play modes, asset handles, loading, updates, filesystem, build, and pitfalls. Use when writing or reviewing YooAsset code, initializing packages, loading assets via handles, setting up hot-update/download, or choosing a play mode, even if the user just says "热更" or "资源包". 为 YooAsset v2.3.18 提供源码锚定的设计规则(初始化、默认包快捷方式、运行模式、资源句柄、加载、更新、文件系统、构建、陷阱);当用户要编写或审查 YooAsset 代码、初始化 package、用句柄加载资源、配置热更/下载、或选择运行模式时使用。
Automate YooAsset hot-update and asset bundles — build bundles, run Editor simulate builds, manage Collector groups, analyze BuildReport, and validate runtime. Use when building or simulating YooAsset bundles, configuring collectors, or validating hot-update assets, even if the user just says "热更" or "打AB包". 自动化 YooAsset 热更新与资源包(构建 bundle、编辑器模拟构建、管理 Collector 分组、分析 BuildReport、运行时校验);当用户要构建或模拟 YooAsset 资源包、配置 collector、或校验热更资源时使用。
Use when writing or running Paparazzi screenshot tests in stripe-android — covers PaparazziRule setup, recording/verifying commands, and test structure
Use when writing or structuring unit tests in stripe-android — covers runScenario pattern, fakes, Turbine Flow testing, and Truth assertions
Use when writing Compose UI tests in stripe-android — covers composeRule setup, Robolectric annotations, node assertions, and test tag patterns
Use when writing NetworkRule integration tests in stripe-android — covers testBodyFromFile, inline JSON modification, request matchers, and fixture patterns
Use when creating a fake test implementation in stripe-android — covers FakeClassName pattern, Turbine call tracking, ViewActionRecorder, and ensureAllEventsConsumed validation
ERC-8004 Trustless Agents — on-chain agent identity + reputation. Resolve an agent by id (Identity Registry ERC-721 → owner + AgentCard), list the canonical registry addresses, and generate a spec-compliant agent registration card (registration-v1, with x402 support + trust models). Custody-free, read + scaffolding. Triggers: ERC-8004, trustless agent, agent identity, agent registry, AgentCard, agent reputation, on-chain agent, A2A, agent discovery, agent card, 8004.
x402 agentic payments (Coinbase's HTTP 402 protocol). Custody-free tools to pay x402-protected endpoints (build the EIP-3009 transferWithAuthorization the payer signs, assemble the X-PAYMENT header), call a facilitator (verify/settle/supported), and monetize your own API by generating PaymentRequirements. USDC on Base. Triggers: x402, HTTP 402, pay per request, agentic payment, machine payment, X-PAYMENT, transferWithAuthorization, EIP-3009, facilitator, monetize API, pay for API, agent pays.
Use when building wallet infrastructure, authenticating users, managing embedded wallets, configuring access controls and policies, or integrating blockchain transactions into applications. Reach for this skill when working with user onboarding, wallet creation, transaction signing, policy enforcement, or API integration across Ethereum, Solana, and other blockchains.
Create, track and update team goals + KPIs via the Deeplake virtual filesystem at memory/goal/ and memory/kpi/. Use whenever the user mentions a goal, objective, KPI, target, milestone, or asks to track progress on something measurable. ALSO use when the user says "task", "todo", "work item", "remind me to", "fix X", or any actionable work item — the goal system replaced the legacy `hivemind tasks` CLI and now covers both objectives and tasks.
Query the local code graph (functions, classes, calls, imports) through the Deeplake mount at memory/graph/. Use when the user asks structural questions about the codebase — "what calls X?", "what does Y import?", "where is Z defined?", "what's the architecture / which subsystems exist?", "what's the impact of changing this?". The graph is an AST-derived map of the repo, queried as files (no build needed — it rebuilds automatically).
Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.
Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.
Create, track and update team goals + KPIs via the Deeplake virtual filesystem at memory/goal/ and memory/kpi/. Use whenever the user mentions a goal, objective, KPI, target, milestone, or asks to track progress on something measurable. ALSO use when the user says "task", "todo", "work item", "remind me to", "fix X", or any actionable work item — the goal system replaced the legacy `hivemind tasks` CLI and now covers both objectives and tasks.
Query the local code graph (functions, classes, calls, imports) through the Deeplake mount at memory/graph/. Use when the user asks structural questions about the codebase — "what calls X?", "what does Y import?", "where is Z defined?", "what is the architecture / which subsystems exist?". The graph is an AST-derived map of the repo, queried as files (no build needed — it rebuilds automatically).
Create, track and update team goals + KPIs in Hivemind via the `hivemind` CLI. Use whenever the user mentions a goal, objective, KPI, target, milestone, or asks to track progress on something measurable. ALSO use when the user says "task", "todo", "work item", "remind me to", "fix X", or any actionable work item — the goal system replaced the legacy `hivemind tasks` CLI and now covers both objectives and tasks.
Query the local code graph (functions, classes, calls, imports) through the Deeplake mount at memory/graph/. Use when the user asks structural questions about the codebase — "what calls X?", "what does Y import?", "where is Z defined?", "what is the architecture / which subsystems exist?". The graph is an AST-derived map of the repo, queried as files (no build needed — it rebuilds automatically).
Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.
Create, track, and read team goals + KPIs via Hivemind from openclaw. Use whenever the user mentions a goal, objective, KPI, target, milestone, or asks to track progress on something measurable. ALSO use when the user says "task", "todo", "work item", "remind me to", "fix X", or any actionable work item — the goal system replaced the legacy `hivemind tasks` CLI and now covers both objectives and tasks.
Query the local AST-derived code graph (functions, classes, calls, imports) for structural codebase questions — what calls X, what does Y import, where is Z defined, blast radius of a change. The graph rebuilds automatically after each agent turn; use hivemind_graph_search and hivemind_graph_neighborhood tools (no manual build step).
Use when answering a question that needs data from a ktx-connected database - investigating, analyzing, "how many", "show me", "what's the breakdown of", finding records by value, exploring tables, comparing periods, explaining metrics, or any data-analysis request. Triggers even when the user does not say "analytics"; if the answer requires querying a configured ktx connection, this skill applies.
Map dbt `schema.yml` / `properties.yml` models and sources into ktx semantic-layer overlays and column notes. Covers `sources:` vs `models:`, column `data_tests` (not_null, unique, accepted_values, relationships), and how bundle-time writes complement manifest backfill from git sync. Load when the WorkUnit's `skillNames` includes `dbt_ingest` or when raw files are dbt YAML under `models/` / `sources/`.
Synthesize durable KTX wiki pages from staged Google Drive document pulls. Load when a WorkUnit contains Google Doc raw files from `docs/**`.
Identify recurring cross-table historic-SQL analytical intents from a bounded pattern shard and emit typed pattern evidence for deterministic wiki projection.
Convert one changed historic-SQL table usage bucket into typed table usage evidence for deterministic _schema projection.
Classify and resolve conflicts detected during bundle ingest (structural duplicates, definitional contradictions, near-duplicate clusters, re-ingest changes, evictions).
Capture semantic-layer and knowledge updates from a live database schema snapshot.
Extract durable ktx knowledge and semantic-layer contribution proposals from staged Looker runtime dashboard, Look, and explore JSON. Load for WorkUnits whose raw files are under explores/, dashboards/, or looks/.
Map a LookML view/model/explore into ktx semantic layer sources. Covers the LookML to ktx primitive table, provenance tagging, and three worked examples (overlay, standalone from derived_table, standalone with sql_always_where). Load when the turn contains `.lkml` content.
Convert Metabase questions, models, and metrics into ktx Semantic Layer source definitions. Covers result-metadata to KSL column type mapping, FK/PK detection, near-duplicate deduplication, pre-aggregation decomposition, join-graph connectivity, and how to react to priorProvenance from earlier ingest syncs. Load when the WorkUnit contains `cards/<id>.json` files under a Metabase bundle.
Map a MetricFlow semantic_model or metric into ktx semantic layer sources. Covers the MetricFlow to ktx primitive table, `extends:` inheritance flattening, metric-type handling (simple / derived / ratio / cumulative / conversion), `model: ref('x')` resolution, and four worked examples. Load when the turn contains `.yml`/`.yaml` files with top-level `semantic_models:` or `metrics:`.
Synthesize durable ktx wiki pages and semantic-layer sources from staged Notion pages, databases, data-source rows, and clustered Notion evidence. Load when a WorkUnit contains Notion raw files or Notion evidence chunks.
Extract durable ktx wiki knowledge from staged Sigma data model specs and workbook summaries. Load for WorkUnits with unitKey sigma-data-models or sigma-workbooks.
ktx's semantic layer - a structured catalog of sources (tables/views), measures, joins, and segments expressed as YAML. Covers the schema and how to query it via `sl_query`. Use when the task involves querying pre-defined metrics (ARR, churn, retention, LTV, MAU) or reading SL source YAML to understand the catalog. Capture is handled by the `sl_capture` skill (memory-agent only).
How to capture new reusable patterns into ktx's semantic layer - when a measure, segment, or join belongs in the catalog and how to write it generically so it stays small and useful over time. Loaded by the post-turn memory-agent only. The research agent does not write to the SL.
ktx's knowledge base - wiki pages for durable, reusable business knowledge. Covers capture workflow for user preferences, metric definitions, organizational conventions, and cross-references between wiki pages and semantic-layer sources. Loaded by the post-turn memory-agent only. The research agent reads wiki via `wiki_read`/`wiki_search` but does not write it.
Installs and configures ktx, the open-source context layer for data agents — runs ktx setup non-interactively with hidden CLI flags, configures database connections and embeddings, installs agent integration, and verifies readiness. Use when the user asks an agent to add ktx to a project, connect data sources, install agent rules, ingest schema, or troubleshoot a local ktx install.
Roll Playwright Java to a new version
Prepare a Playwright Java release after the rolling PR has merged — cut the release branch, mark the Maven version, draft the GitHub release, and tick the Java boxes in the internal checklist.
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
Use when the user gives a topic and wants an automated topic-driven narrated explainer, podcast, or knowledge-summary video (Bilibili / YouTube / Xiaohongshu / Douyin / WeChat Channels), or asks to learn visual design patterns from a reference video/image. Trigger when the user mentions creating a knowledge video, narrated explainer, video podcast, or animated infographic-style video from a topic — even if they don't say "video podcast" explicitly. Also trigger when the user wants to regenerate, re-render, rebuild, update, or iterate on a narrated video this skill already produced — e.g. they edited the script/prompt, changed the visuals, or swapped the background music and want the final video remade (reuse the existing videos/{name}/ directory, never start a new project). Do NOT trigger for generic video editing, trimming, format conversion, color grading, or non-narrative video tasks. Produces 4K video via research → script → TTS → Remotion → MP4 + BGM.
Design principles and creative direction for building HTML artifacts — pages, reports, plans, landing pages, demos, decks, and small tools. Use when creating or restyling any visual HTML deliverable and deciding its palette, type pairing, layout, theming, or overall register, or when the output must not look generically AI-generated.
Direct-invocation specialist for self-contained HTML diagrams whose layout, notation, and interaction clarify relationships, sequence, topology, state, hierarchy, or quantitative structure. Use when the user explicitly invokes html-diagram or the broad html skill routes a diagram request here. Do not activate independently from a general request.
Direct-invocation specialist for clear, self-contained HTML plans that preserve source material while improving hierarchy, sequence, ownership, dependencies, and reviewability. Use when the user explicitly invokes html-plan or the broad html skill routes a plan request here. Do not activate independently from a general request.
Direct-invocation specialist for polished, responsive, self-contained HTML mockups and interactive prototypes grounded in the user's conversation, product context, and design language. Use when the user explicitly invokes html-prototype or the broad html skill routes a mockup or prototype request here. Do not activate independently from a general request. Treat a mockup as a noninteractive fidelity mode within this skill, not as a separate skill.
Direct-invocation specialist for low-fidelity, self-contained HTML wireframes that test information hierarchy, content, navigation, task flow, and responsive structure before visual design. Use when the user explicitly invokes html-wireframe or the broad html skill routes a wireframe request here. Do not activate independently from a general request. Do not use for polished mockups or production-like interaction; use html-prototype for those.
Create or redesign self-contained single-file HTML artifacts with a visual direction shaped by the user's brief, project, and subject. Use when HTML is the deliverable for a report, explainer, landing page, presentation, tool, mixed artifact, or broad request. This is the collection's only implicit router. Route clear wireframe, prototype, mockup, plan, or diagram requests to the matching direct-invocation specialist when available. Do not use for ordinary application implementation when a standalone HTML file is not the deliverable.
Redesign GitHub README homepages or create project-native pure SVG, hybrid SVG-composed PNG/WebP, and opt-in animated GIF assets. Use when a user asks to beautify, redesign, rebrand, visually upgrade, simplify, or audit a GitHub README; create only a hero, section headers, diagrams, badges, motion graphics, showcase modules, or other README assets; or turn a repository homepage into a cohesive visual story. If whole-README work versus asset-only work is unclear, ask which scope the user wants. For hero-like assets where pure SVG and generated raster material are both viable, explain the tradeoffs and confirm the implementation before creating the asset.
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
Generate a Python code skeleton from an experiment blueprint
Claude Skills — questions
Answers built from the skills we actually parsed.