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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mk-loop
by ngocsangyemUse when autonomously improving a measurable scalar metric through bounded, git-tracked iterations: modify one scoped change, verify, keep or revert. Triggers on /mk:loop, 'optimize coverage/bundle size/lint count', 'iterate until the metric improves'. NOT for subjective cleanup (see mk:cook), known-root-cause bugs (see mk:fix), behavioral grading (see mk:evaluate), or shipping (see mk:ship).
mk-jira-time
by ngocsangyemJIRA time tracking via the jira-as wrapper: log work, list/update/delete worklogs, set estimates, generate reports, bulk-log. Triggers: 'log 2h on KEY', 'show worklog for KEY', 'time report for sprint', 'bulk-log 30m to JQL'. Worklog edit/delete loses data — agent confirms. NOT for sprint capacity (mk:jira-agile).
mk-clean-code
by ngocsangyemEnforces KISS/DRY/YAGNI during authoring and ad-hoc quality reviews. Pragmatic standards: concise, direct, no unnecessary comments. NOT for post-hoc diff/PR review (see mk:review); NOT for behavior-preserving simplification passes (see mk:simplify).
mk-confluence-collaborate
by ngocsangyemManage Confluence collaboration surface via the confluence-as wrapper: comments, attachments, labels, watchers. Triggers: 'add comment to page 12345', 'upload attachment to page 12345', 'label page 12345 as urgent', 'watch page 12345'. Inline-vs-footer comment safety enforced. NOT for page CRUD (mk:confluence-page); NOT for bulk ops (mk:confluence-bulk).
mk-confluence-page
by ngocsangyemConfluence page CRUD via the confluence-as wrapper. Triggers: 'create page in SPACE', 'show me page 12345', 'update title of page 12345', 'delete page 12345', 'page hierarchy under 12345', 'copy page 12345 to SPACE'. NOT for bulk ops on 10+ pages (mk:confluence-bulk); NOT for spec analysis (mk:confluence-spec-analyst); NOT for comments/attachments/labels (mk:confluence-collaborate).
mk-confluence-search
by ngocsangyemConfluence CQL search + space list + saved-filter + export via the confluence-as wrapper. Triggers: 'search confluence', 'find pages where X', 'cql for ...', 'export search results', 'list spaces', 'manage saved filters'. CQL injection-safe via the shared sanitizer. NOT for single-page CRUD (mk:confluence-page); NOT for bulk write ops (mk:confluence-bulk).
mk-confluence
by ngocsangyemRouting skill — recommends the correct mk:confluence-* leaf for any Confluence Cloud task. Triggers: 'confluence', 'wiki page', 'spec page', ambiguous Confluence intent. NOT an executor — every actual operation forks via a leaf skill.
mk-jira-admin
by ngocsangyemJIRA project / user / group / scheme / automation administration via the jira-as wrapper (11 sub-domains, ~65 verbs). Triggers: 'create JIRA project', 'list users', 'delete user USERNAME', 'list permission schemes', 'create group NAME'. Requires admin role. Destructive ops (project/user/group delete) require 2-step token confirmation. NOT for per-issue ops (mk:jira-issue / mk:jira-lifecycle); NOT for JSM admin (mk:jira-jsm).
mk-jira-fields
by ngocsangyemJIRA custom field discovery + configuration via the jira-as wrapper. Triggers: 'field ID for X', 'list custom fields', 'check fields for project PROJ', 'configure agile fields for board'. Read ops are open; create/configure-agile require admin. NOT for setting per-issue field values (mk:jira-issue update).
mk-llms
by ngocsangyemGenerate llms.txt files from project documentation following the llmstxt.org spec. Use when asked to create AI-friendly documentation indexes, generate llms.txt, or make a project discoverable by AI assistants.
mk-playwright-cli
by ngocsangyemSession-persistent browser automation via Playwright CLI — form filling, screenshots, data extraction, multi-step flows. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information across multiple requests. NOT for AI-driven long-autonomous flows (see mk:agent-browser); NOT for generating .spec.ts E2E code (see mk:qa-manual).
mk-review
by ngocsangyemMulti-pass structural code review with adversarial analysis, scope-aware dispatch, adversarial persona passes, and forced-finding protocol. Supports input modes: branch diff (default), PR number (#123), commit hash, pending changes (--pending). Use when asked to "review this PR", "code review", "pre-landing review", "check my diff", or "review #123". Proactively suggest when the user is about to merge or land code changes. NOT for behavioral verification against a running build (see mk:evaluate); NOT for post-implementation simplification (see mk:simplify).
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.