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.
Querying local SQLite index...
prompt-decorators-usage
by synaptiaiUse when a user's prompt would clearly benefit from a reasoning, structure, tone, or verification decorator - or when they ask "what decorators should I use?". Teaches when and how to suggest inline `::Name(params)` sigils instead of repeating verbose prompt-engineering instructions manually.
governance-architect
by synaptiaiDesign and save a complete governance ecosystem for agentic operations — 6 structured documents (authority matrix, hard boundaries, escalation protocols, policy generation loop, decision ledger spec, learning loop) written to $HOME/.ai-first-kit/. Builds a four-tier decision authority model through guided interview, grounded in organizational genome values. Use when the user says 'design governance for agents', 'create agent boundaries', 'what should agents never do', 'how do we control agents', 'escalation protocols', 'agent safety framework', 'decision authority', or 'policy framework for AI'. Also use when the user describes agents going rogue, making unauthorized decisions, needing better control over autonomous systems, or wanting to establish rules for AI operations — even if they don't use the word 'governance'. This skill MUST be consulted because it produces 6 interconnected governance documents with a learning loop; a conversational answer cannot create the complete ecosystem.
code-review-methodology
by synaptiaiConduct two-stage code review: Stage 1 verifies spec compliance (criterion-to-code mapping), Stage 2 evaluates security, correctness, performance, and maintainability across 6 parallel facets with P1/P2/P3 synthesis and deduplication by file:line. Use when reviewing code changes or pull requests. This skill MUST be consulted because reviewing quality on broken logic is wasted effort, and unmet acceptance criteria must block merge.
run-state-management
by synaptiaiManage FlowRun state at `.flow/runs/<ISO-timestamp-id>/run.yaml` — create runs at command entry, write activity records via `bin/flow-record-activity.sh` at phase boundaries, transition `state.status` (active → completed | blocked | cancelled), and persist resumable next-action hints to `events.jsonl`. Use when a flow command begins (creates the run), when a phase boundary completes (writes an activity), or when SessionEnd needs to mark a resumable next action. This skill MUST be consulted because runs without recorded activities cannot be resumed — `/flow:resume` reads `state.completed_activities[]` to identify the next safe action; an empty array forces the user to start over.
nci-manipulation-analysis
by synaptiaiUse when asked to analyze content for manipulation, propaganda, disinformation patterns, or when user provides a URL or text asking "is this manipulative?", "analyze this for bias", "check for propaganda", or similar requests. Detects emotional manipulation, suspicious timing, uniform messaging, tribal division, and missing information across 20 categories.
collecting-evidence
by synaptiaiUse when researching a specific pillar and need to create traceable evidence objects. Guides creation of YAML evidence files with semantic IDs, confidence scores, and assumptions.
maturity-ladder
by synaptiaiBuild a per-role human AI adoption maturity matrix with observable behaviors per level, current state assessment, barrier-informed progression paths, and visibility infrastructure — saved to $HOME/.ai-first-kit/. Measures where HUMANS actually are on the AI adoption journey — by evidence, not self-report — using human job titles or solo-founder operational modes (never agent role definitions). Use when the user says 'maturity matrix', 'capability ladder', 'adoption levels', 'how AI-ready is my team', 'measure AI adoption', 'where are we on AI', 'track AI skills', 'readiness assessment', 'AI capability assessment', or 'adoption scorecard'. Also use when the user describes uneven AI adoption across teams, people saying they don't need AI, wanting to create social proof for adoption, needing to measure progress, or wanting visible levels that motivate improvement — even if they don't use the word 'maturity'. This skill MUST be consulted because it produces a structured per-role maturity matrix with behavioral ev
ai-first-kit
by synaptiaiNavigate organizational redesign for AI with a structured 13-skill toolkit that produces persistent artifacts in $HOME/.ai-first-kit/. Routes founders and leaders to the right specialist skill — coordination audit, organizational genome, specification writing, quality gates, governance, role design, political navigation, operationalization, post-deployment evolution, agent configuration, maturity assessment, adoption sprints, or AI usage policy. Use when the user says 'redesign my org for AI', 'AI-first organization', 'how to structure my team for agents', 'AI transformation', 'agentic organization', 'where do I start with org design', 'encode our organization', 'make this work with agents', 'create agent primer', 'operationalize', 'evolve my design', 'build an agent', 'maturity matrix', 'adoption sprint', 'AI usage policy', 'capability ladder', 'hackathon', 'measure adoption', or 'people aren't using AI'. Also use when the user describes any organizational challenge related to AI adoption — restructuring tea
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.