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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interview
by panaversityThis skill conducts discovery conversations to understand user intent and agree on approach before taking action. It should be used when the user explicitly calls /interview, asks for recommendations, needs brainstorming, wants to clarify, or when the request could be misunderstood. Prevents building the wrong thing by uncovering WHY behind WHAT.
hypothesis
by panaversityActivate for: assumption, hypothesis, assumption map, MVP, minimum viable product, lean startup, what assumptions am I making, test my idea, what could go wrong, assumption risk, validate assumption, kill my idea, stress test, what should I test first, MVP design, MVP scoping, what to build, minimum feature set, success criteria, failure criteria, pivot criteria, build plan, riskiest assumption, leap of faith assumption, critical assumption. NOT for: idea generation (use idea), customer discovery (use discovery), pilot results analysis (use validate).
jd
by panaversityWrite and improve job descriptions with inclusive language. Activate for: job description, JD, job posting, role description, job advert, vacancy description, job specification, person specification, write a job description, improve job description, inclusive job description, job requirements, role requirements, hiring, recruiting, talent acquisition, position description, role profile, what to put in job description. NOT for: interview questions or scorecards (use interview-prep), offer letters (use draft-offer), compensation benchmarking (use comp-analysis).
banking-global-router
by panaversityRoutes banking regulatory queries to the correct product skill and jurisdiction overlay. Activate for any query involving IFRS 9, ECL, expected credit loss, Stage 1, Stage 2, Stage 3, SICR, PD, LGD, EAD, Basel III, Basel IV, CET1, RWA, capital ratio, LCR, NSFR, HQLA, ICAAP, stress test, AML, KYC, CDD, EDD, SAR, STR, sanctions, OFAC, HMT, PEP, FATF, reconciliation, nostro, suspense, FRTB, market risk RWA. Covers 7 jurisdictions across multiple regulatory regimes (PRA, ECB/EBA, Fed/OCC, APRA, MAS, CBUAE, SBP).
ijarah-imb
by panaversityActivate for: ijarah, IMB, ijarah muntahia bittamleek, Islamic lease, FAS 8, FAS 32, rental income, lease ending in ownership, operating lease Islamic, finance lease Islamic, right-of-use asset Islamic, IFRS 16 Islamic, ijarah assets, ijarah rental schedule, ijarah depreciation.
kyc-risk-rating
by panaversityActivate for: KYC risk rating, customer risk classification, AML risk score, customer risk assessment, high-risk customer, risk-based approach, risk rating, customer due diligence risk score, PEP risk, geographic risk, product risk, customer risk categories. NOT for: transaction monitoring alerts (use aml-typologies), SAR/STR drafting (use aml-sar-drafting), sanctions screening (use sanctions-screening).
ifrs9-disclosure
by panaversityActivate for: IFRS 7 disclosure, ECL disclosure note, credit risk disclosure, IFRS 9 annual report note, sensitivity analysis IFRS 9, stage distribution table, credit quality table, IFRS 7 note drafting. NOT for: ECL calculation methodology (use ifrs9-ecl), staging assessment (use ifrs9-staging), US GAAP disclosure requirements under ASC 326 / CECL.
ifrs9-scenarios
by panaversityActivate for: macro overlay, macroeconomic scenarios, PIT PD, point-in-time PD, credit cycle adjustment, scenario weighting, forward-looking information, satellite model, GDP, unemployment, house price index, IFRS 9 scenarios, scenario probability. NOT for: ECL calculation mechanics (use ifrs9-ecl), staging assessment (use ifrs9-staging), stress testing for capital adequacy (use stress-testing).
logistics-brief
by panaversityActivate for: logistics, carrier, freight, shipping, route, delivery, on-time delivery, OTD carrier, logistics performance, carrier review, freight cost, cost per kg, lane analysis, route optimisation, logistics brief, carrier scorecard, logistics KPI, shipping performance, freight audit, expedited freight, premium freight, mode of transport, logistics network, carbon emissions, Scope 3 logistics. NOT for: supply network facility placement (use network-design), vendor assessment (use vendor-assessment), spend category analysis (use spend-analysis).
network-design
by panaversityDesigns and compares supply chain network scenarios. Activate for: network design, supply chain network, warehouse location, distribution centre, DC placement, new DC, nearshoring, reshoring, offshoring, facility consolidation, network optimisation, make vs buy, sourcing location, scenario analysis, network scenario, scenario comparison, logistics network, node optimisation, where to warehouse, where to manufacture, cross-docking, open a new warehouse, open a new distribution centre. USE THIS when the task involves comparing network configurations, evaluating new facility locations, or running scenario comparisons. Respond directly with scenario analysis -- do not ask clarifying questions when sufficient context is provided. NOT for: carrier performance (use logistics-brief), vendor assessment (use vendor-assessment), spend category analysis (use spend-analysis).
supplier-risk
by panaversityMonitors ongoing risk signals and produces risk briefs for known vendors. Activate for: supplier risk monitoring, vendor risk alert, supply risk, risk brief, supplier financial risk, credit rating downgrade, supplier operational risk, supplier compliance risk, geopolitical risk, Tier 2 risk, sub-supplier disruption, supply disruption, risk monitor, risk rating change, risk alert, distress signal, supplier news, country risk, supply chain resilience, CVA, administration, insolvency. USE THIS when a KNOWN risk event has occurred (credit downgrade, financial distress, disruption, regulatory action) and you need to assess its impact. NOT for: classifying or scoring a vendor (use vendor-assessment), vendor onboarding or approval (use vendor-assessment), vendor Kraljic classification (use vendor-assessment), invoice processing (use invoice-reconciliation), carrier performance (use logistics-brief).
supply-chain-brief
by panaversityActivate for: supply chain brief, weekly supply chain report, CPO report, COO report, procurement dashboard, supply chain dashboard, weekly report, executive brief, supply chain KPIs, supply chain metrics, procurement performance, logistics performance summary, vendor performance summary, AP performance, weekly update, supply chain status. NOT for: detailed vendor assessment (use vendor-assessment), detailed invoice reconciliation (use invoice-reconciliation), detailed carrier review (use logistics-brief).
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.