Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
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code-audit
by NubaeonUse when the user says '/code-audit', 'audit this code', 'check code quality', 'find duplication', 'find dead code', 'code cleanup', 'technical debt audit', 'code review module', or wants a structured noetic investigation of code quality. This skill runs external analysis tools and structured manual review, producing Empirica artifacts (findings, goals, decisions) that any praxic agent can execute.
code-docs-align
by NubaeonUse when the user says '/code-docs-align', 'check if docs match code', 'verify docstrings', 'find stale comments', 'audit TODOs', 'check ref-doc accuracy', 'documentation accuracy', or wants to verify that documentation, docstrings, comments, and ref-docs actually reflect the current state of the code. This skill bridges /code-audit (code quality) and docs-assess (doc coverage) by checking ACCURACY — do the docs match what the code actually does?
cortex-mailbox-poll
by NubaeonUse when wiring the canonical cortex inbox+outbox polling loop into Claude Code's /loop. This is the orchestration spine — every empirica claude polls Cortex on a fast adaptive cadence (30s base, 5m max) for proposals addressed to itself + status changes on its own outgoing proposals. Self-throttles when an empirica transaction is open (the AI is already busy; no need to interrupt). The canonical loop catalog (empirica/core/cockpit/canonical_loops.py) auto-installs this when the TUI cockpit toggles L on an instance that has no loops registered. This skill is the body the AI runs each fire.
cortex-mailbox-send
by NubaeonUse when sending a message to a PEER AI in the mesh — discussion, FYI, question, request to do work, or completion-ack for a request a peer made of YOU. Pairs with /cortex-mailbox-poll (the receive side). Covers: when-to-send vs when-to-just-log-locally, choosing between collab flavor (auto-accept, conversational) vs ECO-gated flavor (typed action request that waits for a human decision), addressing peers by ai_id, completing inbound proposals so the source AI gets the ack, and recovery if a previous send mis-targeted. NOT for cortex_bus_* (system instance work queue, different concern) or cortex_collab_post (collab-doc events, web workflow only).
dispatch-agent
by NubaeonDispatch subagents with inherited epistemic context from Cortex. Use when spawning Agent tool calls for tasks that would benefit from inherited findings, dead-ends, and anti-patterns. Triggers on 'dispatch agent', 'spawn agent with context', 'epistemic agent', or before any Agent tool call for non-trivial tasks.
empirica-constitution
by NubaeonEmpirica deep governance — phase-aware completion, the cognitive immune system, the turtle principle, and the practice model. Load this when the system prompt's operational routing isn't enough — when you need the *why* underneath the mechanism choice, or when "what counts as done" / "what is this practice" is the question. Triggers: 'empirica constitution', 'practice model', 'what counts as done', 'completion question', 'cognitive immune', 'turtle principle', or any uncertainty about the framework's deeper rules.
epistemic-persistence-protocol
by NubaeonEpistemic Persistence Protocol (EPP) — gives Claude calibrated backbone when holding positions under user pushback. Use this skill whenever Claude needs to maintain, defend, soften, or revise a substantive position during disagreement. Triggers on any conversation where Claude has expressed an opinion, assessment, analysis, or recommendation and the user pushes back, disagrees, challenges, or questions that position. Also use when the user explicitly asks Claude not to be sycophantic, to have backbone, to hold its ground, or to give honest opinions. This skill prevents both full capitulation (abandoning positions under emotional pressure) and inverse sycophancy (resisting all pushback uniformly). It replaces the Anti-Agreement Protocol (AAP) with a calibrated, evidence-gated approach. Part of the Empirica epistemic measurement framework (github.com/Nubaeon/empirica).
epistemic-transaction
by NubaeonUse when starting complex work, planning implementation, breaking down tasks, creating specs, or when the user says 'plan this as transactions', 'plan transactions', 'break this down', 'create a spec', 'how should I approach this', 'transaction plan', or mentions needing a structured approach to multi-step work. This skill guides the full epistemic workflow from task decomposition through measured execution. Prefer this over EnterPlanMode for non-trivial tasks.
ewm-interview
by NubaeonUse when the user says '/ewm-interview', 'run EWM interview', 'create workflow protocol', 'set up my workflow', 'interview me for EWM', or wants to create a personalized AI collaboration protocol. This skill interviews users to discover their goals, domains, tools, preferences, and trust boundaries, then generates a workflow-protocol.yaml.
inbox-listener
by NubaeonUse when arming an event listener for the canonical mesh — when the user says 'arm this listener', 'subscribe to ntfy topic', 'wake me when X arrives', or when responding to a system-reminder from listener-install-pickup. The new canonical flow is `empirica listener on/arm/off` — three single-purpose tool calls that auto-resolve defaults, short-circuit when a persistent OS service is already subscribed, and emit structured next_step JSON the AI can mechanically chain. The older curl-based pattern lives as the 'legacy / custom topics' fallback at the bottom.
loop-cron
by NubaeonUse when scheduling cron-mode loops with Claude Code's /loop, when registering periodic background work, when the user says 'cron loop', 'periodic loop', 'register a cron', 'schedule recurring work', or when configuring a loop that needs to be visible in `empirica status`. This skill provides the prompt template that wires CC's /loop into Empirica's loop registry — register at start, check pause flag each fire, heartbeat at end. Without this wiring, a /loop cron is invisible to the cockpit and uncontrollable from any other terminal.
message-cleanup
by NubaeonDaily housekeeping body for the canonical `message-cleanup` loop. Prunes expired git-notes mesh messages so the inbox stays focused on un-read ones. Loaded by the loop scheduler when the cron entry fires (default 03:17 daily) — never invoked directly by a user. Triggers: `<task-notification>` from the message-cleanup loop, "message housekeeping", "expired messages", "prune mesh".
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.