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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nkda-archimprove-test-promotion
by nkdAgilityAnalyses the test suite to identify tests that can be promoted to a faster category (Live→Simulated→Feature→Unit) and retires slower tests that are made redundant by faster equivalents. Runs standalone or as a post-implementation hook.
nkda-archcheck-architecture-review
by nkdAgilityRuns all five architecture perspective checks (Modular Monolith, Clean Architecture, Hexagonal, Vertical Slice, Screaming Architecture), then runs nkda-archimprove-codebase to propose deepening opportunities, and produces a single combined, prioritised report.
nkda-archimprove-codebase
by nkdAgilityFind deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.
nkda-archimprove-documentation
by nkdAgilityAnalyse repository documentation and propose restructuring, deepening, and broadening opportunities across docs, .agents/30-context, and .agents/20-guardrails. Use when the user wants documentation architecture, documentation audits, audience separation, agent token control, or documentation growth as features are added.
nkda-archimprove-red-team-review
by nkdAgilityChallenges a feature specification with adversarial thinking to surface blind spots, wrong assumptions, missing failure modes, and unstated risks before planning begins.
nkda-archimprove-test-validity
by nkdAgilityScores every test in the target scope for intrinsic value across five dimensions. Tests classified as WASTE are deleted immediately. Runs before nkda-archimprove-test-promotion so that only valuable tests enter the promotion pipeline.
nkda-core-definition-of-done
by nkdAgilityValidates that a completed unit of work meets every criterion in the Definition of Done — build, tests, code quality, connector coverage, documentation, and compliance review. Fails if any redline is violated.
nkda-core-implementation-architecture-compliance
by nkdAgilityImplementation-time architecture compliance review — validates changed code against all architecture perspectives, seam contracts, and guardrails before completion gates.
nkda-core-plan-architecture-compliance
by nkdAgilityDesign-time compliance review — validates spec.md and plan.md against all five architecture perspectives (Modular Monolith, Clean, Hexagonal, Vertical Slice, Screaming) plus constitution and guardrails before task generation begins. Analyses proposed design, not code.
nkda-core-tasks-architecture-compliance
by nkdAgilityDesign-time compliance review for tasks.md — validates task-level implementation coverage against spec.md and plan.md across all architecture perspectives and active guardrails before implementation.
nkda-debugtests
by nkdAgilityMandatory procedure for diagnosing and fixing failing tests. For each failing test- validate it fails, read test results and logs as evidence, state root cause, apply minimal fix, verify. Then widen sequentially to unit, original failing tests, simulated tests, and live tests. Completion is blocked until all required suites are green after the last code, configuration, test, script, or workflow change, with fresh evidence in the response.
nkda-observability-contract
by nkdAgilityInspects, validates, and amends a feature specification to ensure complete, decision-driven observability coverage across metrics, traces, and logs. Fails if minimum standards cannot be met.
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.