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...
fit-guide
by forwardimpactGet career guidance and output review grounded in your organization's engineering standard. Use when a promotion conversation ended without specifics and you need evidence of what to improve, when reviewing agent output and you want a second opinion against the standard, or when asking about skills, levels, and career expectations. Also covers setting up the Guide service stack and ingesting knowledge content.
hyprnote-trim
by forwardimpactTrim a Hyprnote transcript to its logical end. Recordings are often left running after a meeting finishes — this skill finds the natural conclusion (goodbyes, sign-offs) and cuts the transcript there. Use when the user asks to trim, cut, or clean up a Hyprnote transcript.
fit-outpost
by forwardimpactKeep track of people, projects, and threads without depending on memory. Use when context is scattered across email, calendar, and notes and you need a daily briefing, when managing email drafts, or when scheduling background AI tasks, maintaining a personal knowledge base, checking agent status, and waking agents on demand.
sync-teams
by forwardimpactSync recent Microsoft Teams chat messages into ~/.cache/fit/outpost/teams_chat/ as markdown files by reading the Teams IndexedDB cache from disk. Use on a schedule or when the user asks to sync their Teams chats. Requires macOS with the Teams desktop app installed.
fit-summit
by forwardimpactMake staffing decisions you can defend by modeling team capability as a system. Use when a post-mortem surfaces the same skill gap again, when evaluating whether a hire, transfer, or promotion strengthens the team, when detecting structural risks like single points of failure, or when simulating what-if scenarios, aligning growth with team gaps, comparing teams, and tracking capability trajectory over time.
candidate-report
by forwardimpactGenerate an A4 single-page HTML candidate assessment report benchmarked against the agent-aligned engineering standard. Use when the user asks you to create a candidate report, one-pager, or visual assessment for a hiring manager.
req-screen
by forwardimpactScreen candidate CVs against the agent-aligned engineering standard to decide whether to invest interview time. Produces a structured screening assessment with interview/pass recommendation and suggested interview focus areas. Use when the user asks to evaluate a CV or when a new CV is detected.
evaluate-evidence
by forwardimpactEvaluate engineering artifacts against skill markers and write evidence rows for Landmark.
hyprnote-follow
by forwardimpactFollow a live Hyprnote session in real-time, coaching the user through a meeting or interview. Understands context from the session title, knowledge base, and candidate pipeline. Provides talking points, flags gaps in coverage, and suggests follow-up questions as the conversation unfolds. Use when the user asks to follow, shadow, or coach them through a live meeting.
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.