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...
ql-review
by andyzengmathPart of the quantum-loop autonomous development pipeline (brainstorm → spec → plan → execute → review → verify). Two-stage code review. Spec compliance first, then code quality. Use after implementation or before merge. Triggers on: review code, code review, check implementation, ql-review.
ql-spec
by andyzengmathPart of the quantum-loop autonomous development pipeline (brainstorm → spec → plan → execute → review → verify). Generate a structured Product Requirements Document (PRD) with user stories, acceptance criteria, and functional requirements. Use when you have an approved design and need formal requirements, or when starting from scratch. Triggers on: create spec, write prd, spec out, requirements for, ql-spec.
ql-verify
by andyzengmathPart of the quantum-loop autonomous development pipeline (brainstorm → spec → plan → execute → review → verify). Iron Law verification gate. Requires fresh evidence before any completion claim. Use before claiming work is done, before committing, or before marking a story as passed. Triggers on: verify, check, prove it works, ql-verify.
ql-intent-check
by andyzengmathIntent-drift audit for quantum-loop. Compares the user's original intent (immutable snapshot) against downstream artifacts (design.md → PRD → quantum.json ACs → implementation) to detect semantic divergence. Flags drift with file:line evidence. Use before merge or when specs feel "rewritten."
ql-brainstorm
by andyzengmathPart of the quantum-loop autonomous development pipeline (brainstorm → spec → plan → execute → review → verify). Deep Socratic exploration of a feature idea before implementation. Asks questions one at a time, proposes 2-3 alternative approaches with trade-offs, presents design section-by-section for approval, and saves an approved design document. Use when starting a new feature, exploring an idea, or before writing a spec. Triggers on: brainstorm, explore idea, design this, think through, ql-brainstorm.
ql-deep-review
by andyzengmathMulti-perspective post-implementation review aggregator for quantum-loop. Invokes 2-7 reviewer agents in parallel based on risk score, applies actionability filter, dedups, aggregates with evidence requirements. Use AFTER the per-story two-stage review gates pass and before merging a whole-feature PR to master. Complements ql-review (per-story) with whole-feature review.
ql-deslop
by andyzengmathPost-review AI-slop cleanup pass. After quality review approves a story/feature, scans only the changed files for: duplicate code, dead code, needless abstraction, boundary violations, missing tests. Proposes deletions with regression-test gate — rolls back on regression. Mandatory unless explicitly skipped via --no-deslop. Borrowed from OMC Ralph 7.5 + 7.6.
ql-housekeep
by andyzengmathDetect repo-hygiene issues that accumulate during long-running autonomous development (merge-conflict markers, orphan worktrees, CPC-variant duplicates, stale branches, version-manifest drift). Detection-only by default — reports findings, never deletes or modifies without explicit user confirmation.
ql-execute
by andyzengmathPart of the quantum-loop autonomous development pipeline (brainstorm → spec → plan → execute → review → verify). Run the autonomous execution loop. Reads quantum.json, queries the dependency DAG, implements stories with TDD and two-stage review gates. Supports parallel execution via native worktree isolation. Use after /quantum-loop:plan has created quantum.json. Triggers on: execute, run loop, start building, ql-execute.
ql-plan
by andyzengmathPart of the quantum-loop autonomous development pipeline (brainstorm → spec → plan → execute → review → verify). Convert a PRD into machine-readable quantum.json with dependency DAG, granular 2-5 minute tasks, and execution metadata. Use after creating a spec with /quantum-loop:spec. Triggers on: create plan, convert to json, plan tasks, generate quantum json, ql-plan.
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.