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.
Querying local SQLite index...
academic-slide-refiner
by Mr-Fang-VLSIRefines and restructures existing presentations into concise, high-impact versions suitable for short academic conference talks (e.g., 20 minutes). Focuses on logical flow, information density, and visual consistency.
control-preflight-reflect
by Mr-Fang-VLSIBefore each experiment, analyze latest route/STA evidence, reflect on method gaps, and output concrete improvement hypotheses and next-step A/B plan. Use when user asks to start new experiments or when model/flow conclusions may be unstable.
backside-routing-realization-specialist
by Mr-Fang-VLSIDiagnose and implement backside-routing realization paths, including targeted reroute, local DEF/OpenDB patching, and OpenROAD-backed net-level rerouter bring-up, when theory predicts benefit but the current flow does not realize BM2/BM1 usage.
control-theory-veto
by Mr-Fang-VLSITheory-level veto skill for EDA plans. Use before expensive experiment submissions or major flow/model changes to identify logically unsound assumptions, contradiction with known physics/policy, and high-risk invalid comparisons. Produces GO/CONDITIONAL/NO-GO with evidence.
bscost-net
by Mr-Fang-VLSIPlan and execute internet-assisted backside net cost modeling for signal and clock nets, then evaluate stability and correlation against HPWL baselines across multiple designs. Use when users ask to improve/validate backside cost models, choose benchmark designs, compare model vs HPWL, or prepare publishable model-evaluation evidence.
bscost-theory-opt
by Mr-Fang-VLSIBuild and validate theory-grounded optimization models for backside front-vs-back net cost (signal and clock), with strict metric contracts and stability gates against HPWL baselines. Use when users request principled model fitting, crossover reasoning, or promotion decisions from shadow mode to active optimization.
bspdn-goal-driver
by Mr-Fang-VLSIDrive BSPDN objective optimization toward target outcomes (about 8% dynamic-power reduction without area/timing regression, or 5%+ frequency uplift with non-worse power/area) using gated experiments and evidence tracking.
bspdn-pdn-sufficiency-evaluator
by Mr-Fang-VLSIEvaluate whether the current BSPDN PDN contract is strong enough under `BPR reserved for PDN`, separating PDN sufficiency from signal-mixing questions.
bspdn-physical-contract-auditor
by Mr-Fang-VLSIAudit whether the local BSPDN physical contract is coherent across paper assumptions, GT3 tech collateral, layer/via topology, and current flow policy before promotion or expensive attribution experiments.
backside-benefit-attribution-evaluator
by Mr-Fang-VLSIEvaluate whether backside benefits come from `CTS-backside-only` or from moving a selected subset of signal nets to backside, under a fair and physically valid comparison contract.
control-knowledge-explorer
by Mr-Fang-VLSIExplore and structure local EDA knowledge, identify evidence gaps, and prepare targeted literature retrieval tasks for local download and follow-up parsing.
control-postrun-retro
by Mr-Fang-VLSIPerform post-experiment retrospective for EDA runs, classify failure/success mechanisms, propose high-confidence next actions, and decide whether to recursively trigger a new workflow-scoped-execution iteration. Use after each experiment batch with monitor/summary/manifest artifacts.
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.