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...
st-refine-plan
by e0ipsoRefine an existing Strikethroo plan in this repository. Use when the user asks to review, improve, interrogate, or update a specific plan ID — discovers the local .ai/strikethroo root, resolves the plan, runs the project's plan hooks, pressure-tests the document for gaps and contradictions, gathers clarifications interactively or autonomously, and updates the plan in-place while preserving its identity and structure. Do not use for creating new plans or for generic brainstorming outside Strikethroo.
st-execute-task
by e0ipsoExecute a single task from a Strikethroo plan. Use when the user asks to run, implement, or carry out one specific task ID within a plan — discovers the local .ai/strikethroo root, resolves the plan, validates the task file, checks status and dependencies, runs pre-execution hooks, deploys an agent, updates status, documents noteworthy events, and emits a structured Task Execution Result. Do not use for generic development work outside Strikethroo.
st-create-plan
by e0ipsoCreate a new Strikethroo plan for this repository. Use when the user asks to draft, plan, or scope a new strikethroo plan — discovers the local .ai/strikethroo root, runs the project's plan hooks, gathers clarifications, allocates the next plan ID, and writes a Markdown plan conforming to PLAN_TEMPLATE.md. Do not use for generic brainstorming or work outside Strikethroo.
st-execute-blueprint
by e0ipsoExecute a Strikethroo plan blueprint for this repository. Use when the user asks to run, implement, or carry out a specific plan ID — discovers the local .ai/strikethroo root, resolves the plan, validates or auto-generates tasks and the execution blueprint, optionally creates a feature branch, runs phases with lifecycle hooks, enforces validation gates, appends an execution summary, and archives the completed plan. Do not use for generic development work outside Strikethroo.
st-generate-tasks
by e0ipsoGenerate atomic Markdown tasks for an existing Strikethroo plan in this repository. Use when the user asks to decompose a specific plan ID into tasks — discovers the local .ai/strikethroo root, resolves the plan, runs the project's task-generation hooks, allocates sequential task IDs, and writes one task file per atomic unit conforming to TASK_TEMPLATE.md. Do not use for generic project planning or work outside Strikethroo.
st-full-workflow
by e0ipsoExecute the complete Strikethroo workflow from plan creation through blueprint execution for this repository. Use when the user asks to run the full end-to-end workflow for a work order — discovers the local .ai/strikethroo root, creates a plan, generates atomic tasks, and executes the blueprint, all in a single uninterrupted sequence. Do not use for individual plan creation, task generation, or blueprint execution; use the dedicated skills for those.
kk-add
by e0ipsoCapture a kenkeep node manually from the current session. Writes a new node directly under `.ai/kenkeep/nodes/<kind>/`. The reviewer accepts by leaving the file in place and rejects by deleting it. Use when the user wants to record a project convention, gotcha, rationale, or named-thing into the project knowledge base.
kk-bootstrap
by e0ipsoFirst-time bootstrap of the project knowledge base from existing markdown documentation. Surveys docs, follows cross-references, and writes new node files directly under `.ai/kenkeep/nodes/`. Supervised by the user, who reviews each node on disk before accepting or deleting it. Use when the user wants to seed an empty knowledge base from the project's existing docs.
kk-curate
by e0ipsoCurate pending session logs into kenkeep nodes by reading sessions in-host, drafting curator actions, then deduping and persisting via the kenkeep primitives. Resolves any surfaced contradictions interactively with the user. Use when the user wants to process accumulated session captures, or when the SessionStart nudge reports pending session logs.
self-review-critique
by e0ipsoCritique a git diff and generate review.xml with comments and suggestions for human validation in self-review
kb-bootstrap
by e0ipsoFirst-time bootstrap of the project knowledge base from existing markdown documentation. Surveys docs, follows cross-references, and writes new node files directly under `.ai/knowledge-base/nodes/`. Supervised by the user, who reviews each node with `git diff` before committing. Use when the user wants to seed an empty knowledge base from the project's existing docs.
kb-curate
by e0ipsoCurate pending session logs into knowledge-base nodes by running the `npx @e0ipso/ai-knowledge-base curate` CLI, then resolve any contradictions surfaced by the curator with the user in-session. Use when the user wants to process accumulated session captures, or when the SessionStart nudge reports pending session logs.
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.