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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exact-binomial-spending
by keavenBuild and document exact binomial group sequential designs with spending in gsDesign. Use when the user mentions toBinomialExact, gsBinomialExact, repeatedPValueBinomialExact, sequentialPValueBinomialExact, simBinomialSeasonalExact, vaccine/prevention efficacy with seasonal looks, or blinded information-based adaptation with fixed spending fractions.
survival-design-routing
by keavenChoose the appropriate gsDesign survival or exact-binomial workflow when requests involve calendar-time interim analyses, event- or information-driven survival looks, seasonal rare-event exact-binomial monitoring, or explicit randomization ratios; covers when to use gsSurvCalendar(), gsSurv(), simBinomialSeasonalExact(), and toBinomialExact().
graphicalmcpgsd2
by keavenGuide users through group sequential design with graphical multiplicity control using the graphicalMCP and gsDesign2 R packages. Use this skill whenever the user asks about: group sequential designs with multiple hypotheses, graphical multiplicity testing, sequential p-values with gsDesign2, combining graphicalMCP with gsDesign2, clinical trial designs with multiple endpoints and populations, Maurer-Bretz procedures, alpha-spending with multiplicity graphs, or adapting the gMCPLite vignette template. Also trigger when users mention spending time, information fraction, or sequential p-values in the context of group sequential or graphical testing.
multi-endpoint-sim
by keavenGuide users through multi-endpoint group sequential trial simulation with multiplicity-controlled testing. Use this skill when the user asks about: simulating trials with OS, PFS, and ORR endpoints, illness-death model simulation with gsDesign bounds, sequential p-values in simulation loops, combining graphicalMCP with gsDesign for simulation-based operating characteristics, cumulative rejection probabilities, or building a full pipeline from design through simulation to multiplicity-adjusted testing.
gsdesign2
by keavenGuide users through next-generation group sequential design using the gsDesign2 R package. Use this skill when the user asks about: gs_design_ahr, gs_power_ahr, gs_update_ahr, sequential_pval, average hazard ratio designs, non-proportional hazards, piecewise enrollment/failure rates, spending time, or information fraction computation.
gsdesign
by keavenGuide users through classical group sequential trial design using the gsDesign R package. Use this skill when the user asks about: group sequential boundaries, spending functions (sfLDOF, sfHSD, sfPoints), sample size for time-to-event or binomial trials, gsDesign objects, plotting group sequential bounds, gsSurvPower for power computation, or harm bounds (test.type 7/8).
gsdesignnb
by keavenGuide users through sample size calculation, group sequential design, and simulation for clinical trials with negative binomial (recurrent event) outcomes using the gsDesignNB R package. Use this skill when the user asks about: negative binomial sample size, recurrent event trials, overdispersed counts, event gaps, rate ratios, Wald test for count data, seasonal event rates, blinded or unblinded sample size re-estimation, group sequential designs for negative binomial endpoints, or the Zhu-Lakkis method.
illness-death
by keavenGuide users through simulating clinical trials using the illness-death model with response. Use this skill when the user asks about: multi-state models for oncology trials, simulating correlated OS/PFS/ORR endpoints, transition rates between disease states, illness-death model calibration, building ADTTE datasets from simulation, analysis cut date determination, or theoretical survival curves from transition rates.
simtrial
by keavenGuide users through clinical trial simulation using the simtrial R package. Use this skill when the user asks about: simulating survival trials, simfix, sim_pw_surv, cutting data at calendar or event times, weighted logrank tests, MaxCombo tests, or simulation-based power.
wpgsd
by keavenGuide users through weighted parametric group sequential design using the wpgsd R package. Use this skill when the user asks about: correlated test statistics across hypotheses, generate_bounds, closed_test, correlation matrices for nested populations, or parametric multiplicity adjustment with group sequential designs.
vldaft-model-development
by keavenUse when turning a statistical survival-model prompt into vldaft package code, especially new AFT distributions, cure models, censoring or truncation behavior, parameter/link definitions, likelihood/score implementations, C/Rust backend changes, gamlss2 family support, simulation checks, backend parity tests, and model-facing documentation.
gsdesignnb
by keavenDesign, simulate, and adapt clinical trials with negative binomial recurrent event endpoints using the gsDesignNB R package. Use this skill when the task involves: NB sample size or power, event gaps and Jensen correction, calendar-time group sequential design, blinded or unblinded information estimation, sample size re-estimation (SSR), recurrent-event simulation (constant or seasonal rates), interim data cuts, completer-based analyses, or non-inferiority/super-superiority designs for recurrent events.
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.