Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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qwik
by 46ki75Expert guidance for Qwik (v1 and v2) and its meta-framework — Qwik City in v1, Qwik Router in v2 — covering resumability, components, signals, stores, tasks, context, QRLs, file-based routing, route loaders/actions, middleware, endpoints, layouts, async data (useResource$/useAsync$/Suspense), v1→v2 migration, and deployment. Use whenever someone writes, debugs, or reviews Qwik code, asks about useSignal, useStore, useTask$, useAsync$, routeLoader$, routeAction$, component$, the $ suffix, resumability vs hydration, migrating v1 to v2, server-side data fetching, REST/JSON endpoints, middleware, or deployment. Always invoke for any question mentioning Qwik, Qwik City, Qwik Router, QRL, routeLoader$, routeAction$, useVisibleTask$, useAsync$, noSerialize, or the resumable architecture.
ag-ui-knowledge
by 46ki75Expert guidance for AG-UI (Agent–User Interaction Protocol) — the open, event-based protocol connecting AI agents to user-facing apps. Covers the run lifecycle, event types (lifecycle, text, tool-call, state, activity, reasoning), RunAgentInput, AbstractAgent/HttpAgent, multimodal messages, shared state via STATE_SNAPSHOT/STATE_DELTA (RFC 6902 JSON Patch), frontend-defined tools, interrupt-aware HITL with RunAgentInput.resume, encrypted reasoning for ZDR, capability discovery, middleware, serialization with parentRunId branching, the TypeScript (`@ag-ui/client`, `@ag-ui/core`) and Python (`ag-ui-protocol`) SDKs plus community SDKs. Use whenever someone builds or consumes AG-UI, wires up CopilotKit, LangGraph, CrewAI, Mastra, Pydantic AI, LlamaIndex, Agno, AG2, Microsoft Agent Framework, Google ADK, AWS Strands, or Bedrock AgentCore, or mentions AG-UI, ag_ui, RunStartedEvent, STATE_DELTA, TOOL_CALL_START, REASONING_*, AbstractAgent, HttpAgent, or EventEncoder. Always invoke.
rust-toasty
by 46ki75Expert guidance for the Toasty Rust ORM: model definition with `#[derive(toasty::Model)]` and the `#[key]`, `#[auto]`, `#[unique]`, `#[index]`, `#[has_many]`, `#[belongs_to]`, `#[has_one]` attributes; relationships, association preloading, the `create!`, `update!`, `find_by_*`, and `filter_*` query macros and builders; batch operations, transactions, embedded types, deferred fields, scalar `Vec` array fields, and driver-specific behavior for SQLite, PostgreSQL, MySQL, and DynamoDB. Also covers Toasty internals for contributors: the app/db schema layers and mapping, the query-engine compilation pipeline (AST → Simplify → Lower → Plan → Execute), and the driver trait. Always invoke this skill for any question mentioning Toasty, the `toasty` crate, `toasty::Model`, `toasty::Db`, `toasty::HasMany`, `toasty::BelongsTo`, `toasty::HasOne`, the `toasty::create!` or `toasty::update!` macros, code under `submodules/toasty/`, or any Rust code that imports `toasty`.
web-view-transition
by 46ki75Expert guidance for implementing the View Transition API — covering same-document (SPA) transitions with `document.startViewTransition()`, cross-document (MPA) transitions with `@view-transition`, customizing animations via CSS pseudo-elements (`::view-transition-old`, `::view-transition-new`, `::view-transition-group`), per-element animations with `view-transition-name`, JavaScript control via the `ViewTransition` promises (`ready`, `finished`, `updateCallbackDone`), context-aware transition types with `:active-view-transition-type()`, and graceful fallbacks for unsupported browsers. Use this skill when someone wants page transition animations, shared-element transitions, slide/fade/circular-reveal effects, or asks about `startViewTransition`, `@view-transition`, `view-transition-name`, `::view-transition-*` pseudo-elements, or the `ViewTransition` object — even if they just say "smooth page transitions" or "animate between routes".
skill-creator
by 46ki75Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
a2ui-knowledge
by 46ki75Expert guidance for implementing the A2UI (Agent to UI) protocol — covering surfaces, components, data binding, catalogs, message streams (v0.9: createSurface / updateComponents / updateDataModel / deleteSurface / sendDataModel; v0.8: surfaceUpdate / dataModelUpdate / beginRendering / deleteSurface), the A2A extension binding, custom component catalogs (defining schemas, registering renderers, catalog negotiation, versioning, graceful degradation, two-phase validation), client/renderer architecture (MessageProcessor / SurfaceModel / ComponentImplementation / Binder layer), custom functions, and v0.8 → v0.9 migration. Always invoke this skill for any question that mentions A2UI, surfaceUpdate, beginRendering, createSurface, updateComponents, updateDataModel, sendDataModel, a2uiClientCapabilities, supportedCatalogIds, catalogId, basic_catalog, DynamicComponent, formatString, ChildList, ComponentId, or agent-driven UI streaming — even if the question seems simple.
conventional-commits
by 46ki75Write, format, and review conventional commit messages following the Conventional Commits 1.0.0 spec and this project's house rules. Use this skill whenever the user asks you to write a commit message, format or fix a commit message, check whether a commit message is valid, explain the commit convention, suggest a type or scope, or describe what changes to make before committing. Trigger even when the user doesn't say "conventional commits" explicitly — phrases like "how should I word this commit", "what type is this", "write a commit for", "is this commit message ok", or just pasting a raw description and asking for a commit all count.
development-standards
by 46ki75Org-internal engineering standards for this organization's projects. Invoke whenever scaffolding a new repo, auditing an existing one, setting up CI, or configuring tooling files like `Cargo.toml`, `rust-toolchain.toml`, `justfile`, `.editorconfig`, `.markdownlint-cli2.yaml`, `tsconfig.json`, `package.json`, `pnpm-lock.yaml`, `bunfig.toml`, `pyproject.toml`, `uv.lock`, `.python-version`, or `*.tf`. Also invoke for work involving `axum`, `utoipa`, `markdownlint-cli2`, `uv`, `ruff`, `pyright`, `pytest`, or Node package-manager setup (pnpm is the org default). Rust and Python are fully documented: Cargo workspace inheritance, MSRV pinning, `just` as task runner, `cargo-llvm-cov` coverage, hermetic-vs-live test split, Axum + utoipa OpenAPI; uv workspaces, packaged `src` layout, ruff, pyright strict, pytest live-marker tiers. TypeScript, Node.js, Bun, Terraform, Rust libraries, and Rust GraphQL are stubs — invoke anyway so the user can define the convention rather than receive an improvised one.
markdown
by 46ki75Markdown linting and automated fixing using markdownlint-cli2. Use when Claude needs to: (1) Check markdown files for style issues, (2) Fix markdown formatting problems, (3) Ensure markdown follows best practices, (4) Validate markdown documents, or (5) Apply consistent markdown styling
mermaid
by 46ki75Create diagrams and visualizations using Mermaid syntax. Use when users request flowcharts, sequence diagrams, class diagrams, state diagrams, entity relationship diagrams, Gantt charts, pie charts, mindmaps, timelines, user journey maps, Git graphs, quadrant charts, or requirement diagrams. Also use when users want to visualize processes, workflows, systems, data structures, or relationships.
prompt-evaluation-claude-code
by 46ki75Eval-driven prompt refinement that runs entirely inside Claude Code via the Agent/Task tool — no Python, no SDK, no API key. Each candidate run and each judge call executes in an isolated subagent with a fresh context window, so samples are independently graded and the main session stays focused on synthesis and iteration. Trivially parallel: spawn N candidate + M judge subagents in one assistant message. Invoke when the user wants to evaluate, A/B test, regress-test, or iterate on a prompt directly inside Claude Code, especially when they reference subagents, the Task/Agent tool, or "test this prompt without writing code". Phrases like "use Claude Code to evaluate this prompt", "spawn subagents to test", "parallel-test these variants", "A/B these prompts in Claude Code", "grade this rubric with subagents", or "iterate this prompt with fresh contexts" qualify. Pairs with the broader `prompt-evaluation` skill for shared dataset-design and binary-judge methodology.
prompt-evaluation
by 46ki75Eval-driven prompt refinement for Anthropic / Claude API prompts. Turn vibes-based prompt tweaking into a measurable loop: look at real failures, build a dataset, pick a grading approach (code-graded, model-graded, or both), scaffold a runnable eval (Anthropic SDK, `promptfoo`, or Claude Console), run it, analyze failures by category, propose targeted edits, re-run, compare. Invoke whenever the user wants to evaluate, improve, compare, A/B test, regress-test, or iterate on a prompt — even if they don't say "eval". Phrases like "is this prompt good?", "make this prompt better", "compare these two prompts", "my prompt fails on X", "tests for this prompt", "edge cases this classifier misses", "switch to Haiku without regressions", "evaluate this RAG answer", "grade this agent's tool use" all qualify. Covers code-graded, model-graded (LLM-as-judge), RAG-specific (faithfulness, answer-relevance), tool-use / agent evals, dataset design, and production patterns (capability vs regression suites, CI/CD).
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.