Discover and install skills to enhance your AI agent's capabilities.
| Name | Contains | Score |
|---|---|---|
majiayu000/claude-skill-registry Generate visual hierarchy diagrams of agent system showing levels and delegation. Use for documentation or onboarding. | Skills | |
googolme/run0204 Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation. | Skills | |
NeverSight/learn-skills.dev Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations. | Skills | |
Confirm a production deploy actually landed and is healthy. Verifies the latest Vercel Production deployment is READY and matches the current `main` commit, runs HTTP canary checks against travel.matthewcarr.dev, confirms migrations applied, and checks for a post-deploy Sentry error spike. Use after merging to `main`, or when a human asks "is prod healthy?" / "did the deploy go out?" / "smoke test production". Read-only against prod by default. Contains: deploy-smoke-test Confirm a production deploy actually landed and is healthy. Verifies the latest Vercel Production deployment is READY and matches the current `main` commit, runs HTTP canary checks against travel.matthewcarr.dev, confirms migrations applied, and checks for a post-deploy Sentry error spike. Use after merging to `main`, or when a human asks "is prod healthy?" / "did the deploy go out?" / "smoke test production". Read-only against prod by default. | Skills | |
Implement an approved feature specification. Triggered by a routine when a spec PR is merged with the `ai:implement` label (per ADR 057), or by a human asking "implement SPEC-NNN" interactively. Follows the spec's implementation order using TDD, logs deviations, runs the full verification suite, opens an implementation PR with label `ai:done`, and closes out the spec with post-implementation notes and tech debt capture. Contains: implement-spec Implement an approved feature specification. Triggered by a routine when a spec PR is merged with the `ai:implement` label (per ADR 057), or by a human asking "implement SPEC-NNN" interactively. Follows the spec's implementation order using TDD, logs deviations, runs the full verification suite, opens an implementation PR with label `ai:done`, and closes out the spec with post-implementation notes and tech debt capture. | Skills | |
Cross-artefact consistency and quality review of an approved or draft SPEC. Use when the user says "review SPEC-NNN," before `implement-spec` runs, or by `draft-spec` / `revise-spec` before opening or updating a spec PR. Checks the SPEC against the constitution, ADRs, parent epic (if any), and tech debt register. Read-only — produces a structured report, never edits files. Contains: review-spec Cross-artefact consistency and quality review of an approved or draft SPEC. Use when the user says "review SPEC-NNN," before `implement-spec` runs, or by `draft-spec` / `revise-spec` before opening or updating a spec PR. Checks the SPEC against the constitution, ADRs, parent epic (if any), and tech debt register. Read-only — produces a structured report, never edits files. | Skills | |
Draft a SPEC from a GitHub issue and open a PR for review. Use when triggered by a routine on `Issue opened` with label `ai:plan`, or when a user asks to "draft a spec from issue #NNN". Non-interactive — proceeds on best interpretation and surfaces any unresolved questions in the SPEC's §Open Questions section rather than blocking for clarification. The PR review loop is where ambiguity gets resolved. Contains: draft-spec Draft a SPEC from a GitHub issue and open a PR for review. Use when triggered by a routine on `Issue opened` with label `ai:plan`, or when a user asks to "draft a spec from issue #NNN". Non-interactive — proceeds on best interpretation and surfaces any unresolved questions in the SPEC's §Open Questions section rather than blocking for clarification. The PR review loop is where ambiguity gets resolved. | Skills | |
Editorial reviewer for tessl.io blog articles. Scores drafts across six strategic dimensions (audience, AEO/GEO, SEO, Tessl alignment, technical depth, structure) with atom-level recommendations grouped under each dimension. Paste-ready output for Tessl's article review page. Contains: tessl-article-reviewer Reviews blog articles for the Tessl blog (tessl.io/blog) under the current decision-maker editorial direction. Scores the article across six strategic dimensions (Audience & Angle, SEO, AEO/GEO & Originality, Tessl Alignment, Technical Depth, Structure & Readability) totalling /30, with atom-level recommendations grouped under each dimension. Enforces house style (no em dashes, no hype, suggestive tone, heading hierarchy, internal links), commits to one primary keyword from the five priority clusters, and outputs a paste-ready review for Tessl's article review page. Use when someone asks to review, edit, improve, structure, score, or give feedback on a Tessl blog article or draft. | Skills | |
elevenlabs/skills Transcribe audio to text using ElevenLabs Scribe v2. Use when converting audio/video to text, generating subtitles, transcribing meetings, or processing spoken content. | Skills | |
administrakt0r/AI-Agents-Safe-Coding-Skills Azure App Configuration SDK for Java. Centralized application configuration management with key-value settings, feature flags, and snapshots. | Skills | |
administrakt0r/AI-Agents-Safe-Coding-Skills Modern Angular UI patterns for loading states, error handling, and data display. Use when building UI components, handling async data, or managing component states. | Skills | |
anthropics/claude-plugins-official This skill should be used when the user wants to "create a skill", "add a skill to plugin", "write a new skill", "improve skill description", "organize skill content", or needs guidance on skill structure, progressive disclosure, or skill development best practices for Claude Code plugins. | Skills | |
Dicklesworthstone/pi_agent_rust Implement NFT standards (ERC-721, ERC-1155) with proper metadata handling, minting strategies, and marketplace integration. Use when creating NFT contracts, building NFT marketplaces, or implementing digital asset systems. | Skills | |
Dicklesworthstone/pi_agent_rust Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use when building business dashboards, selecting metrics, or designing data visualization layouts. | Skills | |
davepoon/buildwithclaude Automate Google Drive file operations (upload, download, search, share, organize) via Rube MCP (Composio). Upload/download files, manage folders, share with permissions, and search across drives programmatically. | Skills | |
openai/symphony Create a well-formed git commit from current changes using session history for rationale and summary; use when asked to commit, prepare a commit message, or finalize staged work. | Skills | |
elevenlabs/skills Generate music using ElevenLabs Music API. Use when creating instrumental tracks, songs with lyrics, background music, jingles, or any AI-generated music composition. Supports prompt-based generation, composition plans for granular control, and detailed output with metadata. | Skills | |
google/adk-samples Creates product search agents with semantic search and RAG on Google Cloud (Vector Search on Gemini Enterprise Agent Platform, BigQuery, embeddings). Use when the user wants to "build a product search agent", "create an e-commerce search", "make a shopping assistant", "set up semantic catalog discovery", "ingest products into Vector Search", or "deploy a retail RAG agent". Handles the full pipeline: catalog data ingestion to BigQuery, Gemini Enterprise Agent Platform Vector Search collection setup, ADK agent scaffolding, evaluation, and Cloud Run deployment. | Skills | |
Ares960826/ares-agent-toolkit Apply the "learning mechanics" framework — the emerging physics-style theory of deep learning training dynamics — when writing, debugging, scaling, or tuning neural-network code. Use for decisions about learning rate / batch size / width / depth scaling, μP (Maximal Update Parameterization) and hyperparameter transfer, edge-of-stability and progressive sharpening, lazy vs. rich (feature-learning) regimes and initialization scale, neural scaling laws, gradient-flow conservation laws / symmetries, neural collapse, the neural feature ansatz, and for designing scientific experiments on training. Trigger phrases: "learning mechanics", "why is training unstable", "how should I scale the learning rate with width/batch", "muP / mup / hyperparameter transfer", "edge of stability", "lazy vs rich", "feature learning regime", "scaling laws", "tune hyperparameters on a small model". Source: Simon et al., "There Will Be a Scientific Theory of Deep Learning" (2026). | Skills | |
qawolf/cli Manage QA Wolf through the qawolf CLI. Use when asked to create, update, or list coverage requests, bug reports, or maintenance reports; start a run of flows or tags on the QA Wolf platform or read a run's results; list, set, or delete environment variables; manage environments, flows, or tags; run or list flows locally; authenticate; install the local runtime; or drive a live cloud browser (launch a runner, screenshot it, click and type on it, read its recorder) from a shell. | Skills |
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