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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stack-l3-zod
by shynlee04Zod v4 TypeScript-first schema validation — complete API, patterns, and v3→v4 migration
stack-l3-vitest
by shynlee04Vitest testing framework API reference, patterns, and harness-specific testing conventions
hivefiver-persona-routing
by shynlee04Route users into vibecoder, floppy_engineer, or enterprise_architect lanes with strict governance defaults and domain-aware onboarding.
gate-l3-lifecycle-integration
by shynlee04Internal quality gate that evaluates whether Hivemind harness implementations correctly participate in the runtime lifecycle — covering 9-surface mutation authority, CQRS boundaries, actor hierarchy, event-driven wiring, classification fit (src/ vs .opencode/ vs .hivemind/), and OpenCode SDK surface compliance. Synthesized from .planning/codebase/ARCHITECTURE.md (9-surface authority table) and ingested @opencode-ai/plugin SDK v1.14.44 from anomalyco/opencode (tool(), hook() signatures). Use when performing a lifecycle gate check, auditing harness module integration, verifying CQRS boundary compliance, checking delegation hierarchy constraints, evaluating tool/hook registration correctness, running a harness quality gate, validating plugin composition integrity, or running phase audit on src/ modules. Activates during code review of src/ files, phase audit, milestone verification, integration check, and deployment readiness workflows.
hm-l2-phase-execution
by shynlee04Execute GSD-style phase plans with wave-based parallelization and checkpoint recovery. Use when running a multi-plan phase, managing plan dependencies, recovering from mid-phase interruptions, when plans need to run in parallel, when execution needs checkpoint gates, or when phase work spans multiple sessions. Even when the user says "run the phase" or "execute the plan." Triggers: "execute phase", "run phase", "phase execution", "wave-based execution", "parallel plans", "checkpoint recovery", "plan dependencies". NOT for single-task execution or unstructured work.
hm-l2-product-validation
by shynlee04Product-lens methodology for validating technical decisions against user impact, product vision, and business value. Use when the user asks "is this feature valuable", "what's the user impact", "RICE score", "product validation", "validate against users", "how do I measure success", "feature prioritization", "product lens", "product context", "end user value", "does this solve the right problem", "problem vs solution", "stakeholder perspective", "should we build this", "define success metrics", "quantify user benefit", "anti-solution-check", or "prioritize features by impact". NOT for initial brainstorming (use hm-brainstorm), requirements gap analysis (use hm-requirements-analysis), dependency graph design (use hm-feature-ecosystem), or long-term maintainability scoring (use hm-roadmap-maintainability).
hf-l2-delegation-gates
by shynlee04Enforce pre-delegation authorization gates before agent dispatch. Use when setting up checkpoint gates, defining capability matrices, validating agent permissions, or approving a handoff boundary. NOT for orchestration execution, direct implementation, or generic task planning.
hf-l2-agents-md-sync
by shynlee04Detects and fixes drift between AGENTS.md documentation and actual codebase state. Scans source files and .opencode/ directories, compares claims against reality, produces a structured drift report, then applies targeted edits. Triggers on: 'sync agents md', 'update AGENTS.md', 'fix agents md drift', 'AGENTS.md out of date', 'check agent instruction drift'. NOT for generic documentation writing or README refreshes.
hf-l2-custom-tools-dev
by shynlee04This skill should be used when the user asks to "create a custom tool", "build an OpenCode plugin", "write a tool with Zod schema", "add a plugin hook", "create CLI script", "build a tool for agent", mentions tool() helper, Zod validation, plugin lifecycle, hooks (PreToolUse, PostToolUse), bin/ scripts, or needs guidance on OpenCode plugin SDK and custom tool architecture.
hivefiver-bilingual-tutor
by shynlee04Deliver HiveFiver v2 onboarding and instruction in EN/VI with equivalent structure, examples, and validation gates.
hf-l2-use-authoring-skills
by shynlee04This skill should be used when the user asks to "create a skill", "audit this skill", "refactor skills", "doctor agent skills", "check skill quality", "fix frontmatter", "skill pattern selection", "TDD for skills", "cross-platform skill compatibility", or "score skill quality".
hm-l2-completion-looping
by shynlee04Guardrail workflows against regression with non-completion detection and automatic loop-back. Use when a task must loop until verified complete, when guarding against premature success claims, when implementing self-verifying subagent dispatch, when agents report "done" but verification fails, when building autonomous loops that need completion gates, or when tasks keep failing silently. Even when the user says "make sure it actually works" or "verify before claiming done." Triggers: "loop until complete", "verify completion", "completion detection", "guardrail", "regression guard", "self-verifying", "autonomous loop", "completion gate". NOT for one-shot tasks or simple retry loops.
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.