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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fp8-gemm-tuning-sglang-aiter
by AMD-AGIUse when trying to optimize end-to-end SGLang performance with gemm tuning for FP8 models on AMD HIP/ROCm by replacing the default Triton GEMM backend with a tuned Composable Kernel (CK) path through aiter; this skill is the verified playbook for that entire process, using FP8 block-wise GEMM (gemm_a8w8_blockscale) as the primary worked example—GEMM shape/dispatch logging in SGLang, CK composable-kernel tuning, and AITER_CONFIG_GEMM_A8W8_BLOCKSCALE CSV integration. FP8 blockscale and bpreshuffle should also apply by switch the place for dumping gemm and the ck tool used for tuning.
pytorch2flydsl-translation
by AMD-AGIUse when translating PyTorch GPU kernels to FlyDSL. Provides API reference, translation guides, and strategy for mapping PyTorch ops to FlyDSL equivalents.
hip
by AMD-AGIUse when generating a fixed test harness for a HIP / CUDA / CK / HSACO GPU kernel under the v3 GEAK preprocess pipeline. Covers harness CLI contract, the three HIP build shapes (pybind11, standalone make, raw hipcc), the COMMANDMENT wrapper-script rule, --iterations argparse, and the GPU-RNG-pollution pitfall that rocprofv3 punishes.
flydsl
by AMD-AGIUse when working with FlyDSL kernels (`@flyc.kernel` / `flydsl.compiler`) on AMD GPUs. Covers three complementary workflows: writing new tile-programmed kernels, optimizing existing kernels for performance, and debugging correctness issues (NaN, wrong results, compilation errors, hangs).
triton
by AMD-AGIUse when generating a fixed test harness for a Triton (@triton.jit) GPU kernel under the v3 GEAK preprocess pipeline. Covers harness CLI contract, Triton-specific entry-point detection, three-tier shape lists, --iterations argparse rule, and the GPU-RNG-pollution pitfall that rocprofv3 punishes.
pytorch2flydsl-translation
by AMD-AGIUse when translating PyTorch GPU kernels to FlyDSL. Provides API reference, translation guides, and strategy for mapping PyTorch ops to FlyDSL equivalents.
fp8-gemm-tuning-sglang-aiter
by AMD-AGIUse when trying to optimize end-to-end SGLang performance with gemm tuning for FP8 models on AMD HIP/ROCm by replacing the default Triton GEMM backend with a tuned Composable Kernel (CK) path through aiter; this skill is the verified playbook for that entire process, using FP8 block-wise GEMM (gemm_a8w8_blockscale) as the primary worked example—GEMM shape/dispatch logging in SGLang, CK composable-kernel tuning, and AITER_CONFIG_GEMM_A8W8_BLOCKSCALE CSV integration. FP8 blockscale and bpreshuffle should also apply by switch the place for dumping gemm and the ck tool used for tuning.
flydsl
by AMD-AGIUse when working with FlyDSL kernels (`@flyc.kernel` / `flydsl.compiler`) on AMD GPUs. Covers three complementary workflows: writing new tile-programmed kernels, optimizing existing kernels for performance, and debugging correctness issues (NaN, wrong results, compilation errors, hangs).
hip
by AMD-AGIUse when generating a fixed test harness for a HIP / CUDA / CK / HSACO GPU kernel under the v3 GEAK preprocess pipeline. Covers harness CLI contract, the three HIP build shapes (pybind11, standalone make, raw hipcc), the COMMANDMENT wrapper-script rule, --iterations argparse, and the GPU-RNG-pollution pitfall that rocprofv3 punishes.
triton
by AMD-AGIUse when generating a fixed test harness for a Triton (@triton.jit) GPU kernel under the v3 GEAK preprocess pipeline. Covers harness CLI contract, Triton-specific entry-point detection, three-tier shape lists, --iterations argparse rule, and the GPU-RNG-pollution pitfall that rocprofv3 punishes.
backend-patch-explorer
by AMD-AGIInventory and explain the patch (monkey-patch) optimizations Primus layers over upstream training backends such as Megatron-LM, TorchTitan, and MaxText, by reading the current repository code only. Use when the user asks which patches a backend has, wants a customer-facing patch table, asks how a specific patch works (for example deepep or DeepEP), or wants guidance to port a Primus patch into their own upstream framework. Read-only; no training or cluster commands.
backend-gap-report
by AMD-AGICompare a Primus backend against an upstream repository or reference, verify git state, dependencies, directory changes, and integration coupling, then generate comparison reports, dashboard metadata, and a deployable dashboard index. Also owns the shared Primus engineering dashboard under `tools/backend_gap_report/`, which surfaces both backend-gap reports and weekly engineering reports as first-class sections. Use when comparing TorchTitan, Megatron, or other Primus backends with upstream branches, tags, or releases, or when integrating weekly engineering reports into the shared dashboard.
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.