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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blocked-task-state
by neomjsAuthoritative protocol for signaling blocked or input-required task states. Mandates targeted A2A pings using the Task.state envelope rather than global capacity broadcasts. Triggers: Use this skill whenever your execution becomes blocked, requires explicit operator input, or encounters a failure that halts progress.
epic-review
by neomjsAuthoritative protocol for pre-work review of epics. Six-stage gating chain — roadmap fit, approach elegance, source discussion mapping, sub-structure coherence, prescription layer, avoided-traps completeness — posted as a structured comment on the epic ticket. Per-agent-per-epic one-shot; subsequent sub pickups cite the prior review. Triggers: Use this skill when an agent is about to pick up its first sub from an unreviewed epic (per model-identity). Also use when a user explicitly requests an epic review, or when an epic is freshly filed and a reviewer pre-validates before any sub pickup begins.
epic-resolution
by neomjsCloseout protocol for parent epics — answers "we resolved all epic subs, are we done now?" with a structured matrix + verdict recommendation (close / keep open / create missing subs / retire-supersede). Sibling to epic-review (which handles entry); this is the exit gate. Triggers: Use this skill when the last required sub of an epic closes, when a team member claims an epic is complete or substrate-side complete, before closing an epic as COMPLETED, or when a peer broadcasts an epic-readiness signal that needs reconciliation against parent ACs.
epic-create
by neomjsAuthoritative protocol for authoring an Epic body — the creation-side dual of `ticket-create` + entry partner of `epic-review`/`epic-resolution`. Enforces: epic body = problem-scope + intended-solution (NOT pseudo-subs / ACs); ACs live in the SUB tickets; subs are LINKED via `update_issue_relationship` + added incrementally; the body MUST NOT hardcode a sub-list (it stales out → FAIL). Triggers: before creating an Epic (a parent issue labeled `epic`) via `create_issue`, or before decomposing one into sub-tickets.
ticket-triage
by neomjsAuthoritative protocol for maintainer-side label triage of unlabeled contributor tickets. Codifies the social contract for what happens when a ticket arrives without `ai`, primary (`bug`/`enhancement`/`epic`), or secondary labels. Triggers: Use this skill when an agent with maintainer permissions (`WRITE` permission or higher) encounters a ticket lacking `ai`, primary, or secondary labels — typically authored by a non-maintainer contributor or a lower-privileged agent who couldn't apply labels at create-time.
turn-memory-pre-flight
by neomjsAuthoritative protocol for verifying the correct placement and impact of new agentic memory substrate additions. Triggers: Use before inserting or mutating turn-loaded/skill-loaded memory substrate (`AGENTS.md`, `AGENTS_ATLAS.md`, `.agents/skills/**`, `.codex/CODEX.md`, `.claude/CLAUDE.md`, `.agents/ANTIGRAVITY_RULES.md`) to prevent future-session bias.
ticket-create
by neomjsAuthoritative protocol for creating Neo.mjs GitHub issues. Enforces duplicate sweep, Fat Ticket body structure, strict label rules, title hygiene, and the six-stage challenge chain at creation time. CRITICAL: Do NOT run default `npx playwright test` to verify issues; Neo uses multiple custom playwright configs (e.g., unit, e2e) which must be explicitly targeted. Use immediately before calling the create_issue MCP tool. Triggers: Use this skill before any invocation of the create_issue MCP tool. This is the creation-side dual of ticket-intake (which consumes existing tickets).
ticket-intake
by neomjsAuthoritative protocol defining the "Pre-Execution Reflection Gate". Mandates architectural validation, negative ROI calculation, and duplicate sweeps before an agent is permitted to begin working on a GitHub Issue. Triggers: Use this skill immediately when assigned a new ticket, before checking out a branch or writing any codebase modifications.
identity-firewall
by neomjsThe L2 Channel Separation and Prompt Firewall defense mechanisms to prevent injection attacks and Helpful Assistant regression.
ideation-sandbox
by neomjsSafely propose architectural features, unknown unknowns, and brainstorm ideas natively in GitHub Discussions. Triggers: Use this skill when the user asks to brainstorm an architecture change, proposes a highly exploratory / undefined technical idea, or as auto-fire trigger for §5.2 Step 2.5 Architectural Step-Back on high-blast-radius proposals before [RESOLVED_TO_AC] / [GRADUATED_TO_TICKET] graduation.
lead-role
by neomjsSwitch into relaxed-planning + dialogue-first mindset when delegated lead role for coordination. Suspends Auto Mode velocity-bias for the duration. Triggers: Use this skill IMMEDIATELY when the user delegates lead with explicit phrases ("you take the lead", "coordinate the team", "lead this phase", "drive the next planning step", "chief-architect" when scope is swarm/substrate/roadmap/multi-ticket), OR when the Mailbox Check Protocol surfaces a valid `lead-role-baton`, OR when you have just authored a substrate-shaped ticket about to enter implementation, OR via direct /lead-role invocation.
whitebox-e2e
by neomjsStandardized guide and protocol for authoring robust Whitebox End-to-End tests using the Neural Link Playwright fixture. CRITICAL: Neo.mjs uses Playwright in a highly custom way. Standard Playwright patterns will fail. Triggers: Use this skill before writing, modifying, or executing Playwright End-to-End tests, or if the user asks you to write an E2E test, add end-to-end coverage, or test a component holistically.
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.