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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crux-skill-memory-meditation-ensemble
by zotoioEnsemble Aggregation function for meditation: reads N model consolidations, writes `cross-model-synthesis.md`, performs K10 root cross-model reflection (step 3c), manages K10 layered cadence steps 3b–3f (per-tree `finalisation-enhancements.yml` reads + root combined YAML write with `cross_model_candidates` + `union_candidates`), returns single combined `needs_user_input`, dispatches resume-handler by `source` provenance, and hands off to the report skill for ensemble HTML+PDF generation. Use when the `crux-cursor-meditation-guide` agent is spawned with `ensembleAggregation: true`.
crux-skill-memory-meditation-review
by zotoioAdversarial Review function for meditation: 13-dimension audit (incl. citation integrity, slop detection, anti-homogenisation drift, level-conditional Dim 9 peer-review thoroughness, Dim 12 Comprehensiveness fidelity, Dim 13 Init-suggestion AND finalisation-enhancement honour), severity classification, ≤3-iteration loop, MUST_FIX `needs_user_input` schema with mandatory `context` decision-guidance, and Dim 13 `respawn_required: true` Report-Skill Respawn Protocol payload schema (K9 + K10b). Use when the `crux-cursor-meditation-guide` agent is spawned in Adversarial Review function (step 10).
crux-skill-memory-meditation-research
by zotoioResearch-mode meditation protocol: Phases A–G depth-first recursion, depth-0 manager steps 1–13 (incl. step 4b 4-mode `additional_focus_areas[]` reconciliation + `init-suggestions-{ts}.yml` write; step 8 K10c reflection writing `finalisation-enhancements.yml`; step 8b respawn-payload prep), facet registry lock, citations index, peer review file spec, comprehensiveness honouring at leaf depth. Use when the `crux-cursor-meditation-guide` agent runs the depth-0 manager or any Research-mode child agent.
crux-skill-memory-meditation-report
by zotoioMandatory paired HTML+PDF report generation for meditation: Comprehensiveness Level Mapping (12 dimensions × 4 levels), anti-homogenisation rules, Universal Contrast, light/dark mode + print TOC, Chart.js / D3 / calculator content minima with static fallbacks, Per-Branch Section Rule, Depth-3 Leaf Inclusion Rule, Peer-Review Surfacing Rule, Init-Suggestions Honour rules, K10b Per-Cheap-Type Rendering Contract (7 cheap types), and Report-Skill Respawn Protocol resume-handler. Use when the `crux-cursor-meditation-guide` agent runs report generation (step 12), when the ensemble aggregator generates the ensemble-level report, or when the report skill is respawned via Dim 13.
crux-skill-memory-meditation-quick
by zotoioQuick-mode meditation protocol: 6-step parallel fan-out with optional deep-confirm hook, warn-only citation validation, upfront child derivation, no peer review, K10c reflection (same rubric, warn-only at every richness level per K7). Use when the `crux-cursor-meditation-guide` agent runs the Quick depth-0 manager or any Quick-mode child agent.
xfi-release-workflow
by zotoioGuide for managing X-Fidelity releases using the unified release system. Use when releasing, versioning, troubleshooting release issues, or writing commit messages.
xfi-debug-analysis
by zotoioGuide for debugging X-Fidelity analysis issues. Use when troubleshooting analysis failures, rule evaluation problems, VSCode extension issues, or unexpected results.
xfi-documentation-update
by zotoioGuide for updating X-Fidelity documentation including README and website. Use when updating docs, adding new features to documentation, or ensuring docs stay in sync with code.
xfi-execute-plan
by zotoioGuide for executing engineering plans through coordinated subagent work. Use when executing existing plans from knowledge/plans/ directory.
xfi-add-package
by zotoioGuide for creating a new package in the X-Fidelity monorepo. Use when adding new packages, setting up monorepo structure, or configuring workspace dependencies.
xfi-consistency-testing
by zotoioGuide for ensuring CLI and VSCode extension produce identical analysis results. Use when verifying CLI-Extension parity, debugging output differences, or setting up consistency checks.
xfi-create-archetype
by zotoioGuide for creating a new X-Fidelity archetype configuration. Use when defining project templates, configuring rule sets, or setting up dependency requirements.
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.