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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mgd-turn-discipline
by Marina-green-developmentsUse when: TURN CONTROL command; any turn that changes governance, methodology, plugin or registers; any DONE/CLOSED/LANDED claim; missed/diluted instruction; BYPASS recurrence; review chain invoked. Also: IPS when 3+ artefacts; Counterpoint+Harmonic on material recommendations; Kaizen on process suggestions; V/H/C on material claims; Q6 Claude Code candidacy on repeatable developer-surface tasks.
mgd-pmax
by Marina-green-developmentsUse this skill whenever authoring a PMax adversarial review, a PMax brief, an EVP synthesis pack or any PMax-class deliverable. Trigger on `/mgd-methodology:mgd-pmax`, Conductor invocation of PMax authoring intent, or PreToolUse Write to /10_Deliverables/PMax_Reviews/*. Enforces BPG-first-read discipline and the CR-699 six-mandatory-field schema.
mgd-shadow-consult
by Marina-green-developmentsUse this skill when authoring or processing a Shadow CONSULT mode commission. Plugin-managed commission template + verify-disk-state preamble gate. Single source of truth for Shadow CONSULT template.
mgd-sod
by Marina-green-developmentsUse this skill at start of day per the substrate-driven model. Per CM Operating Document Section 8.2 / Section 2: Capability Self-Check + RPM tail read + MIB + Active LAR filter + Risk/Supplier hot-spot + Stand-up Confirmation Block. Replaces full-surface Slack sweep model from v3.x. Does NOT initiate production before Stand-up Confirmation Block.
mgd-propagation
by Marina-green-developmentsUse this skill whenever the user says propagation, propagation check, version drift, version check, document versions, or any request to check whether methodology documents in the CWMF are at the correct version. Also checked at every SOD by every CC: read S0 Version Manifest and compare against Project files. Spawns the propagation-checker sub-agent for full checks. Per CR-018, CR-785.
mgd-eod
by Marina-green-developmentsUse this skill at end of day per the substrate-driven model. Per CM Operating Document Section 8.4: ID retired, ECP cumulative compilation retiring, RPM continuous append-only is canonical. EOD shape is RPM events + Live Artefact confirm + CR Reconciliation + #checkpoints + STOP. Does NOT initiate EOD without Conductor instruction (CR-607).
mgd-checkpoint
by Marina-green-developmentsUse this skill whenever the user says checkpoint, midpoint save, save progress, where are we, status update, or any request for a mid-session state capture. Produces a unified checkpoint + ECP update per CR-552. Also triggers on mgd checkpoint.
mgd-compaction
by Marina-green-developmentsUse this skill on compaction, compacted, context lost, recovery, post-compaction, pre-compaction, sentinel, compaction sentinel, or any indication that a compaction event has occurred or is imminent. Conductor-triggered manual fallback when the PreCompact hook fires too late or not at all. Reads /00_Session_State/Compaction_Sentinel/current_compaction_state.json plus the most recent SOD and checkpoint. Produces a Post-Compaction State Briefing per CR-919 Option A defensive-only architecture. v1.4 supersedes v1.2.5 mgd-compaction and v4.0 unbuilt SKILL.
mgd-isp
by Marina-green-developmentsUse this skill when the Conductor invokes Information Spine Pack, ISP, ISP snapshot, /isp slash command, or otherwise asks for an Information Spine Pack artefact. Conductor-triggered only - this skill does NOT auto-trigger. Surfaces an ISP snapshot or commission-grade ISP artefact pulled from /01_Core_Artefacts/Information_Spine/.
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.