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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paper2ppt
by aibot88Turn research papers into concise, editable PPT decks with a restrained discussion-class whiteboard style. Use when Codex needs to read a paper PDF or paper notes, optionally borrow Paper2Slides for content planning, and then create a simple editable PPTX for paper reading, discussion sessions, group meetings, or seminar reports.
mindfulness
by aibot88Cultivate defensive situational awareness, threat assessment, and mental clarity under pressure. Covers the Cooper color code awareness system, body language reading and intent detection, verbal de-escalation, moving mindfulness in public spaces, combat focus and the OODA loop, rapid grounding techniques for acute stress, context-specific integration, and ongoing review and refinement of awareness skills. Use when entering unfamiliar or potentially hostile environments, needing to assess a situation for safety, de-escalating a verbal confrontation, or integrating awareness practice into daily movement.
alignment-verification
by aibot88Zero-tolerance alignment verification protocol. Alignment is binary - elements are either in line or they're not. Report exact measurements, no tolerances, no "close enough."
convex-patterns
by aibot88Convex backend patterns for this project. Query/mutation/action structure, TypeScript recursion workarounds, auth patterns, resilient generation (10min actions), normalized schema design. Triggers on "convex", "mutation", "query", "action", "internalQuery", "internalMutation", "internalAction".
bazel
by aibot88Bazel build system skill for C/C++ projects. Use when writing BUILD files with cc_library and cc_binary rules, registering toolchains, configuring remote execution, debugging sandbox issues, using query and cquery for dependency graphs, or migrating from CMake to Bazel. Activates on queries about Bazel, BUILD files, cc_library, cc_binary, Bzlmod, bazel query, remote execution, or Bazel toolchain registration.
palantir-data-handling
by aibot88Implement Palantir Foundry data handling with PII protection, markings, and GDPR compliance. Use when handling sensitive data in Foundry, implementing data classifications, or ensuring compliance with privacy regulations. Trigger with phrases like "palantir data", "foundry PII", "palantir GDPR", "foundry data protection", "palantir markings".
palantir-install-auth
by aibot88Install and configure Palantir Foundry SDK authentication with OAuth2 or token auth. Use when setting up a new Foundry integration, configuring API credentials, or initializing the foundry-platform-sdk in your project. Trigger with phrases like "install palantir", "setup palantir", "palantir auth", "configure palantir API key", "foundry SDK setup".
palantir-multi-env-setup
by aibot88Configure Palantir Foundry across development, staging, and production environments. Use when setting up multi-environment Foundry deployments, managing per-environment credentials, or implementing environment-specific configurations. Trigger with phrases like "palantir environments", "foundry staging", "foundry dev prod", "palantir environment setup".
palantir-security-basics
by aibot88Apply Palantir Foundry security best practices for credentials, scopes, and access control. Use when securing API tokens, implementing least privilege access, or auditing Foundry security configuration. Trigger with phrases like "palantir security", "foundry secrets", "secure palantir", "palantir API key security", "foundry scopes".
palantir-deploy-integration
by aibot88Deploy Palantir Foundry integrations to cloud platforms with secrets management. Use when deploying Foundry-powered applications to production, configuring platform-specific secrets, or setting up deployment pipelines. Trigger with phrases like "deploy palantir", "foundry deploy", "palantir production deploy", "foundry Cloud Run".
governance-architect
by aibot88Design and save a complete governance ecosystem for agentic operations — 6 structured documents (authority matrix, hard boundaries, escalation protocols, policy generation loop, decision ledger spec, learning loop) written to $HOME/.ai-first-kit/. Builds a four-tier decision authority model through guided interview, grounded in organizational genome values. Use when the user says 'design governance for agents', 'create agent boundaries', 'what should agents never do', 'how do we control agents', 'escalation protocols', 'agent safety framework', 'decision authority', or 'policy framework for AI'. Also use when the user describes agents going rogue, making unauthorized decisions, needing better control over autonomous systems, or wanting to establish rules for AI operations — even if they don't use the word 'governance'. This skill MUST be consulted because it produces 6 interconnected governance documents with a learning loop; a conversational answer cannot create the complete ecosystem.
grad-ai-ethics
by aibot88Apply AI ethics frameworks (fairness, accountability, transparency, privacy) to evaluate AI systems for algorithmic bias, explainability gaps, and value alignment failures. Use this skill when the user needs to audit an AI system for ethical risks, design fairness constraints, assess explainability requirements, or when they ask 'is this AI system fair', 'how do we detect algorithmic bias', 'what are the ethical implications of this AI deployment', or 'how do we make this model explainable to stakeholders'.
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.