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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nist-800-53-rmf
by amurthygithubPerform NIST SP 800-53 Rev 5 control selection, implementation, assessment, and continuous monitoring using the NIST Risk Management Framework (SP 800-37 Rev 2 RMF). Covers FIPS 199 categorization, baseline selection (Low/Moderate/High), 800-53A assessment procedures, control inheritance (FedRAMP/shared services/cloud), SAR/POA&M and ATO determination. Activate when performing RMF Step 2 (categorize), Step 3 (select), Step 4 (implement), Step 5 (assess), Step 6 (authorize), or Step 7 (monitor); when mapping SOC 2 / ISO 27001 / PCI / HIPAA to 800-53; when planning or executing a FedRAMP authorization; or when responding to a federal/DoD assessment.
audit-category-pointer
by amurthygithubPointer to a library of 4 specialized Audit skills — ISACA, COSO, AICPA SOC, and Audit Workpapers. Use when working on IT audit, internal controls, SOC reporting, or audit documentation tasks.
hipaa-security-rule
by amurthygithubHIPAA Security Rule (45 CFR Part 164, Subpart C): 22 standards across administrative (9), physical (4), technical (5), organizational (2), and policies/documentation (2) families, with Required vs Addressable implementation specifications. Serves BOTH auditor and auditee personas. Use to run a §164.308(a)(1)(ii)(A) risk analysis, work addressable-specification dispositions per §164.306(d)(3), check a BAA against §164.314(a)(2)(i), build an OCR-readiness matrix across all 22 standards, or right-size safeguards via the §164.306(b)(2) flexibility factors. Activate when the user says 'HIPAA Security Rule', 'ePHI', '45 CFR 164', 'addressable specification', 'business associate agreement', 'BAA', 'OCR audit', 'security risk analysis', 'HIPAA safeguards', or 'recognized security practices'.
fedramp-authorization
by amurthygithubFedRAMP cloud-authorization program (Rev 5) — the FedRAMP Authorization Act of 2022 (44 U.S.C. 3607-3616), OMB M-24-15, the Rev 5 baselines (Low 156 / Moderate 323 / High 410 / LI-SaaS 156, tailored from NIST SP 800-53 Rev 5), the SSP/SAP/SAR/POA&M package, the 3PAO assessment, monthly Continuous Monitoring, and the FedRAMP 20x direction. Two load-bearing facts: FedRAMP baselines ARE tailored 800-53 controls (not a separate catalog — that is nist-800-53-rmf), and the current authorizer is the statutory FedRAMP Board, NOT the retired JAB. Use to categorize a system (FIPS 199 high-water mark) and select a baseline, scope an authorization package, plan a 3PAO assessment, run monthly ConMon and POA&M, or determine LI-SaaS eligibility. Activate when the user says 'FedRAMP', 'cloud authorization', 'ATO', 'P-ATO', 'agency authorization', '3PAO', 'SSP', 'SAR', 'POA&M', 'ConMon', 'continuous monitoring', 'Li-SaaS', 'FedRAMP baseline', 'FedRAMP Moderate/High', 'FedRAMP 20x', 'authorization to operate', or 'cloud servic
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.