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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onboard
by saml212One-time researcher onboarding interview that compiles a `taste/` corpus (SOUL.md, STYLE.md, METHODOLOGY.md, DISMISSALS.md, MEMORY.md, INDEX.md) capturing the researcher's intellectual taste, beliefs, and values. Auto-injects INDEX.md into every future session via SessionStart. Triggers on first install ("no taste corpus found"), explicit `/onboard`, `/onboard --deep` for Tier 2 voice laddering, or `/onboard --redo` / `/onboard --section <name>` to refresh.
upstream-contribute
by saml212Scan the current session for harness-level patterns that would be useful to other rockie users — pruning fixes, small skill improvements, new hooks, cross-discipline-useful capabilities, memory-schema upgrades — strip the project-specific specificity, and dispatch a writer sub-agent that forks `saml212/rockie-claude`, applies the generalized change on a `contrib/<slug>` branch, runs `tests/smoke-test.sh`, and opens a PR. Never auto-merges; the maintainers review. Triggers when the user says "upstream this", "contribute back", "anything generalizable here", or after `/clean` emits its post-audit nudge. Sibling to `/propose-harness-change`, but scoped at the public upstream rather than a private fork.
powerpoint
by saml212Create validated PowerPoint `.pptx` deck artifacts through the lean Rockie PPTAgent wrapper and emit them to the lab.
propose-harness-change
by saml212Package a harness-level improvement (a new hook, a fixed script, an improved skill) as a reviewed, verified patch — optionally openable as a PR against the rockie upstream repo. Uses Generator/Verifier/Updater role separation so the proposing agent never auto-commits; a fresh-context verifier plus the smoke test must agree, and the human signs off before anything is written to the user's rockie checkout or pushed anywhere. Triggers when the user says "upstream that", "propose a harness change", "write a PR for rockie", or when a recent `[LEARN harness-upstream]` block is waiting.
autopilot
by saml212Continuous-operation mode for rockie — runs the experiment queue autonomously, using Zero-Cost Monitoring ($0 LLM cost during training), anti-burn exponential cooldown on failures, and ntfy to wake the human only when a decision is needed. Use when you want agent-driven research to proceed for days without human input. Not appropriate for unproven projects — only enable after you have a populated queue, budget ceilings, and a working launcher.
excel
by saml212Create validated Excel `.xlsx` workbook artifacts with XlsxWriter, formula-injection protection, and Rockie artifact emission.
gpu-custom-setup
by saml212One-time onboarding flow for users who set ROCKIE_GPU_MODE=custom (i.e., they have their own GPU setup — own AWS account, on-prem cluster, SSH tunnel to a workstation, university HPC, custom orchestration — instead of using Rockie's deidentified GPU router). Trigger this when (1) the user mentions GPU/training/provisioning, (2) `echo $ROCKIE_GPU_MODE` returns `custom`, AND (3) `.claude/gpu-custom.md` doesn't exist or is empty. Walks the user through their auth/provision/connect/monitor/terminate flow and saves it to .claude/gpu-custom.md so future agent sessions reuse the saved flow without re-asking. Run this AT MOST ONCE per project.
gpu-custom
by saml212Runtime skill for users in ROCKIE_GPU_MODE=custom — invoked when the user (or agent) needs to do anything GPU-related (provision, connect, check status, check cost, terminate) in a project where Rockie's GPU router is bypassed in favor of the user's own setup. Reads `.claude/gpu-custom.md` (populated by /gpu-custom-setup) for the user's documented flow and follows it. Replaces the deidentified `rockie-gpu` surface and /gpu-spend in custom mode — those route to gpu.py which exits gracefully when ROCKIE_GPU_MODE is not 'router'. If `.claude/gpu-custom.md` doesn't exist, redirect to /gpu-custom-setup first.
gpu-spend
by saml212When the user (or you) needs to know GPU spend — "what's my burn rate?", "how much have I spent this week?", "is anything still running?", "am I close to budget?", "what's running idle?" — invoke this. Wraps `rockie-gpu spent --json` (the deidentified Rockie-GPU spend surface) for accurate, reconciled numbers, then summarizes for the user. `rockie-gpu` is the single GPU surface: it never names the underlying compute supplier and never exposes a supplier API key. **Custom-mode users:** if `ROCKIE_GPU_MODE=custom`, invoke `/gpu-custom` instead — `rockie-gpu` is bypassed in that mode.
paper
by saml212Write submission-grade research papers end to end inside a Rockie lab, the way a careful human researcher does — not generic LLM filler. Three entry points. /lit-review pulls and ranks a corpus and persists a human reading list Note plus a machine-readable index Note. /paper-draft produces a brief, a page-budgeted outline, per-section drafts, an adversarial review gauntlet (attack, defense, rebuttal, style, format), and a final AI-vs-human detector gate. /publish assembles a downloadable bundle, lands it as a lab Note, and optionally exports to GitHub or Hugging Face. Triggers on "write a paper", "lit review", "literature review", "draft the paper", "review my paper", "run the gauntlet on this draft", "publish the paper", "submit to <venue>", "/lit-review", "/paper-draft", "/publish".
budget-term-sheet
by saml212Build a pre-deploy Rockie GPU budget term sheet before any Rocky-originated experiment submit. Trigger words "/budget-term-sheet", "quote the GPU budget", "show me the term sheet", or any workflow that is about to call `/experiment` for GPU / torch / triton / training / weight-download work.
experiment
by saml212Run a materials-science / ML compute job on Rockie GPU capacity. Trigger words "run experiment", "submit job", "/experiment". Picks the right GPU type and count from a natural-language description (DFT for QE/VASP/ABINIT, MD for GROMACS/LAMMPS/OpenMM, training for PyTorch/JAX), generates the script, routes Rockie-originated submits through `/budget-term-sheet` plus `runtime/submit.py`, polls status, streams logs, and surfaces the final artifacts. Use this for anything that needs a GPU — single A100 up to multi-pod B200 clusters.
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.