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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launch-with-slurm
by mlc-aiReference for launching jobs inside a SLURM allocation via srun (single-node or multi-node). Use whenever $SLURM_JOB_ID is set and work needs to run on the allocated compute — from direct user requests ("run on the cluster", "launch on slurm", "train across N nodes", "dispatch the job") OR from within another skill's workflow (e.g., validate-correctness running validation on the allocation, add-new-model reaching pp=2/ep=2). Covers how to read the allocation context from $SLURM_JOB_ID via scontrol, the srun flags that matter (-W 0, -N, -o, --open-mode, --nodelist), and gotchas like the executable bit requirement and distributed-aware output redirection.
add-new-model
by mlc-aiAdds support for a new MoE language model to PithTrain. Use when the user asks to "add support for model X", "implement model Y in pithtrain", "port model Z", or otherwise integrate a new MoE architecture. Scope covers the model file, all framework wiring (setup_model, apply_fsdp, test_fsdp), optional checkpoint conversion, and running training + inference tests from pp=1/ep=1 up to pp=2/ep=2.
capture-nsys-profile
by mlc-aiCapture a Nsight Systems (.nsys-rep) profile of a short PithTrain run for performance analysis. Use when the user asks to "capture an nsys profile", "profile training", or "grab an nsys trace", or wants to inspect kernel timelines / pipeline behavior / all-to-all overheads. Adaptive over pipeline-parallel (PP), expert-parallel (EP), context-parallel (CP), and sequence length; size the global batch so the pipeline reaches steady state without producing a multi-GB .nsys-rep. Run 5 warmup steps + 1 profiled step from a released checkpoint.
validate-correctness
by mlc-aiValidates that code changes do not break training correctness by comparing per-step loss curves between a base branch and the current feature branch. Use when user asks to "validate correctness", "check if changes break training", "compare loss curves", "run a regression test", or "verify my changes are correct". The user specifies which model to validate and at which parallelism mesh (PP/EP/CP) — do not infer this from git diff.
analyze-nsys-profile
by mlc-aiQuery a captured PithTrain Nsight Systems profile to measure compute/communication overlap, locate exposed comm by DualPipeV stage, and inspect per-rank stream behavior. Use when the user asks to "analyze an nsys profile", "check overlap quality", "find exposed comm", "which stage is the bottleneck", or any question that starts from an existing `.nsys-rep` file. Assumes the trace was already captured (see capture-nsys-profile); provides query primitives the agent composes for the specific question being asked.
setup-benchmark-inputs
by mlc-aiSet up the minimal set of artifacts (tokenized DCLM corpus shard + released HuggingFace checkpoint converted to DCP) required to benchmark, profile, or regression-test a MoE model in PithTrain. Use when the user asks to "prepare benchmark inputs", "set up the benchmark workspace", "download the DCLM shard", "fetch and convert the released checkpoint", "tokenize DCLM for DeepSeek/Qwen3", or when a downstream skill (capture-nsys-profile, validate-correctness, or any short canonical run) needs its workspace pre-populated. Produce `workspace/datasets/dclm-baseline/toktxt/<model>` and `workspace/checkpoints/<model>/torch-dcp/step-00000000`. Idempotent and safe to re-run.
launch-with-slurm
by mlc-aiReference for launching jobs inside a SLURM allocation via srun (single-node or multi-node). Use whenever $SLURM_JOB_ID is set and work needs to run on the allocated compute — from direct user requests ("run on the cluster", "launch on slurm", "train across N nodes", "dispatch the job") OR from within another skill's workflow (e.g., validate-correctness running validation on the allocation, add-new-model reaching pp=2/ep=2). Covers how to read the allocation context from $SLURM_JOB_ID via scontrol, the srun flags that matter (-W 0, -N, -o, --open-mode, --nodelist), and gotchas like the executable bit requirement and distributed-aware output redirection.
estimate-memory
by mlc-aiEstimate peak GPU memory for a DualPipeV training run. Use when the user asks to "estimate memory", "will this fit in memory", "how much GPU memory", "check if this OOMs", "memory for training X on Y GPUs", or mentions memory planning for a training configuration. Translates natural-language descriptions of hardware, model, and training setup into the exact CLI arguments for `python -m tools.memory_estimator`.
add-memory-prints
by mlc-aiAdd detailed memory profiling prints throughout the training framework. Instruments distributed setup, model creation, checkpoint loading, pipeline scheduling, per-layer activations, saved tensor profiling, expert MLP internals, and memory snapshot dumps. Use when user asks to "add memory prints", "instrument memory", "profile memory", "memory breakdown", or "debug memory".
add-new-model
by mlc-aiAdds support for a new MoE language model to PithTrain. Use when the user asks to "add support for model X", "implement model Y in pithtrain", "port model Z", or otherwise integrate a new MoE architecture. Scope covers the model file, all framework wiring (setup_model, apply_fsdp, test_fsdp), optional checkpoint conversion, and running training + inference tests from pp=1/ep=1 up to pp=2/ep=2.
analyze-nsys-profile
by mlc-aiQuery a captured PithTrain Nsight Systems profile to measure compute/communication overlap, locate exposed comm by DualPipeV stage, and inspect per-rank stream behavior. Use when the user asks to "analyze an nsys profile", "check overlap quality", "find exposed comm", "which stage is the bottleneck", or any question that starts from an existing `.nsys-rep` file. Assumes the trace was already captured (see capture-nsys-profile); provides query primitives the agent composes for the specific question being asked.
capture-nsys-profile
by mlc-aiCapture a Nsight Systems (.nsys-rep) profile of a short PithTrain run for performance analysis. Use when the user asks to "capture an nsys profile", "profile training", or "grab an nsys trace", or wants to inspect kernel timelines / pipeline behavior / all-to-all overheads. Adaptive over pipeline-parallel (PP), expert-parallel (EP), context-parallel (CP), and sequence length; size the global batch so the pipeline reaches steady state without producing a multi-GB .nsys-rep. Run 5 warmup steps + 1 profiled step from a released checkpoint.
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.