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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cleo-validator
by kryptobaseddevIndependent IVTR peer-reviewer role. The Validator is spawned by a Lead AFTER a Worker reports an implementation candidate to verify that every Acceptance Criterion is satisfied by programmatic evidence. The Validator MUST be a different agent instance from the Worker who built the change — same-agent self-attestation is rejected as rubber-stamping. Use when spawned with `subagent_type: cleo-validator` (or equivalent role token) and a target task ID; the Validator pulls the AC list via `validator.ac-pull`, runs the IVTR rubric, then ships exactly ONE binary verdict — `validator.attest` (pass) or `validator.reject` (fail). Triggers: 'validate this implementation', 'run the IVTR rubric', 'attest the acceptance criteria', 'is this PR ready to merge?', any spawn carrying role=validator. Implements ADR-083 §4 (Validator role) and the SG-IVTR-AC-BINDING saga (T10377).
skill-monorepo
by kryptobaseddevCLEO provider adapter for Anthropic Claude Code CLI. Default export is the adapter class for dynamic loading by AdapterManager. T5240 Use when: (1) calling its 2515 API functions, (2) configuring @cleocode/monorepo, (3) understanding its 1754 type definitions, (4) working with its 104 classes, (5) user mentions "@cleocode/monorepo" or asks about its API.
skill-caamp
by kryptobaseddevCentral AI Agent Managed Packages - unified provider registry and package manager for AI coding agents Use when: (1) running caamp CLI commands, (2) calling its 276 API functions, (3) configuring @cleocode/caamp, (4) understanding its 180 type definitions, (5) working with its 8 classes, (6) user mentions "ai", "agent", "skills", "cli", "claude", (7) user mentions "@cleocode/caamp" or asks about its API.
ct-adr-recorder
by kryptobaseddevRecords Architecture Decision Records from accepted consensus verdicts. Use when promoting a consensus outcome to a formal ADR: drafts the document in the proposed-then-accepted HITL lifecycle, links to the originating consensus manifest, persists the decision to the canonical SQLite decisions table, and triggers downstream invalidation when an accepted ADR is later superseded. Triggers on phrases like 'write ADR', 'record architecture decision', 'formalize this decision', 'lock in the choice', 'create ADR-XXX', or when a consensus task reaches completed status and needs formalization.
ct-artifact-publisher
by kryptobaseddevBuilds and publishes artifacts to registries (npm, PyPI, cargo, docker, GitHub releases, generic tarballs) following the validate, then dry-run, then build, then publish, then record-provenance pipeline. Invoked by ct-release-orchestrator as a sub-skill when a release has artifact config. Never stores credentials in output or manifest (ARTP-008), always dry-runs first (ARTP-002), halts and attempts rollback on failure (ARTP-009). Triggers when a release config has at least one enabled artifact handler.
ct-cleo
by kryptobaseddevCLEO task management protocol - session, task, and workflow guidance. Use when managing tasks, sessions, or multi-agent workflows with the CLEO CLI protocol.
ct-codebase-mapper
by kryptobaseddevCodebase analysis and mapping for autonomous agent understanding. Builds structured maps of project stack, architecture, conventions, testing, integrations, and concerns.
ct-consensus-voter
by kryptobaseddevRuns structured multi-agent voting for decision tasks with confidence scores, conflict detection, and HITL escalation when the threshold is not met. Use when two or more agents must vote on options: architecture choices, tool selection, policy decisions, when a task carries agent_type:analysis, or on phrases like 'reach consensus', 'vote on options', 'resolve the debate', 'pick the best approach'. Produces a voting matrix JSON, enforces the 0.5 threshold, flags ties within 0.1 confidence as contested and escalates to human tiebreak.
ct-contribution
by kryptobaseddevGuided workflow for multi-agent consensus contributions. Use when user says "/contribution", "contribution protocol", "submit contribution", "consensus workflow", "multi-agent decision", "create contribution", "contribution start", "contribution submit", "detect conflicts", "weighted consensus", "decision tracking", "conflict resolution".
ct-council
by kryptobaseddevConvene "The Council" — a 5-advisor, shuffled gate-based peer-review, chairman-synthesis workflow for reviewing a plan, decision, architecture, or piece of work inside the current project. Use when the user says "convene the council" (or "counsel"), "get the council on this", "council review", "run the five advisors", "stress-test this", "get multiple perspectives", or asks for a rigorous multi-angle challenge of a proposal (Contrarian, First Principles, Expansionist, Outsider, Executor → shuffled peer review with pass/fail gates → convergence detector → Chairman verdict). Operates on the current codebase — each advisor grounds their analysis in actual files/commits before opining. Output is validated by scripts/validate.py.
ct-dev-workflow
by kryptobaseddevDevelopment workflow orchestration for task-driven development with atomic commits, conventional commit messages, and systematic release processes. Enforces task traceability, branch discipline, smart test scope selection, and GitHub Actions integration. Use when committing code, creating releases, managing branches, or following contribution protocols. Triggers on commit operations, release preparation, or workflow compliance needs.
ct-docs-lookup
by kryptobaseddevThis skill should be used when the user asks "how do I configure [library]", "write code using [framework]", "what are the [library] methods", "show me [framework] examples", or mentions libraries like React, Vue, Next.js, Prisma, Supabase, Express, Tailwind, Drizzle, Svelte. Triggers for library setup, configuration, API references, framework code examples, or version-specific docs ("React 19", "Next.js 15").
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.