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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doc-writer
by grantkeeSequential editing pipeline for technical documentation. Decomposes human-writing guidelines into focused single-pass agents that each apply one narrow set of rules. Works for any professional technical documentation: architecture docs, READMEs, crate docs, guides, changelogs. Trigger on: "doc-writer", "edit docs", "polish docs", "run doc pipeline", "clean up this document", "make this sound human"
feynman-auditor
by grantkeeDeep business logic bug finder using the Feynman technique. Language-agnostic — works on Solidity, Move, Rust, Go, C++, or any codebase. Questions every line, every ordering choice, every guard presence/absence, and every implicit assumption to surface logic bugs that pattern-matching misses. Triggers on /feynman, feynman audit, or deep logic review.
tn-debug-e2e
by grantkeeDebug failing end-to-end tests in the telcoin-network repo (Narwhal/Bullshark consensus + Reth EVM). Trigger when the user shares e2e stdout/stderr, mentions a failing e2e test, asks about test_logs, or pastes node traces, consensus errors, or execution-engine failures. Covers panics, timeouts, races, epoch boundaries, restart failures, consensus hangs.
tn-add-benchmark
by grantkeeGenerate Criterion benchmarks for telcoin-network hot paths. Trigger on: "benchmark", "add bench", "performance test", "criterion", "measure latency", "throughput test"
tn-domain-consensus
by grantkeeDomain expert reference for the telcoin-network BFT consensus layer — Bullshark ordering, certificate construction and validation, vote aggregation, header chains, DAG invariants, and quorum math. Loaded by tn-rust-engineer and tn-domain-reviewer when work touches the primary, certifier, proposer, executor (consensus output), or aggregator code paths. NOT user-invocable. Loaded programmatically by tn-* agents via the Skill tool.
tn-domain-epoch
by grantkeeDomain expert reference for the telcoin-network epoch lifecycle and node-management layer. Loaded by tn-rust-engineer and tn-domain-reviewer when work touches epoch boundaries, EpochManager, RunEpochMode, GasAccumulator catchup, governance-driven config updates, or any state read/written at the start or end of an epoch. NOT user-invocable. Loaded programmatically by tn-* agents via the Skill tool.
tn-domain-execution
by grantkeeDomain expert reference for the telcoin-network execution layer — Reth integration, EVM block production, payload building, and the executor/engine boundary contract. Loaded by tn-rust-engineer and tn-domain-reviewer when work touches reth_env, payload builder, batch builder's block-shaping logic, base fee derivation, or any code that reads or writes EVM state. NOT user-invocable. Loaded programmatically by tn-* agents via the Skill tool.
tn-domain-storage
by grantkeeDomain expert reference for the telcoin-network storage layer — consensus DB (REDB), reth-db (MDBX), key encoding, table layout, atomic writes, epoch-scoped vs persistent tables. Loaded by tn-rust-engineer and tn-domain-reviewer when work touches the Database trait, table definitions, snapshot logic, or any read/write to the consensus DB. NOT user-invocable. Loaded programmatically by tn-* agents via the Skill tool.
tn-domain-worker
by grantkeeDomain expert reference for the telcoin-network worker layer — batch construction, transaction pool management, EIP-1559 fee calculation, beneficiary committee enforcement, and the worker/primary boundary contract. Loaded by tn-rust-engineer and tn-domain-reviewer when work touches the worker crate, batch-builder, transaction pool, batch fetcher, or quorum-waiter. NOT user-invocable. Loaded programmatically by tn-* agents via the Skill tool.
tn-nemesis-scan
by grantkeeDeep combined audit using iterative Feynman + State Inconsistency analysis across 8 phases with specialized agents. Language-agnostic. Dynamically discovers domain-specific patterns via Phase -1 before the main pipeline. Spawns nemesis-orchestrator which coordinates domain discovery, recon, mapping, interrogation, state checking, feedback loop, journey tracing, verification, and reporting. Triggers on /nemesis-scan or deep combined audit.
tn-review-contracts
by grantkeeCode Review & Security Analysis for tn-contracts
tn-review
by grantkeeCode Review & Security Analysis Skill
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.