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.
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Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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mlmm-architecture
by t-0hmuraWhere the source code lives in `mlmm-toolkit`. 6 physical layer directories (`cli` / `workflows` / `domain` / `backends` / `io` / `core`) + 3 repo-internal forks (`pysisyphus` / `thermoanalysis` / `hessian_ff`). Tells an agent which directory to grep for a given concern (Click option, ONIOM stage runner, MLIP backend, hessian-ff analytical MM Hessian, output writer, chemistry default, link-atom math) before touching code. TRIGGER on questions like "where is X implemented", "which file defines flag Y", "how is the repo organised", "what's safe to refactor". SKIP for usage questions — those belong to `mlmm-cli` / `-overview`.
mlmm-workflows-output
by t-0hmuraOutput parsing and multi-step workflow selection for mlmm-toolkit — `summary.json` schema, R/TS/P/IM canonical paths, `bond_changes` interpretation, energy-diagram conventions, and the cluster + 1-step / multistep / scan-list / endpoint-MEP / TS-only / DFT//ML/MM recipes used to extract numerical results. TRIGGER on output parsing (`summary.json`, `result.json`, `seg_NN/`), extracting barriers / ΔE / Gibbs for a paper, or choosing between multi-input / scan-list / endpoint-MEP / TS-only modes. SKIP for single-subcommand syntax (CLI skill) or install / HPC questions.
mlmm-structure-io
by t-0hmuraPDB / XYZ / GJF / Amber parm7+rst7 input-file reference for mlmm-toolkit, plus the charge / multiplicity decision workflow and the B-factor layer encoding (ML=0 / movable-MM=10 / frozen-MM=20) that defines a three-layer ONIOM system from a single prepared PDB. TRIGGER on editing or inspecting a structure file, deciding `-q` / `-l` / `-m`, interpreting residue / charge / spin in an input, or assigning B-factor layers. SKIP for subcommand syntax, output parsing, install, or HPC questions.
mlmm-overview
by t-0hmuraOrientation for mlmm-toolkit — what it is, when to use it, and how it differs from generic ML/MM MD packages (three-layer ML/movable-MM/frozen ONIOM via PDB B-factor encoding, analytical hessian_ff full-system Hessian, microiteration with link-atom Jacobian coupling, AmberTools-driven MM parameterization). TRIGGER on first-touch / "what is mlmm-toolkit" / "should I use it" / "how does it compare to OpenMM / GROMACS / Sire" questions. SKIP when the user has already named a subcommand, an install issue, an output file, a structure format, or a cluster — sibling skills cover those.
mlmm-mcp
by t-0hmuraHow to drive `mlmm-toolkit` from an MCP-speaking agent (Claude Desktop / Claude Code / Cursor / Codeium / custom Python or TypeScript MCP SDK clients) via the bundled `mlmm-mcp` server. Lists the 22 MCP tools (one per CLI subcommand, including mlmm-specific topology / ONIOM-layer / ONIOM-input tools) and the shared `SubcmdResult` schema. TRIGGER on questions about MCP setup, agent integration, tool names, return-value shape, or "how does an agent invoke mlmm". SKIP for direct CLI usage — that's `mlmm-cli`.
mlmm-install-backends
by t-0hmuraInstall recipes for mlmm-toolkit core + MLIP (UMA / MACE / Orb / AIMNet2) / DFT / xTB / AmberTools backends, with CUDA / PyTorch / e3nn / aarch64 quirks. TRIGGER on install / setup / `pip install` / `conda env` / `ImportError` / CUDA-version mismatch / "GPU not detected" / `huggingface` auth / e3nn version conflict / AmberTools / `tleap` not-found questions. SKIP when mlmm imports cleanly and the user is invoking subcommands — the CLI skill covers usage.
mlmm-hpc
by t-0hmuraPBS (Torque / PBSPro) and SLURM submission for mlmm-toolkit — generic preamble templates with placeholders, walltime budgeting, CPU vs GPU choice, job monitoring, and the dynamic-dispatch (`flock` + `pbsdsh`) recipe in `dynamic-dispatch.md`. TRIGGER on cluster submission / `qsub` / `sbatch` / walltime / preamble / multi-job dispatch / `pbsdsh` / `flock` / many-system batch questions. SKIP for local single-machine runs, install setup, or output parsing. Note: mlmm-toolkit is single-GPU per job and has no `--resume` / `--workers` interface.
mlmm-env-detect
by t-0hmuraFallback skill for detecting the current compute environment (local / PBS / SLURM, CPU architecture, GPU, CUDA, conda env). Use only when the env is unknown — other skills assume placeholders are filled by the user or context.
mlmm-cli
by t-0hmuraPer-subcommand reference for mlmm-toolkit's 22 CLI subcommands (extract / mm-parm / define-layer / opt / tsopt / freq / irc / dft / scan / path-search / all / …). SKILL.md is the orientation + cross-cutting flag conventions + canonical recipes; each subcommand has its own md (extract.md / mm-parm.md / tsopt.md / …) for flags, validation, and caveats. TRIGGER on questions about a specific subcommand, flag, or shell invocation. SKIP for install / HPC / output-parsing / structure-format-editing / overview questions.
pdb2reaction-workflows-output
by t-0hmuraOutput parsing and multi-step workflow selection for pdb2reaction — `summary.json` schema, `seg_NN/` layout, R/TS/P/IM canonical paths, `bond_changes` interpretation, and the cluster + 1-step / multistep / scan-list / endpoint-MEP / TS-only / DFT//MLIP recipes plus energy-diagram extraction. TRIGGER on output parsing (`summary.json`, `result.json`, `seg_NN/`), extracting barriers / ΔE / Gibbs for a paper, or choosing between multi-input / scan-list / endpoint-MEP / TS-only modes. SKIP for single-subcommand syntax (CLI skill) or install / HPC questions.
pdb2reaction-architecture
by t-0hmuraWhere the source code lives in `pdb2reaction`. 6 physical layer directories (`cli` / `workflows` / `domain` / `backends` / `io` / `core`) + 2 repo-internal forks (`pysisyphus` / `thermoanalysis`). Tells an agent which directory to grep for a given concern (Click option, stage runner, MLIP backend, output writer, chemistry default, link-atom math) before touching code. TRIGGER on questions like "where is X implemented", "which file defines flag Y", "how is the repo organised", "what's safe to refactor". SKIP for usage questions — those belong to `pdb2reaction-cli` / `-overview`.
pdb2reaction-cli
by t-0hmuraPer-subcommand reference for pdb2reaction's 18 CLI subcommands (extract / path-search / tsopt / freq / irc / dft / scan / opt / sp / all / …). SKILL.md is a 1-line input→output cheatsheet; each subcommand also has its own md (`extract.md` / `tsopt.md` / …) for flags, validation, caveats. See `freeze-atoms.md` for cluster-boundary frozen-atom mechanics. TRIGGER on questions about a specific subcommand, flag, or shell invocation. SKIP for install / HPC / output-parsing / structure-format-editing questions.
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.