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...
js-analyzer
by H-mmerJavaScript static analysis agent for client-side security review. Use for analyzing JS bundles, finding hardcoded secrets, tracing DOM XSS source-sink flows, identifying postMessage handlers, extracting API endpoints, and reviewing client-side access controls. Provide URLs or local JS file paths.
quality-check
by H-mmerReport quality scorer. Use BEFORE submitting any report to validate completeness, clarity, title strength, CVSS accuracy, PoC quality, and overall report grade. Provide the draft report path or content.
quickscan
by H-mmerRun a quick security scan on a target. Consults the Brain first, validates scope, runs passive recon + vuln scan in parallel.
quality
by H-mmerScore a report draft before submission. Usage: /quality <draft-path-or-finding-description>
quickscan
by H-mmerRun a quick security scan on a target. Consults the Brain first, validates scope, runs passive recon + vuln scan in parallel.
quality
by H-mmerScore a report draft before submission. Usage: /quality <draft-path-or-finding-description>
quickscan
by H-mmerRun a quick security scan on a target. Consults the Brain first, validates scope, runs passive recon + vuln scan in parallel.
quality
by H-mmerScore a report draft before submission. Usage: /quality <draft-path-or-finding-description>
xxe-hunter
by H-mmerXXE specialist (H1 #63). Use for testing XML parsing endpoints, file upload processors, SOAP services, SVG handlers, and any feature accepting XML input.
hunt-business-logic
by H-mmerHunting skill for business-logic vulnerabilities (CWE-840 Business Logic Errors, CWE-841 Improper Enforcement of Behavioral Workflow, CWE-639 Authorization Bypass via User-Controlled Key in business contexts, CWE-362 race conditions on financial flows). Built from 44 corpus reports plus 8.8K shared-platform reports across HackerOne, Bugcrowd, Huntr, GitHub Security Advisories, plus 2024-2026 meta verified against NVD — Lilishop coupon overpurchasing (CVE-2024-50654 CVSS 7.5), WWBN AVideo wallet double-spend TOCTOU (CVE-2026-34368, GHSA-h54m-c522-h6qr), Keycloak 2FA bypass (CVE-2025-3910, GHSA-5jfq-x6xp-7rw2), AlegroCart 1.2.9 negative-quantity price manipulation (Andrey Stoykov SecLists Apr 2025), Bagisto cart price manipulation (Rudransh Singh Rajpurohit Sep 2025), Doppler free-trial reset (Aditya Sunny Dec 2024), Stripe hasEverTrialed bypass (better-auth
cost
by H-mmerShow cost tracking and ROI for this engagement.
status
by H-mmerShow engagement dashboard with program info, scope, brain state, findings, agent activity, and cost estimate.
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.