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.
Querying local SQLite index...
environment
by alistaircrollAccess environmental data for Montréal: public tree inventory (333,556 trees), air quality monitoring, green spaces, canopy coverage, water quality, and urban heat islands. / Accéder aux données environnementales de Montréal : inventaire des arbres publics (333 556 arbres), qualité de l'air, espaces verts, canopée, qualité de l'eau et îlots de chaleur.
safety
by alistaircrollAccess public safety and security data for Montréal: criminal incidents, emergency service interventions (fire, police), 311 citizen requests, traffic collisions, fire stations, police stations, coyote sightings, and fire hydrant locations. / Accéder aux données de sécurité publique de Montréal : incidents criminels, interventions des services d'urgence (incendies, police), demandes 311, collisions routières, casernes de pompiers, postes de police, signalements de coyotes et emplacements des bornes fontaines.
download-resource
by alistaircrollDownload raw files (CSV, GeoJSON, Shapefile, PDF, GBFS) from Montréal's open data portal. Handles non-DataStore resources that require direct download. / Télécharger des fichiers bruts (CSV, GeoJSON, Shapefile, PDF, GBFS) du portail de données ouvertes de Montréal.
culture-and-recreation
by alistaircrollEN: Access Montreal's parks, cultural facilities, programming, public art, and recreational infrastructure through CKAN datasets. FR: Accédez aux parcs, installations culturelles, programmation, art public et infrastructures récréatives de Montréal via les données CKAN.
montreal-infrastructure-data
by alistaircrollAccess and query Montreal's infrastructure datasets including snow removal status, garbage collection schedules, road closures, parking regulations, property assessment, nearby amenities, and street signage. Provides real-time data via REST API, SOAP API for snow removal, Overpass QL for amenities, and external APIs for utility outages.
budget-and-finance
by alistaircrollAccess Montréal's operating budget, contracts, subsidies, procurement, tax rates, real estate transactions, and participatory budget results. Analyze spending, track supplier activity, and explore financial accountability. / Accéder au budget de fonctionnement de Montréal, aux contrats, subventions, approvisionnement, tarifs fiscaux, transactions immobilières et résultats du budget participatif. Analyser les dépenses, suivre les fournisseurs et examiner la reddition de comptes financière.
data-freshness
by alistaircrollPatterns for checking when Montréal open data was last updated, detecting stale data, and understanding update schedules across different datasets. / Stratégies pour vérifier la fraîcheur des données, détecter les données périmées et comprendre les calendriers de mise à jour.
understand-ckan
by alistaircrollReference guide for the CKAN API powering Montréal's open data portal. Covers authentication, endpoints, pagination, error handling, and conventions. / Guide de référence pour l'API CKAN du portail de données ouvertes de Montréal. Couvre l'authentification, les points d'accès, la pagination, la gestion d'erreurs et les conventions.
permits-and-planning
by alistaircrollAccess building permit data, zoning information, urban planning, property assessments, and land use data for Montréal. / Accéder aux données de permis de construction, zonage, urbanisme, évaluations foncières et affectation du sol pour Montréal.
visualization
by alistaircrollPatterns for presenting Montréal open data as charts, maps, and tables suitable for agent-generated output: HTML/SVG inline charts, text-based tables, map links, and data formatted for external tools. / Stratégies de visualisation : graphiques HTML/SVG, tableaux texte, liens cartographiques, et formats pour outils externes.
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.