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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ecosystem-services-assessment
by baratadiegoMaps and quantifies ecosystem services including carbon stocks, water yield, soil erosion, and habitat quality with trade-off analysis. Use this skill when the user mentions ecosystem services, InVEST models, ES mapping, carbon sequestration, water yield estimation, RUSLE erosion modeling, habitat quality, pollination services, trade-off analysis, PES (payments for ecosystem services), natural capital, or ES valuation.
landscape-connectivity
by baratadiegoAnalyzes landscape connectivity using graph theory, resistance surfaces, and corridor identification for conservation planning. Use this skill when the user mentions habitat connectivity, wildlife corridors, Circuitscape, least-cost paths, resistance surfaces, IIC/dPC connectivity metrics, stepping stones, patch importance ranking, betweenness centrality, fragmentation analysis, landscape graphs, or pinchpoint identification.
spatial-prioritization
by baratadiegoSolves systematic conservation planning problems using integer linear programming (prioritizr), Marxan, or Zonation for protected area design. Use this skill when the user mentions conservation planning, 30x30 targets, Marxan, Zonation, prioritizr, irreplaceability, boundary length modifier (BLM), minimum set problems, representation targets, systematic conservation, or protected area network design.
species-distribution-modeling
by baratadiegoRuns the complete species distribution modeling (SDM/ENM) pipeline: occurrence preparation, model fitting (MaxEnt, ensemble), thresholding, projection under climate scenarios, and interpretation. Use this skill when the user mentions habitat suitability, niche modeling, MaxEnt, biomod2, potential distribution, range maps, suitable area mapping, climate projections, invasion risk, range shift analysis, suitability mapping, ENM, ecological niche model, or calibration area definition.
geoprocessing-for-ecology
by baratadiegoHandles spatial data operations: reprojection, raster stacking, clipping, extraction, and environmental predictor downloads for ecological analyses. Use this skill when the user needs CRS reprojection, raster masking or cropping, spatial extraction, buffer creation, raster resampling, spatial joins, GeoTIFF processing, shapefile operations, WorldClim/CHELSA/ERA5 predictor downloads, GDAL operations, or predictor stack preparation.
acoustic-monitoring
by baratadiegoProcesses passive acoustic monitoring (PAM) data to compute soundscape indices and detect species from audio recordings. Use this skill when the user mentions acoustic monitoring, bioacoustics, soundscape ecology, acoustic indices (ACI, NDSI, ADI), BirdNET, AudioMoth, bat detectors, dawn chorus analysis, passive acoustic recorders, species detection from audio, or sound diversity metrics.
camera-trap-processing
by baratadiegoProcesses camera trap image records into structured detection data, activity patterns, and trap effort summaries. Use this skill when the user mentions camera traps, wildlife cameras, trap nights, detection events, diel activity patterns, camtrapR, temporal overlap indices (Dhat), RAI (relative abundance index), camera station data, detection history generation, or independence thresholds for photo events.
community-ecology-ordination
by baratadiegoPerforms multivariate community ecology analyses including ordination, diversity metrics, and assemblage comparisons. Use this skill when the user mentions species composition, NMDS, PCA ordination, PERMANOVA, beta diversity, alpha diversity, species richness, Bray-Curtis dissimilarity, indicator species analysis, cluster analysis, species-by-site matrices, diversity indices, or assemblage structure comparisons.
population-viability-analysis
by baratadiegoBuilds matrix population models (Leslie/Lefkovitch) and runs stochastic PVA simulations to assess extinction risk and IUCN criteria. Use this skill when the user mentions PVA, population viability, lambda growth rate, Leslie or Lefkovitch matrices, quasi-extinction thresholds, elasticity or sensitivity analysis, stochastic population projections, minimum viable population (MVP), or IUCN Criterion E assessment.
ecological-impact-assessment
by baratadiegoQuantifies ecological impacts using BACI designs, landscape fragmentation metrics, and pressure indices. Use this skill when the user mentions BACI analysis, before-after-control-impact, impact assessment, disturbance effects, land use change impacts, fragmentation metrics, landscape metrics, pressure or threat indices, intervention effectiveness, management outcome evaluation, or control-impact comparisons.
predictive-modeling-best-practices
by baratadiegoGuides predictor selection, collinearity checks, cross-validation strategy, and hyperparameter tuning for ecological predictive models. Use this skill when the user mentions VIF, collinearity, feature selection, spatial cross-validation, block CV, hyperparameter tuning, overfitting prevention, data leakage auditing, background point selection, pseudo-absence generation, ENMeval tuning, regularization, or spatial autocorrelation correction.
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.