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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yd-law-search
by ThomasMoreAI元典法律法规检索技能（开放平台版 https://open.chineselaw.com）。 用于中国法律、行政法规、司法解释、部门规章、地方性法规等规范性文件的检索。 适合：法条语义检索、法条关键词检索、法规级别检索、法条/法规详情查询。 不负责最终法律结论；如需基于检索结果做法律分析，应继续调用 cs-china-lawyer-analyst； 若是"做法律研究备忘录"类完整流程，应交由 legal-research 编排技能调度。
yd-case-search
by ThomasMoreAI元典案例检索技能（开放平台版 https://open.chineselaw.com）。 用于中国案例、类案、典型案例、权威案例的关键词检索与语义检索，以及案例详情拉取。 不负责最终法律结论；如需基于案例做论证，应继续调用 cs-china-lawyer-analyst； 若是"做法律研究备忘录"类完整流程，应交由 legal-research 编排技能调度。
collaboration-platform-advisor-scott-margetts
by ThomasMoreAICollaboration platform configuration methodology for legal matter sites. Site architecture, workflow identification, dashboard design, data quality governance, and user adoption for SharePoint, Teams, and equivalent platforms. M365 is the reference implementation — outputs are platform-agnostic enough to brief IT or build simple automations without becoming a Power Automate manual. Use when setting up a matter site, identifying workflows to automate, designing reporting dashboards, managing platform data quality, or driving user adoption. Trigger on: 'set up the matter site', 'configure SharePoint', 'build a dashboard', 'what should we automate', 'brief IT on this workflow', 'nobody is using the platform', 'data quality is poor', 'set up Teams channel', 'matter site structure', 'alerts and notifications', 'user training', 'platform governance', 'status dashboard', 'what workflows can we automate', 'matter site template'.
continuous-improvement-engine-scott-margetts
by ThomasMoreAICapture, structure, and recycle lessons from active and closed legal matters. Three modes: in-flight capture (triggered by scope changes, risk events, status updates — highest value), mid-matter review (phase gates or quarterly), and matter close retrospective (full structured findings). Lessons are formatted for immediate reuse, not filed and forgotten. Use when a risk materialises, a scope change lands, a phase completes, or a matter closes and you want to convert what happened into something useful for the next matter. Trigger on: 'capture a lesson', 'what did we learn', 'matter close', 'retrospective', 'lessons learned', 'what went wrong', 'what worked', 'phase gate review', 'debrief', 'extract the learning', 'close the matter', 'what should we do differently', 'pattern from this matter', 'improve the next one'.
document-approval-tracker-scott-margetts
by ThomasMoreAIApproval cascade definition and tracking for multi-stakeholder document workflows. Internal review sequences, client approval workflows, regulatory review, overdue chasing with escalation logic, version control coordination, and cross-jurisdiction document dependencies. Use when defining who reviews a document and in what order, tracking where a document is stuck in the approval chain, chasing an overdue reviewer, preventing reviewers working on superseded drafts, or mapping dependencies between documents across jurisdictions. Trigger on: 'who needs to approve this', 'document approval', 'review sequence', 'stuck in review', 'overdue approval', 'who has the document', 'version control', 'wrong version', 'document dependency', 'NL SPA waiting on German opinion', 'approval cascade', 'review chain', 'document circulation', 'chasing the partner', 'client approval process', 'where is the document'.
local-counsel-manager-scott-margetts
by ThomasMoreAIEnd-to-end external local counsel lifecycle management for multi-jurisdiction legal matters. LC selection criteria and capability assessment, engagement setup and instruction design, performance monitoring and check-in cadence, scope enforcement, and relationship escalation. Use when selecting local counsel for a jurisdiction, designing LC instructions, managing the LC check-in rhythm, enforcing scope boundaries when LC signals overreach, or escalating a performance or relationship issue beyond the matter team. Trigger on: 'which LC should we use', 'LC instruction', 'brief the local counsel', 'LC hasn't responded', 'LC is going off scope', 'LC scope dispute', 'confirm scope with LC', 'LC check-in', 'LC is slow', 'monitor the LC network', 'LC engagement letter', 'LC selection', 'what should we tell the local counsel', 'LC onboarding', 'LC performance issue', 'LC relationship problem'.
matter-plan-builder-scott-margetts
by ThomasMoreAIConvert agreed scope into a structured matter plan — phases, workstreams, milestones, dependencies, owner assignments, and matter setup decisions. Use when planning a new matter, running a kickoff, building a workstream plan, structuring phases, setting up task codes, or producing a plan to drive status reporting. Trigger on: 'build a plan', 'matter plan', 'project plan', 'what are the phases', 'workstream plan', 'how do we sequence this', 'who owns what', 'task codes', 'matter setup', 'workstream plan', 'matter plan', 'rolling wave', 'plan the next phase', 'what comes first', 'kickoff agenda'.
plain-english-jeremylongshore
by ThomasMoreAITranslates every clause of a contract into plain language at an 8th-grade reading level and flags deliberately confusing language patterns. Use when a user says "explain this contract", "what does this mean", or needs a non-lawyer to understand an agreement. Trigger with "/plain-english" or "translate this contract to plain English".
collateral-valuation
by ThomasMoreAIValues collateral and allocates secured vs. unsecured claim portions under U.S. bankruptcy standards, applying Chapter 7 liquidation value, Chapter 13 replacement value (Rash), the 910-day vehicle rule, and § 506(b) oversecured interest. Use when preparing proofs of claim, plan treatment analyses, cramdown disputes, deficiency calculations, or valuation motions under FRBP 3012.
inverse-condemnation-complaint
by ThomasMoreAIDrafts inverse condemnation complaints seeking just compensation for government takings without formal eminent domain. Use when a user needs a takings clause complaint, inverse condemnation pleading, or property rights constitutional claim involving physical, regulatory, or temporary takings.
orange-flaggen-katalog
by ThomasMoreAIKatalog schwacher positiver Formulierungen im Arbeitszeugnis, die auf Note 3 hindeuten. Umfasst alle Orange-Signale: fehlende Steigerungsadverbien, eingeschränkte Lobesformeln und strukturelle Abschwächungen mit Notentendenz Note 3.
byod-policy
by ThomasMoreAIDrafts a Bring Your Own Device (BYOD) policy for U.S. employers governing personal device access to company systems. Covers MDM enrollment, encryption, remote wipe authority, privacy expectations, data classification, and regulatory overlays (HIPAA, GLBA, SOX, GDPR). Use when creating or updating BYOD policies, mobile device security policies, or personal device programs.
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.