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...
gjalla-code-review
by gjallaReview a code change (diff or PR) for ship-readiness before merge. Use when reviewing your own or someone else's changes prior to committing/merging/etc.
gjalla-breakdown
by gjallaBreak a feature spec into intentional waves and bite-sized tasks grouped by dependency. Use after a spec is written to prepare for easy-to-track implementation.
gjalla-test-audit
by gjallaAudit a test suite to find tests that give false confidence — tests that encode bugs, duplicate coverage, or are so heavily mocked they can't catch real regressions. Use to improve robustness, audit coverage, or harden a risky area.
gjalla-prepare-and-commit
by gjallaMake sure your code is ready to commit, then stage changes into clean, atomic commit. Use before committing.
gjalla-spec-review
by gjallaFull review of a plan or spec to be sure there are no surprises, gaps, or mistakes. Use to harden a spec before implementation.
gjalla-spec
by gjallaCreate a well-thought-out plan covering problem statement, goals, technical approach, and verification criteria. Use while planning / before implementing any non-trivial feature.
design-api
by gjallaDesign APIs with the discipline expected at companies known for great developer experiences. Use this skill when building new API endpoints, redesigning existing ones, or reviewing API contracts. Triggers on phrases like "design the API", "new endpoint", "API contract", "what should the API look like", any task that involves defining how systems communicate, or when building a feature that will expose an API. Good API design should happen before implementation, not after.
conduct-post-mortem
by gjallaConduct a blameless post-mortem analysis after an incident, outage, or significant bug. Use this skill when something went wrong in production and the team needs to understand what happened, why, and how to prevent it from happening again. Triggers on phrases like "post-mortem", "root cause analysis", "RCA", "what went wrong", "why did this break", or after resolving a production issue when the user wants to document learnings.
design-for-change
by gjallaDesign to accommodates the system's likely future, not just its present state. This is "temporal design" — reasoning about how today's implementation interacts with tomorrow's roadmap and evolving requirements. Use this skill when making decisions during implementation: where to place abstractions, how to structure interfaces, when to make something configurable vs. hardcoded. Triggers when you're choosing between approaches and the difference between them is how well they'll hold up over time.
manage-tech-debt
by gjallaIdentify, catalog, and plan the resolution of technical debt in a codebase. Use this skill when the user wants to understand the health of their codebase, prioritize cleanup work, or plan a refactoring effort. Triggers on phrases like "tech debt", "code quality", "refactor", "cleanup", "code health", "maintainability", "this code is a mess", or when you notice code quality issues during implementation that should be tracked rather than fixed inline (which could result in a game of whack-a-mole).
perform-review
by gjallaThree-perspective code review from Senior Tech Lead, Product Manager, and QA Engineer. Use this skill when the user wants to be sure that their feature is complete, high quality, and bug free. Triggers when the user says things like "review this code", "is this done", "is this production ready", or any request for substantive code review beyond. If the user wants both cleanup and review, use prepare-for-review first. Also triggers when you are reviewing your own implementation to ensure completeness before reporting that you're done.
prepare-for-review
by gjallaCleanup and completeness gate to run before task completion in preparation for a code review. Use this skill when implementation is done and the code needs to be tidied up before handing off to reviewers. Triggers when the user says things like "clean this up for review", "prep for PR", "check the definition of done", "remove dead code", "is this complete", "ready to submit", "wrap this up", or when you are nearing completion of a 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.