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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neqsim-capability-map
by equinorStructured inventory of NeqSim's capabilities by engineering discipline. USE WHEN: checking what NeqSim can do, planning implementations, assessing gaps for engineering tasks, or routing work to the right agent. Covers thermodynamics, process equipment, PVT, standards, mechanical design, flow assurance, safety, and economics.
neqsim-depressurization-mdmt
by equinorEmergency depressurization (blowdown) per API 521 §5.20 and minimum design metal temperature (MDMT) assessment per ASME UCS-66 / API 579 / EN 13445 — VU-flash transient inventory model, time-to-target-pressure, low-temperature embrittlement screening, and integration with PSV/flare loads. USE WHEN: a task requires sizing a blowdown valve, generating a P-vs-time curve for a vessel under fire / depressurization, checking MDMT against blowdown end-temperature, providing source terms for relief and flare networks, or distinguishing blowdown from trapped-liquid fire rupture screening. Anchors on neqsim.process.safety.depressurization.DepressurizationSimulator and neqsim.process.safety.mdmt.MDMTCalculator.
neqsim-pid-process-operations
by equinorP&ID-to-NeqSim operational workflow. USE WHEN: understanding P&ID symbols, converting P&ID topology into NeqSim process simulations, linking equipment and instrument tags to plant historian data via tagreader, evaluating steady-state and dynamic valve/equipment changes, or preparing water-hammer valve-closure handoffs.
neqsim-root-cause-analysis
by equinorRoot cause analysis (RCA) framework for process equipment — Bayesian-inspired diagnosis integrating multi-source reliability data (IOGP/SINTEF, CCPS, IEEE 493, Lees, OREDA) as prior, plant historian evidence (likelihood), and NeqSim simulation verification. USE WHEN: diagnosing compressor trips, high vibration, efficiency loss, separator carryover, heat exchanger fouling, or any operational anomaly. Anchors on neqsim.process.diagnostics classes.
neqsim-technical-document-reading
by equinorReads and extracts structured engineering data from technical documents (PDFs, Word, Excel, CSV) and engineering images/drawings (P&IDs, vendor datasheets, mechanical arrangements, performance maps). USE WHEN: a user provides engineering documents or images — equipment data sheets, technical requirements, design basis, well test reports, P&ID descriptions, inspection reports, standards, vendor drawings, compressor maps, phase envelopes, material certificates, trapped-liquid fire rupture evidence packs, or water-hammer route/event evidence — and needs structured data for process simulation. Covers document classification, extraction patterns by document type, image/figure analysis with view_image, unit normalization, data quality scoring, and output formats.
neqsim-trapped-liquid-fire-rupture
by equinorTrapped-liquid fire rupture study workflow for blocked-in liquid-filled pipe segments. USE WHEN: a task asks for trapped liquid, blocked-in liquid, thermal expansion rupture, fire exposure without relief, PFP demand, flange/pipe rupture screening, or generating a Word/HTML safety study from P&IDs, STID, line lists, piping specs, material certificates, and fire documents. Anchors on neqsim.process.safety.rupture plus trapped inventory, document retrieval, and source-term handoff.
neqsim-water-hammer
by equinorWater hammer and liquid hammer screening workflow for NeqSim. USE WHEN: evaluating fast valve closures, pump trips, check-valve slam, hydraulic surge, STID/P&ID route extraction, tagreader event windows, or MCP runWaterHammer studies. Covers WaterHammerPipe, WaterHammerStudy, STID route geometry, tagreader field-data overrides, pressure envelopes, Joukowsky checks, and screening limitations.
neqsim-ccs-hydrogen
by equinorCO2 capture, transport, storage (CCS) and hydrogen systems patterns for NeqSim. USE WHEN: modeling CO2 pipelines, injection wells, impurity effects on phase behavior, CO2 dense phase transport, hydrogen blending, electrolysis, or any CCS/H2 value chain analysis. Covers CO2 phase behavior, impurity management, well integrity, and hydrogen systems.
neqsim-controllability-operability
by equinorProcess controllability and operability — operating envelope mapping, turndown analysis, startup/shutdown sequencing, control valve sizing per ISA-75 (Fl, Fp, choked flow), rangeability, hunting/loop tuning, recycle stability. USE WHEN: a task involves operability assessment, turndown studies, control valve sizing, startup/shutdown procedures, or 'will this design operate?' questions. Complements neqsim-dynamic-simulation (transient solver) with steady-state operability framing.
neqsim-equipment-cost-estimation
by equinorEquipment-level CAPEX estimation — Turton/Peters/Ulrich/Seider correlations, CEPCI escalation, material/pressure factors, bare-module → grass-roots, AACE class 1–5, location factors, currency conversion. USE WHEN: a task requires a +30%/-30% Class-3/4 cost estimate at the equipment level (vessels, columns, pumps, compressors, HX, piping). Anchors on neqsim.process.costestimation.CostEstimationCalculator.
neqsim-field-economics
by equinorOil & gas field economics, NPV, IRR, cash flow, and fiscal regime modeling with NeqSim. USE WHEN: calculating project economics (NPV, IRR, payback), evaluating tax regimes (Norwegian NCS, UK, generic), building cost estimates (CAPEX/OPEX), or running Monte Carlo sensitivity analysis on economic outcomes.
neqsim-flow-assurance
by equinorFlow assurance analysis patterns for NeqSim. USE WHEN: predicting hydrate formation, wax appearance, asphaltene stability, CO2/H2S corrosion, pipeline hydraulics, water/liquid hammer screening, slug flow, thermal analysis, or chemical inhibitor dosing. Covers all flow assurance threats with NeqSim code patterns and industry standards.
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.