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.
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Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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area-governance-craft
by FraysaXIIUse when chartering or maturing an O5-1 area, running the area- completeness matrix, or wiring I93 area-governance artefacts. Codifies the craft for the 14-component bar, conservative skip on mirrors, and pattern_area_buildout inheritance. Triggers on area governance, compose_AREA, validate_area_completeness.py, AREA-NN component, area score matrix, pattern_area_buildout, hol_peopl_dtp_area_governance_001. Pairs with .cursor/rules/akos-area-governance.mdc (WHEN); this skill is HOW.
executable-process-catalog-craft
by FraysaXIIUse when authoring a process catalog, automation playbook, adapter or integration registry, or any canonical that registers executable actions (human-run, agent-run, or hybrid) in this AKOS workspace. Codifies the craft for paired SOP+runbook pairing (RULE 1), adapter status metadata (RULE 2), cadence taxonomy (RULE 3), and DAMA-DMBOK 2.0 alignment posture (RULE 4). Triggers on process catalog, paired SOP+runbook, adapter registry, integration registry, cadence taxonomy, on_demand, scheduled, event_triggered, gated_operator, AC-HUMAN, AC-AUTOMATION, Normalized Adapter Pattern, status enum. Pairs with .cursor/rules/akos-executable-process-catalog.mdc (the WHEN); this skill is the HOW.
uat-discipline-craft
by FraysaXIIUse when authoring or amending a closure-UAT report in this AKOS workspace. Codifies the craft for producing 11-section closure UATs that PASS the validator on the first run, surface real signal (not cargo-cult sections), and honor the PASS-WITH-FOLLOWUP discipline without abusing it. Triggers on any phrase like closure UAT, UAT report, uat-*.md authoring, verdict line, PASS-WITH-FOLLOWUP rationale, UAT-FM-NN finding, UAT-SEC-NN finding, validate_uat_report.py, 11 sections, closure-criteria verification, deploy verification at closure, browser-evidence audit trail, or when closing a wave that needs a closure-UAT mint. Pairs with .cursor/rules/akos-uat-discipline.mdc (the WHEN); this skill is the HOW.
pass-with-followup-governance-craft
by FraysaXIIUse when authoring or amending a closure UAT report whose verdict is PASS-WITH-FOLLOWUP in this AKOS workspace. Codifies the craft for writing the verdict_followup_rationale block well — picking the right followup class, naming a falsifiable closure target, citing the right tracker, and surviving the FAIL ramp. Triggers on any phrase like PWF rationale, PASS-WITH-FOLLOWUP, verdict_followup_rationale, followup_class, closure_target, PWF-FM-01, PWF-FM-04, monitoring obligation, deferred work with tracker, convention-class followup, mechanical recovery, escalation to blocker tracker, or after validate_pwf_governance.py emits a finding. Pairs with .cursor/rules/akos-pwf-governance.mdc (the WHEN); this skill is the HOW.
finance-ops-craft
by FraysaXIIUse when minting or maintaining FINOPS artefacts — counterparty register, rev-rec policy, pricing/tax registries, mirror spine evidence, monthly recon, or registered_fact entity gate. Codifies the HOW of FIN-01..05 and compose_FINOPS. Triggers on FINOPS, finops_counterparty, rev-rec, PRICING_TIER_REGISTRY, FINOPS_TAX_CALENDAR, validate_finops_ledger, finops_monthly_recon, FINANCE-AREA-FULL F3/F4. Pairs with .cursor/rules/akos-finance-ops.mdc (WHEN); this skill is HOW.
operations-delivery-craft
by FraysaXIIUse when executing Operations area delivery work — PMO/RevOps/SMO/Engagement SOP pairing, PMBOK domain mapping, automation-first tranches, area-completeness tier gates, or I94 operational sweep packets. Codifies the HOW of Operations delivery per OPERATIONS_DELIVERY_DOCTRINE. Triggers on Operations delivery, PMO inbox/WIP, cohesion render, RevOps dispatch, AREA-03, I94 P3 ops sweep, automation-first pairing. Pairs with akos-operations-delivery.mdc (WHEN); this skill is HOW.
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.