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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variant-hunt
by vinhnxvFind similar bugs across the codebase based on a confirmed finding. Takes a TOME finding ID or pattern description, extracts the root cause, and systematically searches for variants using progressive generalization. Use when: "find more like this", "variant analysis", "similar bugs", "same pattern elsewhere", "hunt for variants", "variant hunt".
tarnished
by vinhnxvMaster command — natural-language router for all Rune workflows. Parses VN+EN input, chains multi-step workflows, checks prerequisites. Also a Rune expert for guidance and education ("how does rune work", "what should I do"). Use for: "/tarnished plan|work|review|arc ...", "rune help", "which command", "do everything", "thảo luận", "khám phá", master command, routing, custom agent, agent list.
team-sdk
by vinhnxvCentralized team management SDK for Rune workflows. Provides ExecutionEngine interface (TeamEngine), shared lifecycle protocols (teamTransition, cleanup, session isolation), preset system, and monitoring utilities. Use when spawning agent teams, monitoring teammates, or cleaning up workflows. Loaded automatically by workflow skills (appraise, strive, devise, mend, etc). Keywords: team management, team lifecycle, teamTransition, cleanup, agent teams, TeamCreate, TeamDelete, spawnAgent, shutdown, wave execution.
testing
by vinhnxvTest orchestration pipeline for arc Phase 7.7 — 4-tier testing (unit, property-based, integration, E2E/browser) with diff-scoped discovery and structured reporting. Extended tier covers contract validation, visual regression, design tokens, a11y, test history, and flaky detection. Auto-loaded by the arc orchestrator during the test phase. Keywords: testing, test pipeline, unit test, integration, E2E, PBT, property-based, fast-check, hypothesis, proptest, visual regression, design token, accessibility, flaky test, contract validation.
using-rune
by vinhnxvUse when the user asks to review code, plan features, brainstorm ideas, audit a codebase, implement a plan, fix review findings, debug failed builds, analyze code impact, or run end-to-end workflows. Also use when the user seems unsure which Rune command to use, when the user says "review", "plan", "brainstorm", "explore idea", "audit", "implement", "fix findings", "ship it", "check my code", "what changed", or "help me think through this". Routes user intent to the correct /rune:* command. Keywords: which command, what to use, rune help, workflow routing, review, audit, plan, brainstorm, explore, implement.
mcp-builder
by vinhnxvGuide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
appraise
by vinhnxvMulti-agent code review using Agent Teams. Summons up to 7 built-in Ashes in standard mode, each with their own dedicated context window. Handles scope selection, team creation, review orchestration, aggregation, verification, and cleanup. Optional `--deep` runs multi-wave deep review with up to 18 Ashes across 3 waves. Phase 1.5 adds UX reviewers when frontend files are detected (UX subsystem hardcoded in v3.x). Phase 1.7 adds data flow integrity reviewer (FLOW prefix) when 2+ stack layers detected in diff.
arc
by vinhnxvFull plan-to-merged-PR pipeline with checkpoint framework, QA phases, and multi-agent orchestration. Use when running end-to-end from plan to merge, resuming an interrupted arc with --resume after a crash or session end, or when any named phase fails (forge, work, code-review, mend, test, ship, merge, etc. — see body for full phase list). Keywords: arc, pipeline, --resume, checkpoint, convergence, forge, mend, ship, merge.
ash-guide
by vinhnxvUse when summoning Rune agents, when encountering "agent not found" errors, when selecting which review agents to use, or when checking agent capabilities and tools. Quick reference for all 116 agents across 8 categories (74 core in agents/ — investigation, meta-qa, qa, research, review, utility, work + 42 extended in registry/ — investigation, review, testing, utility, work). Plus 13 shared resources in agents/shared/. Keywords: agent list, Ash, subagent type, agent not found.
audit
by vinhnxvFull codebase audit using Agent Teams. Sets scope=full and depth=deep (by default), then delegates to the shared Roundtable Circle orchestration phases. Summons up to 7 built-in Ashes (custom Ashes wired in orchestration layer in v3.x). Optional `--deep` runs multi-wave investigation with deep Ashes. Phase 0.45 context building spawns context-builder agent to map trust boundaries, invariants, and state flows before vulnerability hunting (auto for deep). Supports `--focus` for targeted audits. Supports `--incremental` for stateful, prioritized batch auditing with 3-tier coverage tracking (file, workflow, API) and session-persistent audit history.
brainstorm
by vinhnxvCollaborative exploration of features and ideas through structured dialogue. Explore WHAT to build before planning HOW. Three modes: Solo (conversation), Roundtable (agent advisors engage user), Deep (advisors + elicitation sages). Produces persistent brainstorm documents in docs/brainstorms/. Use when: "brainstorm", "explore idea", "what should we build", "discuss feature", "thao luan", "kham pha y tuong", "brainstorm this", "let's think about".
cc-inspect
by vinhnxvClaude Code runtime environment inspector. Comprehensive diagnostic tool that reports all Claude Code environment variables, session identity, config directory, plugin paths, system toolchain versions, Rune runtime state, and platform details. Use when debugging environment issues, verifying session isolation, checking config directory resolution, or diagnosing plugin loading. Trigger keywords: cc inspect, claude code inspect, env check, environment, session id, config dir, diagnostic, runtime info, plugin env, plugin root, system info, toolchain. Note: This skill uses disable-model-invocation: true because it is a diagnostic tool meant for explicit user invocation only. It appears in the /rune:cc-inspect routing table entry.
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.