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.
Querying local SQLite index...
galaxy-gen
by coilyco-flight-deckPointer to the coilysiren/galaxy-gen repo. Triggers - galaxy-gen, procedural galaxy simulation, Rust WASM, galaxy-gen.coilysiren.me.
coding-galaxy-gen-astrophysics
by coilyco-flight-deckGalaxy-scale astrophysics for the procedural galaxy sim. Morphology, mass distribution, rotation curves, star formation, stellar populations, AGN, mergers. Triggers - galaxy generation, galaxy morphology, spiral arms, elliptical, rotation curve, dark matter halo, IMF, HR diagram, star formation, AGN, supermassive black hole, galaxy merger, Hubble sequence, Tully-Fisher.
coding-galaxy-gen-cosmology
by coilyco-flight-deckCosmology and relativity for the procedural galaxy sim. Universe-scale physics - Hubble flow, FLRW, redshift, dark sector, gravitational physics, CMB. Sister to the galaxy-scale skill. Triggers - cosmology, FLRW, Hubble's law, redshift, dark energy, dark matter, CMB, cosmic web, gravitational lensing, Schwarzschild, black hole, general relativity, lookback time, comoving distance.
repo-eco-mods-public
by coilyco-flight-deckPublic C# gameplay mods for Eco (Strange Loop Games): BunWulf professions family, DirectCarbonCapture, MinesQuarries, ShopBoat, EcoNil, WorldCounter. Triggers - eco-mods-public, bunwulf-professions, directcarboncapture, minesquarries, public-eco-mods, shopboat
tooling-autonomous-pickup
by coilyco-flight-deckDrain repo-recall's on-disk dispatch queue into Claude Desktop "Start locally" cards. For each unhandled `~/.repo-recall/dispatch/<repo>/*.md`, calls `mcp__scheduled-tasks__create_scheduled_task` with a near-future fireAt, then writes a `.handled` sidecar. Pairs with `recall-dispatch` to close the autonomous-engineering loop. Triggers - autonomous pickup, pickup dispatches, drain dispatch queue, fan out dispatches, run AFK pickup, dispatch queue, lights-out pickup, sweep dispatches.
ops-investigation-k3s-pod-eviction
by coilyco-flight-deckDiagnose silent pod evictions on the kai-server k3s cluster. Walks events, describe output, node taints, OOM kills, ephemeral-storage pressure, image-pull failures, and QoS-class evictions in a top-down order. Aliases - k3s eviction, pod eviction, pod evicted, why was my pod evicted, kubelet eviction, k3s pod eviction, k3s-pod-eviction-diagnosis, evicted pod debug, pod disappeared, pod restart loop.
ops-investigation-k3s-upgrade-homelab
by coilyco-flight-deckInstructions for updating the k3s homelab cluster one machine at a time while preserving custom systemd ExecStart arguments.
ops-eng-o2r-grafana
by coilyco-flight-deckUse when working with the Tempo + Grafana backend for otel-a2a-relay (o2r). Names the specific Grafana surface for each question (overview vs LUCA-flow vs Tempo Explore vs service graph), with direct dashboard URLs, TraceQL snippets, and the dockerized stack lifecycle. Triggers - grafana, tempo, o2r grafana, o2r tempo, o2r-overview, luca-flow dashboard, traceql, service graph, tempo explore, span metrics, o2r-tempo-harness, tempo-up.
ops-eng-o2r-agent-channel
by coilyco-flight-deckUse when working with the Agent Channel coordination layer of otel-a2a-relay (o2r). Encodes the default reflex: a 4-char id in a sentence about channels is a live channel - look it up via the API, do not grep the repo or ask if the id is a dictation mangle. Triggers - o2r, otel-a2a-relay, agent channel, agent-channel, channel id, channels-protocol, 4-char id, coily channel, VHGC-shaped.
ops-eng-o2r-phoenix
by coilyco-flight-deckUse when working with the Arize Phoenix backend for otel-a2a-relay (o2r). Names the specific Phoenix UI surface for each question (waterfall vs JSON tree vs session list vs agent topology), with direct URLs and click paths. Triggers - phoenix, arize phoenix, o2r phoenix, phoenix sessions, phoenix trace, where is the waterfall, agent graph, openinference, o2r-harness, o2r-view, o2r-phoenix-bootstrap.
otel-a2a-relay
by coilyco-flight-deckPointer to the coilysiren/otel-a2a-relay repo. Triggers - otel-a2a-relay, o2r, OAR (dictation of o2r), agent channel, A2A relay, luca-flow.
autonomous-dispatch
by coilyco-flight-deckLong-horizon autonomous dispatch planner. Triages the org backlog, splits oversized tickets, proposes rehoming/dedup/stale-close, scores tickets for AFK-ability, surfaces structural-context asks for the operator, then fires each survivor as a headless `coily dispatch` session. Aliases - autonomous dispatch, autonomous engineering, AFK queue, lights-out queue, AFK dispatch, plan the AFK run, queue autonomous work, run the lights-out factory, what should the bots work on.
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.