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.
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Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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tool-calling-tutor
by WenyuChiouWhen the user is building a tool-calling agent and gets stuck — "為什麼 LLM 不呼叫我的 tool", "我這 schema 哪裡寫壞", "tool 被呼叫但 args 不對", "ReAct loop 跑不停", "the LLM won't call my tool", "help me design a function schema", "debug this tool-use behavior". Walks them through a 4-branch diagnostic + 5-step schema design walkthrough, with references to bad/good schema A/B and SDK-diff cheatsheet. Do NOT use for: pure LangChain / LangGraph / CrewAI framework questions (route to Stage 4 frameworks), MCP server building (route to cookbook 2), production agent observability (route to Stage 7).
codex-delegate
by WenyuChiouDelegates implementation-heavy or repetitive coding work (batch edits, boilerplate, multi-file refactors with clear patterns, test scaffolding) from Claude to OpenAI Codex CLI. Use when token cost outweighs judgment cost. Trigger phrases include "delegate to codex", "let codex do this", "batch refactor across files", "scaffold tests for". Avoid for architecture, security review, or root-cause debugging.
gemini-delegate
by WenyuChiouDelegates large-context reading, bilingual or Chinese (CJK / 中文 / zh-TW) drafting, cross-file synthesis, and second-opinion review to Google Gemini CLI. Use when input exceeds Claude's working budget, when the user writes in Chinese, when terminology must align across long documents, or when a reviewer pass is needed. Trigger phrases include "summarize this in Chinese", "second-opinion review", "long-context synthesis", "draft this in zh-TW". Avoid for bulk code generation or security-sensitive coding.
zotero-library-curator
by WenyuChiouAudit and curate a Zotero library — find duplicate DOIs, orphan items missing required tags, propose collection rebinds, identify bloated or under-used collections, generate tag hygiene reports, emit preview-only cleanup plans. Use when the user asks to "audit Zotero", "find duplicates", "tag hygiene report", "which collections are bloated or under-used", or "propose a Zotero cleanup plan". Defers all CRUD operations to the standalone `zotero-skills` skill or `research-hub zotero` CLI. Includes a backup-first reminder before any apply/CRUD handoff suggestion.
zotero-skills
by WenyuChiouFull CRUD operations on Zotero library — search, add, update, delete items with notes, tags, collections, and PDF attachments. Uses dual-API architecture (local API for fast reads, Web API for writes). Use this skill whenever the user mentions Zotero, references, citations, literature management, reading notes, or wants to organize academic papers — even if they don't explicitly say 'Zotero'.
research-project-orienter
by WenyuChiouRead the .research/ manifest files at a project root and produce a single orientation memo (research question, datasets, current stage, key entrypoints, evidence artifacts, open questions). Use when the user asks to "orient me in this project", "what is this repo about", or "build a context map for this paper" — and the project already has .research/ manifests (or trigger research-context-compressor first).
notebooklm-brief-verifier
by WenyuChiouCompare a downloaded NotebookLM brief against the source bundle research-hub uploaded, and report missed sources, unsupported claims, contradictions, and recommended follow-up prompts. Use when the user asks to "verify this NotebookLM brief", "check if the brief missed anything", or "compare downloaded notes to the cluster papers".
paper-summarize
by WenyuChiouAfter research-hub ingests a cluster of cited papers, fill the per-paper Key Findings + Methodology + Relevance sections in BOTH Obsidian markdown and the Zotero child note. Use when the user says "fill the TODO Key Findings/Methodology blocks left by research-hub auto", "I just ran auto and don't know what these papers are about", or "summarize the papers in cluster X". Invokes a supported LLM CLI on each paper's abstract. NOT for summarizing the user's own manuscript draft — that's `paper-memory-builder`. NOT for cluster-level briefs — that's `research-hub notebooklm generate`. This skill is per-cited-paper only. If the user says "extract claims from these papers", disambiguate before acting: their own manuscript draft → `paper-memory-builder`; a cross-paper comparison matrix → `literature-triage-matrix`; per-cited-paper Key Findings → this skill.
research-hub
by WenyuChiouUse research-hub to operate Zotero, Obsidian, and NotebookLM research workflows through CLI, MCP, REST, and dashboard. Trigger when the user asks to find papers, ingest sources, organize a literature review, build Obsidian research notes, upload sources to NotebookLM, inspect clusters, generate AI briefs, or maintain a research vault.
gap-to-topic
by WenyuChiouTurn a research area into a go/no-go decision dossier for ONE candidate thesis/proposal topic — a 3-gate verdict (is the gap open? is it a contribution? is it feasible?) with the evidence laid out so the researcher can verify it. Use when the user asks "is this gap worth pursuing", "help me pick a thesis topic", "is this idea already taken", "find me a defensible research gap", "vet this research idea before I commit", or "should I do this". NOT a literature review (use `literature-triage-matrix` for a comparison matrix) and NOT a study design (use `research-design-helper` once a topic is chosen). Produces a `.research/topic_dossier.md`, a `.research/topic_dossier.docx` (Word, colour-coded), a `.bib`, and a `.gaps.yml`.
literature-triage-matrix
by WenyuChiouTurn a list of papers (Zotero collection, Obsidian cluster, manual list) into a compact comparison matrix written to .research/literature_matrix.md, instead of generic per-paper summaries. Use when the user asks to "make a literature matrix", "compare these papers by method/data/limitations", or "decide which papers are central to my review". If the user says "extract the claims from these papers": cross-paper comparison matrix → this skill; claims from their own manuscript draft → `paper-memory-builder`; per-cited-paper Key Findings → `paper-summarize`. This skill compares a KNOWN paper set; to turn a research area into a go/no-go decision on a candidate thesis/proposal topic, use `gap-to-topic`.
research-design-helper
by WenyuChiouGuide a researcher through 5 Socratic segments — research question sharpening, expected mechanism, identifiability check, validation plan, risk register — and produce `.research/design_brief.md`. Use when the user asks to "frame this research question", "design my study", "help me think through what model to build", "sharpen my hypothesis", "is my research question sharp enough to be falsifiable?", or "before I start coding, walk me through the design". Runs AFTER a topic is chosen — it designs the study for a given question; to decide whether a research gap is worth pursuing at all (open / a contribution / feasible), use `gap-to-topic`. Does NOT write the model spec; does NOT invent the research question — guides the human to articulate them.
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.