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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aoa-local-stack-bringup
by 8DionysusBring up a bounded local multi-service stack by rendering runtime truth, checking selector-aware host readiness, and launching through one explicit lifecycle entrypoint with a visible stop path. Use when profiles, presets, or overlays can change what starts and host readiness must be reviewed before launch. Do not use for remote deployment, pure diagnostics without launch intent, or generic infra-change work.
aoa-local-stack-bringup
by 8DionysusBring up a bounded local multi-service stack by rendering runtime truth, checking selector-aware host readiness, and launching through one explicit lifecycle entrypoint with a visible stop path. Use when profiles, presets, or overlays can change what starts and host readiness must be reviewed before launch. Do not use for remote deployment, pure diagnostics without launch intent, or generic infra-change work.
titan-receipt
by 8DionysusCreate, validate, note, or close Titan session receipts as witnesses rather than final truth. Use when a Titan service-cohort route needs this explicit bounded step. Do not use for hidden background agents, silent mutation, unreviewed proof sovereignty, or memory canonization without owner confirmation.
aoa-checkpoint-closeout-bridge
by 8DionysusBridge provisional checkpoint evidence into one explicit reviewed closeout execution chain without turning notes into final harvest, progression, or quest authority. Use when a reviewed session artifact exists, checkpoint notes or closeout handoffs already carry focus hints, and the next honest move is to reread the reviewed artifact while driving donor harvest, progression lift, and quest harvest in that fixed order. Do not use for mid-session collection only, for hidden execution inside `aoa closeout run`, or when the request tries to mint final verdicts from checkpoint notes without reviewed evidence.
atm10-source-of-truth-check
by 8DionysusApply the aoa-source-of-truth-check workflow inside an atm10-* repository using repo-relative public-surface roles, document maps, canonical-file patterns, entrypoint trimming, and local review posture. Use when contributors need a thin project overlay to identify authoritative ATM10 docs or separate active, archived, generated, local-only, and runtime-adjacent surfaces. Do not use when the task is broader policy design, purely code-local, runtime authority, or better handled by the base skill without local adaptation.
aoa-decision-correct
by 8DionysusCorrect, supersede, reindex, or repair graph-coverage drift in AoA decision records by checking graph issues, editing source notes first, rebuilding repo-local indexes, and refreshing the workspace decision graph. Use when a decision note, index metadata, source-surface list, status, supersession, generated index, unknown surface, or graph packet is stale or wrong. Do not use for pure lookup or for creating a new decision note.
aoa-core-logic-boundary
by 8DionysusSeparate reusable core logic from glue, orchestration, and infrastructure detail. Use when stable rules are mixed with wiring, the same logic repeats in several places, or reviews are muddy because the responsibility center is unclear. Do not use for tiny isolated fixes or when the real task is introducing a port or adapter around a concrete dependency.
aoa-invariant-coverage-audit
by 8DionysusAudit whether existing tests and checks actually constrain the stable invariants that matter. Use when you need to judge if current coverage proves a rule or only repeats examples, and you want the smallest bounded follow-up gaps. Do not use when the invariant itself is still undefined or when the real task is to author new invariants rather than audit coverage.
aoa-property-invariants
by 8DionysusExpress stable system or domain truths as invariant-oriented tests or checks rather than only fixed examples. Use when correctness depends on behavior that should hold across many inputs or states, such as monotonicity, idempotency, conservation, or structural rules. Do not use when no meaningful invariant is known yet or when the real task is auditing existing coverage instead of writing the properties.
aoa-bounded-context-map
by 8DionysusClarify system or domain boundaries, name contexts, and surface interfaces so changes stay semantically scoped. Use when naming is overloaded, responsibilities are mixed, or a task needs a cleaner boundary before coding. Do not use for tiny local edits or when the boundary is already clear and the real task is contract validation.
titan-appserver-bridge
by 8DionysusOperate the Titan app-server bridge as inspectable thread, turn, event, approval, replay, and metrics state without hidden execution. Use when a Titan service-cohort route needs this explicit bounded step. Do not use for hidden background agents, silent mutation, unreviewed proof sovereignty, or memory canonization without owner confirmation.
aoa-safe-infra-change
by 8DionysusMake bounded infrastructure, configuration, service, or operational changes with explicit risk framing, proportional verification, and rollback thinking. Use when a change has runtime or deployment implications and needs stronger discipline than a normal code edit. Do not use for purely local code changes, or when the main need is approval classification or preview-first execution.
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.