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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dk-slop-audit
by deepklarityRun a codebase hygiene audit. Scans for misplaced files, dead code, temp files, security issues, structural problems, dependency slop, and git slop. Outputs a prioritized report with P0-P4 findings. Use periodically or before releases. Triggers on: 'audit the codebase', 'find slop', 'hygiene check', 'clean up', or /dk-slop-audit.
dk-agent-native-audit
by deepklarityRun comprehensive agent-native architecture review with scored principles. Audits a codebase against 8 agent-native architecture principles (Action Parity, Tools as Primitives, Context Injection, Shared Workspace, CRUD Completeness, UI Integration, Capability Discovery, Prompt-Native Features) by launching parallel sub-agents and producing a scored report. Use when the user wants to evaluate how agent-friendly their architecture is, or audit specific principles. Triggers on: 'agent native audit', 'architecture review', 'how agent-friendly is this', or /dk-agent-native-audit.
dk-breadcrumb-creator
by deepklarityTraces a workflow end-to-end through the harness-kit monorepo and creates a breadcrumb analysis doc in docs/breadcrumb_analysis/. Use this skill whenever the user wants to document a flow, trace a workflow, understand how a feature works across layers (frontend → backend → worker → CLI), or create debugging guides for a specific flow. Also use when the user mentions 'breadcrumb', 'trace this flow', 'how does X work end to end', 'document this workflow', or /dk-breadcrumb-creator.
dk-changelog
by deepklarityGenerate changelog entries from git diffs, prepend to CHANGELOG.md, and optionally commit + PR. Use when the user wants to update the changelog.
dk-close-the-loop
by deepklarityIteratively improve any output by running a structured observe-hypothesize-change-rerun loop. Uses an organized scratch directory to prevent context blowup — the conversation stays thin while iterations accumulate on disk. Use when an output (reflection, plan, prompt, pipeline result) isn't good enough and needs systematic refinement. Triggers on: 'close the loop', 'this output isn't good enough', 'iterate on this', 'refine this output', 'improve this reflection', or /dk-close-the-loop.
dk-compound
by deepklarityCapture learnings from the current conversation as a clean, human-readable doc in docs/. Use after solving a non-trivial problem, discovering a design pattern, hitting a gotcha, or making an architectural decision worth remembering. Triggers on: 'document this', 'capture this learning', 'that was tricky', 'let's compound this', or /dk-compound.
dk-local-follow-breadcrumb
by deepklarityConsults existing breadcrumb analysis docs before exploring the codebase. Use this skill whenever the user asks about how a flow works, where something happens in the code, how to debug or test a specific area, what files are involved in a feature, or needs to understand the path data takes through the system. Also trigger when the user mentions 'where does X happen', 'how does Y work', 'trace this', 'what files handle Z', 'how to test this flow', 'debug this area', or when you're about to spawn multiple exploration subagents to understand a cross-cutting flow. Even if the user doesn't explicitly ask — if the task requires understanding how multiple layers connect (frontend → backend → worker → CLI), check breadcrumbs first. This is cheaper and more accurate than re-discovering the same information through code search.
dk-local-inspect
by deepklarityRun diagnostic scripts on tasks, specs, boards, or reflections. Wraps testing_tools/ with proper working directory and output mode selection. Use this skill whenever the user wants to inspect, debug, or check the state of a task, spec, board, or reflection — even if they don't say 'inspect' explicitly. Triggers on: 'why did task X fail', 'show me spec Y', 'what's on the board', 'check task', 'inspect', 'diagnose', or /dk-local-inspect.
dk-local-logs
by deepklarityInspect and debug errors via logs across the harness-kit monorepo. Reads the right log file based on the error layer — backend, frontend, celery, odin CLI, or odin task execution. Use this skill whenever someone mentions a 500 error, a stack trace, a crash, 'check the logs', 'what went wrong', a service not starting, celery task failures, or any runtime error. Also triggers on: 'getting 500', 'backend error', 'celery error', 'odin failed', 'check logs', 'tail logs', 'debug this error', or /dk-local-logs.
dk-local-mock-first-approach
by deepklarityMock-first, layer-by-layer feature development. Instead of building a feature end-to-end and hoping the interface works, start by mocking at the user-facing surface with realistic data, get user acceptance on the experience, then deepen one complexity layer at a time with TDD. Everything is anchored on disk so work survives across sessions. Use whenever building a new feature, adding significant UI, planning a multi-layer change, or when the user mentions 'mock first', 'let me see it first', 'prototype this', 'simulate this feature', 'build this layer by layer', or /dk-local-mock-first-approach.
dk-local-run-spec
by deepklarityPlan and execute an odin spec. Handles working directory, auth, nested-session detection, and post-run diagnostics. Use whenever the user wants to run a spec, test a spec, or do a smoke test. Triggers on: 'run this spec', 'test this spec', 'odin plan', 'smoke test', or /dk-local-run-spec.
dk-loop-audit
by deepklarityAudit whether an AI agent can autonomously close the loop on problems in a given area — from discovering a symptom to verifying a fix — without human intervention. Evaluates documentation, diagnostic tools, commands, logs, and flows for completeness and actionability. Generates a gap-focused report with ratings. Use this skill whenever someone wants to assess debugging readiness, check if docs are agent-sufficient, audit a workflow for autonomous solvability, evaluate operational tooling coverage, or wants to know 'could an agent fix this on its own?' Triggers on: 'loop audit', 'audit this flow', 'is this debuggable', 'agent readiness', 'can an agent solve this', 'autonomous debugging check', or /dk-loop-audit.
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.