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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create-huntable-agent
by dfirtntAdd a new extraction sub-agent to Huntable CTI Studio as a first-class peer of CmdlineExtract, ProcTreeExtract, HuntQueriesExtract, RegistryExtract, ServicesExtract, and ScheduledTasksExtract. Use this skill whenever the user asks to "add a new agent", "create a sub-agent", "wire up a new extractor", "add a new extraction type", or anything related to adding a new LangGraph extraction sub-agent to the agentic workflow pipeline. This covers the full stack: schema, config pipeline, migration, services, routes, UI templates, config display JS, presets, and tests.
cut-release
by dfirtntInteractive walkthrough for cutting a new release of Huntable CTI Studio. Use this skill whenever the user says "cut a release", "ship a release", "tag a version", "bump the version", "new release", "do the release", "release vX.Y.Z", "ship v5.4.0", "time to release", or otherwise signals they want to move code from the release branch to main and publish a tagged GitHub Release. Drives scripts/release_cut.py plus the branch unlock/lock dance and the tag push that triggers .github/workflows/release.yml, pausing at every irreversible step so the operator can confirm.
add-source
by dfirtntAdd a new CTI intelligence source to Huntable CTI Studio for article ingestion. Use this skill when the user says "add a source", "add a feed", "new source", "add this blog", "ingest from <url>", or wants to configure a new RSS/scraping source for the ingestion pipeline.
create-issue
by dfirtntSummarizes problems or bugs from the open chat (or uses user-provided content), proposes a Todoist issue with Title and Description and optionally subtasks, then creates tasks in the Huntable CTI Studio Todoist project via Todoist MCP after user approval. Use when the user says "Create Issue", "create issue", or asks to turn chat discussion into Todoist tasks in Huntable CTI Studio.
source-healing
by dfirtntDiagnose and repair troubled CTI intelligence sources in Huntable CTI Studio — sources that ingest 0 articles or rack up recurring fetch failures. Use this skill whenever the user says "heal a source", "fix a broken/failing source", "this source has 0 articles", "why isn't <source> collecting", "diagnose troubled sources", "source-healing", or whenever a source has stopped producing articles, has stale content, redirects, switched to JS rendering, or is accumulating failures — even if the user doesn't say the word "heal". Operator-invoked and propose-and-confirm: it diagnoses, proposes one concrete config fix, and applies it only after the operator approves. It never auto-applies, never runs on a schedule.
lgtest
by dfirtntWhen the user invokes "LGTest", reviews the most recent changes (especially from the current chat, but not limited to it) and proposes adding new tests—unit, UI, smoke, API, or integration—as appropriate. Use when the user says "LGTest" or asks to review changes and propose tests.
lgl
by dfirtntLite commit and push to current branch (git add ., commit, push)
lg-workflow
by dfirtntPerforms full pre-commit hygiene (changelog, docs, deps, security, Vulture dead-code check), then stages, commits, and pushes to the current branch. Use when the user says "lg" or "LG"; do not commit or push on "go" or "implement" unless they also say LG.
add-cloud-model
by dfirtntRegister a new OpenAI or Anthropic model in CTI Studio so it appears in the Workflow Agent configuration dropdown. Use this skill whenever the user says "add a model", "make gpt-X available", "why isn't model X in the dropdown", "register claude X", "add support for o4", "I want to use gpt-5-pro", or anything about making a specific cloud model selectable in the UI. Also use it when a new model has been released and the user wants to start using it in their pipelines.
eval-fixture-audit
by dfirtntAudit and correct the eval fixtures (Eval1 expected counts + Eval2 ground-truth item lists) for one extractor subagent in Huntable CTI Studio: CmdlineExtract, ProcTreeExtract, RegistryExtract, ServicesExtract, ScheduledTasksExtract, or HuntQueriesExtract. Use this skill whenever the user asks to "audit the evals", "run the eval audit", "check/fix/populate ground truth", "reconcile eval fixtures", "are the evals set up right", mentions Eval1/Eval2 counts or expected_items drift, or names any extractor together with evals, fixtures, ground truth, or the evals spreadsheet — even if they don't say "audit". Also use it for re-audits after an extractor contract/spec change. Operator-gated propose-and-confirm: it extracts blind, reports divergences, and writes nothing until the operator approves sink by sink. Interactive-only — it depends on a human answering its STOP gates; do not run it headless or autonomously.
minor-release-highlights
by dfirtntProposes 2–3 high-level feature-set themes for a minor or point release compared to the previous minor—user-facing capability or UX shifts, not patches or commit lists. Use when cutting release notes, tagging a minor/point version, summarizing “what changed since x.y.0”, or when the user asks for dot/minor release messaging.
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.