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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get-td-event-log
by intelGet TDVM event log
get-td-quote
by intelGet TDVM Quote Information
blockptr-to-tdesc
by intelTranslate a Triton kernel from the deprecated block-pointer API (tl.make_block_ptr / tl.advance / tl.load(boundary_check=...)) into an equivalent kernel using the modern device-side tensor-descriptor API (tl.make_tensor_descriptor / desc.load / desc.store) for the Intel XPU backend. Use this skill whenever the user wants to migrate, convert, translate, port, modernize, or "update" a kernel from block pointers to tensor descriptors; whenever they mention tl.make_block_ptr or tl.advance and ask for a modern/non-deprecated equivalent; whenever they ask how to use tensor descriptors in a kernel that currently uses block pointers; or when they paste a kernel using block pointers and ask how to speed it up or make it use DPAS / 2D block I/O on Intel GPU (PVC/BMG). Produce the descriptor form the XPU backend can lower efficiently, not just any form that compiles.
xpu-pr-auto-labeling
by intelRules for automatically determining which disable_* labels to apply to a PR based on the file paths changed. Used by the auto-label workflow.
xpu-ops-pr-creation
by intelHow to create a pull request for the intel/torch-xpu-ops repository. Use when an agent has finished implementing a fix or feature and needs to prepare a branch and PR description that satisfies CI and review requirements.
oob-perf-analysis
by intelGenerate and analyze T1/T2/R roofline reports for PyTorch OOB workloads comparing Intel XPU and NVIDIA CUDA. Use when working with eager profiling artifacts, per-model reports, fleet summaries, graph consistency, or XPU-vs-CUDA software efficiency analysis.
tmux-long-tasks
by intelUse tmux to launch and manage long-running jobs that must survive beyond the bash tool timeout. Use this skill when the user mentions long-running commands, background tasks, training jobs, large builds, or any command expected to exceed the bash tool timeout.
performance-patterns
by intelDetect and fix x86/C/C++ performance patterns from source code or profiling output (perf, VTune, flamegraphs). Invoke when the user asks to optimize, review for performance, or write new SIMD/vectorized code — even without profiling data. Trigger on: serial accumulator loops, narrow SIMD (xmm/ymm that could be ymm/zmm), _mm* intrinsics, HITM/cmpxchg clusters, false sharing, missing restrict or vzeroupper, futex_wake/notify_all thundering herd, hot symbol inside a system library (.so) with a version gap, or any request to write a fast reduction, dot product, or CPU-dispatched function. Patterns: serial accumulator, TTAS spinlock, SIMD upconversion (zipper), false sharing, per-CPU stats, missing vzeroupper, missing restrict, cv-thundering-herd, mutex-to-rwlock, CPU dispatch, library version upgrade, fast CRC32C, known algorithms (Cosine Similarity, Hamming Distance, Jaccard Distance), SIMD sort (x86-simd-sort).
linux-kernel-oops
by intelExpert x86 Linux kernel crash/oops analyser. Trigger whenever a kernel crash is involved in any form: (1) inline — keywords "BUG:", "Oops:", "WARNING:", "Kernel panic", "Call Trace", "paging request"; (2) in a file — "I saved the crash/oops/dmesg in <file>", "oops.log", etc.; (3) in dmesg — "pull the crash from dmesg", "my system crashed, look at dmesg"; (4) via bug tracker — "Ubuntu/Fedora/Debian/kernel bug <N>", or Launchpad, bugzilla.kernel.org, bugs.debian.org, bugzilla.redhat.com URLs containing a kernel crash. Do not analyse kernel crashes without consulting this skill's primitives and flows — it is the authoritative source for x86 kernel crash triage.
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.