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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framework
by BrainBlend-AIGuide for the Atomic Agents Python framework — schemas, agents, tools, context providers, prompts, orchestration, and provider configuration. Use when code imports from `atomic_agents`, defines an `AtomicAgent`, `BaseTool`, or `BaseIOSchema`, or the user asks about multi-agent orchestration or LLM-provider wiring in an atomic-agents project.
new-app
by BrainBlend-AIScaffold a new Atomic Agents project from scratch — create the directory, `pyproject.toml`, env file, first agent, and a runnable entry point. Use when the user asks to start a new atomic-agents project from scratch, says "scaffold" / "new project" / "start from zero", or runs `/atomic-agents:new-app`.
create-atomic-agent
by BrainBlend-AIBuild and wire an `AtomicAgent[InSchema, OutSchema]` — schemas, `AgentConfig`, `SystemPromptGenerator`, provider client, history, hooks, optional context providers. Use when the user asks to "create an agent", "add another agent", "build an `AtomicAgent`", "wire up an agent", "make a planner/router/extractor agent", or runs `/atomic-agents:create-atomic-agent`.
create-atomic-context-provider
by BrainBlend-AIBuild a `BaseDynamicContextProvider` that injects a named, titled block into an agent's system prompt at every `run()` — current time, user identity, retrieved RAG docs, session state, cached DB schema. Use when the user asks to "add a context provider", "inject X into the prompt", "give the agent dynamic context", "wire up RAG", "make a `BaseDynamicContextProvider`", or runs `/atomic-agents:create-atomic-context-provider`.
create-atomic-schema
by BrainBlend-AIDesign and write a `BaseIOSchema` input/output pair for an Atomic Agents agent or tool — docstrings, field descriptions, validators, error variants. Use when the user asks to "create a schema", "design the input/output schema", "define an `IOSchema`", "write a `BaseIOSchema`", "model the agent's output", or runs `/atomic-agents:create-atomic-schema`.
create-atomic-tool
by BrainBlend-AIBuild a `BaseTool[InSchema, OutSchema]` subclass — input/output schemas, `BaseToolConfig`, `run()` (and optional `run_async()`), env-driven secrets, typed failure outputs. Use when the user asks to "add a tool", "create a tool", "wrap an API as a tool", "build a `BaseTool`", "make a calculator/search/weather tool", or runs `/atomic-agents:create-atomic-tool`.
release
by BrainBlend-AIRelease a new version of atomic-agents to PyPI and GitHub. Use when the user asks to "release", "publish", "deploy", or "bump version" for atomic-agents.
tesseron-diagram
by BrainBlend-AICreate professional technical diagrams (architecture, sequence, state, flow) in the Tesseron docs visual language — dark slate base with a single amber accent, dotted-grid background, gradient cards, Lucide-style icons, Gaussian-blur glow on the emphasized node, and Inter + JetBrains Mono typography. Use when creating docs diagrams for a Tesseron-style project, any Starlight/Astro docs site that wants a restrained monochrome-plus-one-accent aesthetic, or when the user says "use our Tesseron design system".
update-docs
by BrainBlend-AIUse this skill after any change to `packages/**` source that shifts Tesseron's public surface - protocol messages, exported types, action/resource builder APIs, ActionContext methods, transports, gateway CLI flags, or React hooks - to sync `docs/src/content/docs/` so the Starlight site matches the code. Triggers on "update the docs", "sync docs", "docs are stale after this change", or when a session ends with public-surface edits unaccompanied by doc edits. Do NOT trigger for test-only, tooling-only, or internal refactors that leave the public surface identical.
framework
by BrainBlend-AIQuick-reference mental model for the Tesseron TypeScript SDK — core abstractions (app, action, resource, handler, ActionContext, transport, session, gateway), canonical imports per consumer package (`@tesseron/web`, `/server`, `/react`, `/core`, `/mcp`), and the minimum-viable-app template. Load when the user is starting Tesseron work, wiring a first action or resource, deciding which consumer package to import, writing a handler for the first time, or orienting on how the pieces fit together. Triggers on code that imports from `@tesseron/*`, on calls to `tesseron.action(...)`, `tesseron.resource(...)`, `tesseron.connect(...)`, `useTesseronAction`, `useTesseronResource`, or `useTesseronConnection`, and on broad questions like "what is Tesseron", "how do actions work", "where do I start". For authoritative specs — exact wire format, error code tables, handshake and resume shapes, full protocol behaviour — prefer the `tesseron-docs` skill, which queries the live `@tesseron/docs-mcp` server. This skill is the ch
tesseron-docs
by BrainBlend-AIAuthoritative Tesseron documentation lookup via the `@tesseron/docs-mcp` MCP server. Calls `search_docs` to find relevant pages and `read_doc` to fetch the full markdown body when the user asks precision questions about Tesseron protocol or SDK behaviour. Triggers on requests that need an exact spec, not an overview — wire format (JSON-RPC envelopes, `tesseron/hello`, `tesseron/welcome`, `tesseron/resume`, `actions/progress`, `actions/cancel`), transport framing, handshake and claim-code flow, session resume and resumeToken, lifecycle transitions, sampling contract, elicitation contract (`ctx.confirm`, `ctx.elicit`), progress + cancellation via `AbortSignal`, error codes (`TesseronErrorCode`, `SamplingNotAvailableError`, `ElicitationNotAvailableError`, `TimeoutError`, `CancelledError`, `ResumeFailedError`), capability negotiation, origin allowlist, multi-app namespacing, MCP gateway config, Standard Schema (Zod, Valibot, Typebox) integration, action-builder steps, resource read/subscribe, React hook semantics
tesseron-explorer
by BrainBlend-AIMaps existing Tesseron TypeScript codebases — catalogs apps, actions, resources, context-method usage, transports, React hooks, and session-lifecycle wiring; traces how agent invocations flow through handlers into app state; returns a compact architecture summary with file:line references. Use PROACTIVELY when the user asks to "explore", "map", "understand", "analyze", "trace", or "explain how this works" in a project that imports from `@tesseron/core`, `@tesseron/web`, `@tesseron/server`, `@tesseron/react`, or `@tesseron/mcp`, or before extending a non-trivial Tesseron codebase. The caller should pass the scope (project root, package, or specific feature) in the invocation prompt.
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.