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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agentic-sales-spec
by v9aiSpec-Driven Development for /agentic-sales. INVOKE when the user runs `/agentic-sales-spec`, says 'spec this out' or 'write a spec' on agentic-sales work, or asks for clarification on a feature touching the agentic-sales architecture (graph identity / routing contract / worker pool / observability plane / audio guide content). Reads the agentic-sales constitution at `apps/ai-engineer-roadmap/specs/agentic-sales/{mission,tech-stack,roadmap}.md`, picks the next roadmap phase or takes a pasted requirement, runs ONE structured 3-axis AskUserQuestion interview (scope / decisions / context) with agentic-sales-flavored options, then writes `specs/agentic-sales/YYYY-MM-DD-<slug>/{requirements,plan,validation}.md` against the REAL agentic-sales contracts. Documents only — never writes feature code. Stops for human review after the triplet.
agentic-sales-sync
by v9aiDeterministic, text-only sync for the /agentic-sales audio guide. INVOKE when the user runs `/agentic-sales-sync`, or when the scheduled Claude Code web routine runs the agentic-sales daily sync (see specs/agentic-sales/SYNC-ROUTINE.md). Runs `node scripts/sync-agentic-sales.mjs` to reconcile the page-served `data/agentic-sales.json` with the authoritative narration in `data/audio/agentic-sales.script.md` (NO re-TTS, NO deploy, audio fields preserved), then: on no change reports 'in sync' and stops; on text-only drift opens ONE review PR via the GitHub MCP tools; on structural drift (chapter count/title mismatch) opens a reconcile PR/issue WITHOUT editing files. Never merges, never deploys, never runs the audio pipeline.
feature-spec
by v9aiINVOKE THIS SKILL when starting a new feature, planning a change before writing code, or when the user says 'spec this out', 'write a spec', or 'plan this feature' in agentic-sales. Grounds on the specs/ constitution, picks the next roadmap phase (or takes a pasted requirement), interviews the user, then writes specs/YYYY-MM-DD-<slug>/{requirements,plan,validation}.md against this repo's real contracts (GraphQL + codegen, Drizzle/D1, Apollo resolvers, Zod, LangGraph) and enforced gates. First step of the Spec-Driven loop (feature-spec → feature-implement → feature-validate).
feature-validate
by v9aiINVOKE THIS SKILL to verify a built feature against its specs/<feature>/validation.md — runs the enforced agentic-sales gates (pnpm strategy:check, codegen, db:migrate, lint, build, vitest, evals) and the manual walkthrough, then reports pass/fail with the real command output. Use when the user says 'validate the feature', 'run the gates', 'check the spec is done', or before opening a PR / pnpm run deploy. Final step of the Spec-Driven loop (feature-spec → feature-implement → feature-validate).
langsmith-dataset
by v9aiINVOKE THIS SKILL when creating evaluation datasets, uploading datasets to LangSmith, or managing existing datasets. Covers dataset types (final_response, single_step, trajectory, RAG), CLI management commands, SDK-based creation, and example management. Uses the langsmith CLI tool.
audio
by v9aiINVOKE THIS SKILL when the user pastes text and wants it optimized for an audio / listening / text-to-speech experience in agentic-sales — e.g. 'make this audio-ready', 'optimize for audio', '/audio', or 'turn this into a voiceover script'. Rewrites agentic-sales content (research-paper abstracts & synthesis, outreach emails, enrichment/SQL reports, or any pasted text) into a clean spoken script: strips markdown/tables/code, expands acronyms & symbols, verbalizes URLs/numbers/citations, resolves {{placeholders}}, and adds natural narration flow. Returns audio-ready text only — does NOT generate an audio file.
langsmith-trace
by v9aiINVOKE THIS SKILL when working with LangSmith tracing OR querying traces. Covers adding tracing to applications and querying/exporting trace data. Uses the langsmith CLI tool.
wrangler
by v9aiCloudflare Workers CLI for deploying, developing, and managing Workers, KV, R2, D1, Vectorize, Hyperdrive, Workers AI, Containers, Queues, Workflows, Pipelines, and Secrets Store. Load before running wrangler commands to ensure correct syntax and best practices. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
infra-spec
by v9aiSpec-Driven Development for the production agentic-sales infrastructure (LangGraph control plane / Cloudflare data plane / LangSmith observability plane). INVOKE when the user runs `/infra-spec`, says 'spec this out' or 'write a spec' on infra work, or asks for clarification on a change touching: a LangGraph graph / the `agentic_sales.registry.GRAPHS` registry / a state reducer / a checkpointer; a Cloudflare Pyodide worker under `services/*` or `apps/agentic-sales/workers/*`; the dispatcher routing contract (`route_for.py`); worker bindings (D1 / R2 / Queues / KV / Vectorize / Workers AI); the ai-gateway LLM cache; or the LangSmith tracer (`services/_shared/tracing/_tracing.py`). Reads the infra constitution at `specs/infra/{mission,tech-stack,roadmap}.md`, picks the next roadmap phase or takes a pasted requirement, runs ONE structured 3-axis AskUserQuestion interview (scope / decisions / context) with infra-flavored options, then writes `specs/infra/YYYY-MM-DD-<slug>/{requirements,plan,validation}.md` agains
langsmith-evaluator
by v9aiINVOKE THIS SKILL when building evaluation pipelines for LangSmith. Covers three core components: (1) Creating Evaluators - LLM-as-Judge, custom code; (2) Defining Run Functions - how to capture outputs and trajectories from your agent; (3) Running Evaluations - locally with evaluate() or auto-run via LangSmith. Uses the langsmith CLI tool.
feature-implement
by v9aiINVOKE THIS SKILL after a spec is approved, to build the feature by executing specs/<feature>/plan.md one numbered task group at a time, pausing for human review between groups. Use when the user says 'implement the spec', 'build this feature', or 'work the plan' in agentic-sales. Runs the matching gate after each group (pnpm codegen after schema, pnpm db:generate/migrate after src/db/schema.ts), keeps Drizzle-only SQL, admin-guards prod mutations, and never edits src/__generated__/. Second step of the Spec-Driven loop (feature-spec → feature-implement → feature-validate).
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.