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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quality
by igmarinComplete code quality loop for Rails projects with hard gates: enforce naming conventions and linter compliance (rubocop/brakeman/erblint must pass) → refactor only after characterization tests PASS on current code, verify behavior preserved after each extraction → generate YARD docstrings for all public APIs → NEVER open PR before linter, ERB linter, full test suite, security scan, and YARD docs all pass; phases conventions review→refactoring→documentation. Use this composite end-to-end loop instead of individual refactoring or documentation skills when full three-phase production-readiness review is needed in one pass. Trigger: code review prep, before PR, full Rails quality sweep, quality audit, production-ready review, end-to-end quality check.
refactor-code
by igmarinUse when refactoring Rails code to change structure without changing behavior — must write characterization tests and verify they pass on the current code BEFORE touching any production files, identify inputs/outputs keeping public interfaces stable, run verification after every step and the full suite at the end, and include a Stable behavior statement and Verification evidence showing actual command output under the Observed output label. Trigger words: refactor, restructure, extract service, split class, reduce duplication.
code-review
by igmarinReviews Rails (Ruby on Rails) pull requests, diffs, and merge requests for quality, security, and conventions. Use when asked to do a PR review, review my diff, review my merge request, or code review of Ruby on Rails code. Grounds every finding in a real file:line from the actual diff, applies exactly three severity labels (Critical, Suggestion, Nice to have) where Critical covers security/data loss/crash and Always Critical flags (permit!, html_safe on user-supplied content, business logic in controllers, unparameterized SQL, destructive migrations), and always includes a "Code review before merge" task line. Follows the principle: review early, review often; self-review before PR; re-review after significant changes.
apply-stack-conventions
by igmarinUse when writing new Rails code (Ruby on Rails) for the PostgreSQL + Hotwire + Tailwind stack, including TDD (test-driven development), write-tests-first, or red-green-refactor workflows — must write specs and validate them RED BEFORE implementation, verify they pass GREEN after, show spec file content (not just spec path), include a Tests-first proof before implementation section showing actual spec code, the run command (bundle exec rspec spec/[path]_spec.rb), and the Observed RED output and Observed GREEN output labels, keeping steps testable in isolation. MVC structure, ActiveRecord queries, Turbo Frames/Streams, Stimulus controllers, and Tailwind patterns. Not for general Rails design principles — scoped to this specific stack.
implement-graphql
by igmarinUse when building or reviewing GraphQL APIs in Rails with graphql-ruby — must follow the TDD gates by writing a failing spec in spec/graphql/ using AppSchema.execute rather than HTTP controller dispatch, define arguments/return types without leaking internal model names (use connection_type for pagination), implement resolver/mutation classes that delegate to services, prevent N+1 queries by using and priming the dataloader on association loads, and ensure mutations return result and errors shapes on failure. Trigger words: graphql, graphql-ruby, resolver, mutation, dataloader, schema.
write-tests
by igmarinUse when writing, reviewing, or configuring RSpec tests in Ruby on Rails — must execute the spec via `bundle exec rspec` and capture the actual test output (failure message or stack trace) rather than describing expected behavior, prefer behavioral confidence over implementation coupling, pick the smallest spec type exercising the behavior (model > service > request > system), mirror the file paths of the source, use # frozen_string_literal: true, define subject(:result) for service specs, and consult `assets/tdd_proof_checklist.md` when the task involves new behavior. Use when adding test coverage, refactoring specs, or practicing TDD. Trigger words: write spec, rspec, test-driven development, testing, write tests.
rails-agent-skills
by igmarinEntry point for Rails development workflows covering TDD, RSpec, Service Objects, DDD, GraphQL, Engines, and Code Quality. Use when the user asks about Ruby on Rails development patterns, needs RSpec test suites generated, wants service objects scaffolded, is setting up GraphQL schemas, performing Rails code review, refactoring .rb files, working with domain-driven design, implementing background jobs, conducting Rails security checks, or building Rails engines. Generates RSpec tests, structures service objects, enforces TDD workflows, configures GraphQL schemas, and coordinates domain-driven design patterns. Trigger keywords: Rails, RSpec, TDD, Rails testing, Rails refactor, Rails API, Rails code review, domain driven design, service objects, GraphQL, Rails engine, Ruby, .rb, background jobs, Rails migrations, Rails security check.
tdd
by igmarinOrchestrates the full Rails TDD cycle with hard gates: test MUST exist, be run, and FAIL for the correct reason (e.g. undefined method, not syntax error) before any implementation code — propose minimal implementation and wait for user approval → verify test PASSES → run full suite with rubocop, brakeman, rspec all green → produce YARD documentation and self-reviewed PR; phases context/test design→implementation→iterate→finish. Use when practicing test-driven development, red-green-refactor, TDD workflow, writing tests before code, adding tests first, or building a Rails feature where specs must gate implementation.
implement-hotwire
by igmarinUse when creating Hotwire UIs with progressive enhancement in Rails — generates Stimulus controllers, Turbo Frame markup, Turbo Stream responses, and ActionCable broadcast setups, then verifies degraded mode by disabling JavaScript (or running rails test:system with Capybara rack_test driver) and confirming forms submit, links navigate, and data persists after reload. Includes a Verification section with explicit no-JavaScript checks. Stimulus, Turbo, Turbo Frames, Turbo Streams.
agnostic-planning-skills
by igmarinMaster orchestrator for the Agnostic Planning Skills library. Use to discover and activate 11 language-agnostic skills and 4 personas for product planning, task breakdown, estimation, risk assessment, ticket generation, backlog prioritization, sprint planning, retrospectives, requirements clarification, and status reporting. Personas guide end-to-end workflows. prd, planning, tasks, tickets, estimation, risks, status, backlog, sprint, retrospective, tdd, agile, product management, requirements, clarification.
requirements-clarifier
by igmarinTransforms vague task descriptions into actionable specifications with user stories acceptance criteria and identified edge cases — NEVER write implementation code or suggest solutions, do NOT edit files, do NOT produce configuration or test cases, produce requirements only. Language-agnostic. Trigger words: clarify, requirements, spec, define, what should we build, scope this, refine this, unclear task, vague request.
estimate-tasks
by igmarinAssigns relative effort estimates using story points (Fibonacci) t-shirt sizes or time ranges — never mix frameworks within a single table, include confidence level per task, use table format with ID Task Estimate Confidence Notes, and flag high-uncertainty items. Language-agnostic. Use when the user asks to estimate effort, size tasks, or assign story points to backlog items. Trigger words: estimate, story points, t-shirt size, effort, sizing, fibonacci.
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.