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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auto-gpu-kernel
by drunkcodingScaffold and operate an autonomous Triton GPU-kernel optimization loop on FlashInfer-Bench + Modal, modeled on the MLSys-2026 DSA contest winner (Dogacel/auto-gpu-kernel). Provides a ready-to-run template (CLAUDE.md, .claude/commands/{optimize,benchmark,log-experiment}, .claude/agents/{profiler,research,workload-inspector}, Modal benchmark + paired A/B harness, packed-solution scripts), plus customization guidance for retargeting to a different FlashInfer-Bench kernel definition. Use when the user wants to (1) bootstrap an autonomous Triton-kernel optimizer for any FlashInfer-Bench kernel, (2) set up an experiment-logged optimization loop with sub-agents for profiling, workload inspection, and plateau-breaking research, (3) run cloud-only (no local GPU) GPU benchmarking via Modal B200, or (4) reproduce or adapt the auto-gpu-kernel architecture for FlashInfer competition tracks or similar kernel-optimization workflows.
academic-grant-proposal
by drunkcodingWrite academic grant proposals for major funders — NSF (CAREER, standard, CRII), NIH (R01, R21, K-series, F-series), ERC (Starting, Consolidator, Advanced), EU Horizon Europe, DARPA/DoD/DOE BAAs, and UKRI/EPSRC/Royal Society. Covers section drafting (Specific Aims, Project Description, Broader Impacts, Excellence/Impact/Implementation, Heilmeier Catechism), mock-reviewer critique against funder criteria, budget and budget justification, biosketches, Data Management Plans, and responses to reviewer critique on resubmission. Discipline focus: CS / Systems / ML, with examples aligned to compute-heavy research. Use when asked to draft, review, critique, revise, or budget any academic grant proposal, white paper, pre-proposal, or LOI.
academic-rebuttal
by drunkcodingWrite conference paper rebuttals and author responses that effectively counter incorrect or unreasonable reviewer comments. Targets systems venues (OSDI, NSDI, SIGCOMM, MOBICOM, SOSP, ASPLOS, EuroSys, USENIX Security, CCS, PLDI) on HotCRP and ML venues (NeurIPS, ICML, ICLR, AAAI) on OpenReview. Core focus: identify and resolve reviewer false impressions with evidence-backed, firm-but-professional corrections. Covers rebuttal triage, false impression taxonomy (8 types with resolution playbooks), venue-specific constraints, response structure patterns, and ready-to-use phrase templates. Use when asked to write, draft, review, or improve a conference paper rebuttal, author response, or response to reviewer comments.
academic-reviewer
by drunkcodingReview systems conference papers for OSDI, NSDI, SIGCOMM, MOBICOM, SOSP, and FAST. Produces structured HotCRP-style reviews with paper summary, strengths, weaknesses, detailed comments, questions for authors, and overall merit/confidence scoring. Covers evaluation criteria (novelty, soundness, significance, evaluation quality, clarity, relevance, reproducibility), venue-specific expectations, common paper weaknesses, constructive feedback tone, one-shot revision reviews, and reviewer ethics. Use when asked to review, critique, or provide feedback on a systems conference paper submission.
cuda-tutor-setup
by drunkcodingGenerate an Obsidian-format CUDA StudyVault for learning NVIDIA's GPU programming stack. Three modes: (1) Curriculum mode (default, no source files needed) builds a prereq-ordered vault around the 6-topic learning path: CUDA kernels (threads/blocks/warps, memory hierarchy, TMA, WGMMA, cp.async), CUTLASS + CuTe (Layout/Tensor, MMA atoms, GEMM pipelines), cuTile (Python tile DSL), open-gpu-kernel-modules (RM, GSP firmware, UVM, kernel-open), NCCL (collectives, NVLink-SHARP, transports), and NVSHMEM (PGAS, symmetric heap, IBGDA); (2) Codebase mode generates an onboarding vault from a real CUDA/CUTLASS/NCCL/NVSHMEM/driver source tree; (3) Document mode turns NVIDIA whitepapers, GTC slide decks, and PDFs into study notes. Mode is auto-detected from CWD markers. Use when the user wants to learn CUDA from scratch, onboard onto a GPU codebase, build an exercise list for CUDA topics, or preprocess NVIDIA PDFs/docs into structured study material. Pair with the `cuda-tutor` skill to quiz against the resulting vault.
cuda-tutor
by drunkcodingInteractive quiz tutor for a CUDA StudyVault built by `cuda-tutor-setup`. Delivers 4-question rounds with concept-level proficiency tracking (🟥/🟨/🟩/🟦/⬜) across the 6 CUDA learning topics: CUDA kernels (threads/blocks/warps, memory hierarchy, TMA, WGMMA, cp.async), CUTLASS + CuTe (Layout/Stride/Tensor, MMA atoms, GEMM pipelines), cuTile (Python-first tile DSL), open-gpu-kernel-modules (RM, GSP firmware, UVM, kernel-open layout), NCCL (collectives, topology, NVLink-SHARP, transports), and NVSHMEM (PGAS, symmetric heap, IBGDA, on-stream API). Use when the user wants to (1) take a diagnostic CUDA assessment, (2) drill weak GPU concepts, (3) study a specific CUDA topic, (4) review the learning dashboard, or says things like "quiz me on CUDA", "test my CUTLASS knowledge", "drill NCCL", "/cuda-tutor", "퀴즈".
education-statement
by drunkcodingWrite a Statement on Education (Teaching Statement / Teaching Philosophy / Teaching Vision / "onderwijsvisie") for an academic faculty application, especially in Computer Science / AI / Data Science at research universities. Covers structure (1–3 page format), evaluation rubrics, evidence-based pedagogy, supervision frameworks, US vs Dutch/EU framing, TU Delft programme curricula (BSc CSE, MSc CS, MSc DSAIT) with verified course mapping, real-world example anatomies, and a section-by-section drafting workflow that produces a credible, specific, non-generic statement. Use whenever an applicant must write or revise a teaching statement, education statement, teaching vision, teaching philosophy, or onderwijsvisie — particularly for TU Delft, ETH, KTH, Cambridge, Imperial, or any EU/NL CS faculty position.
research-statement
by drunkcodingWrite, review, and revise academic Research Statements / Research Visions / Research Plans / 5-Year Plans for faculty, postdoc, fellowship, and promotion applications across disciplines and regions. Covers Mission/Strategy/Evidence/Story, US-school guidance from Cornell, CMU, MIT, Caltech, Penn, Yale, Harvard, UConn, Notre Dame, Delaware, Berkeley, length tiers, openings, past-work theming, fundable future lines, department fit, funding vectors, rubric scoring, red flags, and examples. Use when asked to write, draft, outline, edit, critique, score, red-team, or improve any research statement, research vision, research plan, future research statement, or research pitch.
triton-tutor-setup
by drunkcodingGenerate an Obsidian-format Triton StudyVault for learning OpenAI Triton's GPU programming stack. Three modes: (1) Curriculum mode (default, no source files needed) builds a prereq-ordered vault around 6 topics: Triton basics, tiling & autotuning, matmul patterns (tiled GEMM, split-K, FP8 `tl.dot`), attention & reductions (FlashAttention), compiler internals (TTIR/TTGIR/LLIR, NVIDIA/AMD backends, WGMMA + TMA lowering), and ecosystem/production (`torch.compile`/Inductor, AOT, `proton`); (2) Codebase mode generates an onboarding vault from a real Triton/Inductor source tree; (3) Document mode turns Triton docs, blog posts, and tutorial PDFs into study notes. Mode is auto-detected from CWD markers. Use when the user wants to learn Triton from scratch, onboard onto a Triton codebase, build a Triton exercise list, or preprocess Triton PDFs/docs into study material. Pair with the `triton-tutor` skill to quiz against the resulting vault.
triton-tutor
by drunkcodingInteractive quiz tutor for a Triton StudyVault built by `triton-tutor-setup`. Delivers 4-question rounds with concept-level proficiency tracking (🟥/🟨/🟩/🟦/⬜) across 6 Triton topics: Triton basics (`@triton.jit`, masks, `tl.load`/`tl.store`), tiling & autotuning (`triton.autotune`, `num_warps`/`num_stages`), matmul patterns (tiled GEMM, pid swizzling, persistent, split-K, FP8 `tl.dot`), attention & reductions (FlashAttention, online softmax, `tl.associative_scan`), compiler internals (TTIR/TTGIR/LLIR, NVIDIA/AMD backends, WGMMA + TMA lowering), and ecosystem/production (`torch.compile`/Inductor, AOT, `proton`). Use when the user wants to take a diagnostic Triton assessment, drill weak Triton concepts, study a specific Triton topic, review the learning dashboard, or says things like "quiz me on Triton", "drill FlashAttention", "/triton-tutor", "퀴즈".
tutor-handouts
by drunkcodingGenerate a university-style course pack (PDF lecture handouts, programming-assignment writeups, conceptual problem sets, topic cheatsheets, a capstone project, and a one-page syllabus) plus matching graded programming-exercise scaffolds (CUDA / Triton / PyTorch starters, a LeetGPU-style autograder harness, and gated solutions for hardest exercises) from a `StudyVault/` produced by `cuda-tutor-setup` or `triton-tutor-setup`. Visual style modeled on CMU 15-418, Stanford CS149, MIT 6.172, OLCF CUDA Training Series, and AlphaGPU LeetGPU challenges. Emits `.tex` + `Makefile`; compiles via `latexmk + pdflatex` if available, otherwise gracefully falls back to source-only. Use when the user wants to produce printable lecture handouts, programming assignments, problem sets, an autograder, or a complete course pack from an existing StudyVault, or says things like "make handouts for my StudyVault", "generate the course pack", "build programming exercises for CUDA", "/tutor-handouts".
sglang-bisect-ci-regression
by drunkcodingInvestigate a consistently failing SGLang CI test to find the root cause — whether it is a code regression from a specific PR, a hardware/runner-specific issue, or an environment change. Optionally reproduces the failure on a remote GPU server over SSH + Docker. Use when an SGLang CI test is failing on main, you need to bisect which PR introduced a regression, you suspect a runner-specific or GPU-specific issue, or you want to reproduce a CI failure on a remote server.
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.