381,784 Collected SKILL.md files

Explore AI Agent Skills & Claude Prompts

Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.

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Showing 8 of 8 skills
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go-project

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Scaffold a Go project with the canonical layout — cmd/ entrypoints with a thin main, private packages under internal/, a separate tools module pinning Go developer CLIs (invoked directly via go tool, no GOBIN), Node tools pinned in package.json and run from node_modules/.bin, and a Makefile whose pr target runs the full local gate. Use when creating a new Go project, service, or repository, restructuring an existing Go repo to the standard layout, pinning Go or Node developer tooling, or adding standard Makefile targets (pr, fmt, vet, test, fuzz, build) to a Go codebase.

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schedule Updated 18 days ago
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go-testing

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Go testing conventions — table-driven tests with t.Run subtests, plain stdlib assertions (no testify or assertion frameworks), hand-written fakes behind small interfaces, t.Helper() helpers, httptest for HTTP handlers, and native fuzz targets (testing.F) with seed corpora. Use when writing or reviewing Go tests (_test.go files), testing a Go HTTP handler, adding a fuzz test or seed corpus to a Go package, or choosing go test flags for a Makefile or CI (-race, -fuzz, -fuzztime).

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schedule Updated 18 days ago
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terraform-validate

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Format and validate Terraform code with the fmt / init / validate / tflint loop. Use after editing any .tf file, before committing Terraform changes, when asked to lint, format, validate, or check Terraform modules and root configurations, or when setting up tflint for a repository.

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schedule Updated 19 days ago
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go-docs

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Go documentation conventions — a doc comment on every exported function, struct, type, and package, package comments in a dedicated doc.go for multi-file packages, godoc style (complete sentences beginning with the identifier's name), and LLM-ready CLI reference generation for cobra tools via a docgen helper built on cobra/doc. Use when writing or reviewing doc comments or godoc in Go code, documenting an exported Go API, package, function, or struct, deciding where a package comment or doc.go belongs, or generating markdown CLI documentation for a cobra-based Go tool.

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schedule Updated 18 days ago
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terraform-style

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Terraform/HCL style conventions for writing readable, reusable modules — collection types, resource naming, name_prefix, for_each toggles, variable and output grouping, null defaults, and canonical file layout. Use when writing, reviewing, or refactoring Terraform code (.tf files, modules, root configurations), or when deciding how to name resources, structure variables, or shape module outputs.

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schedule Updated 18 days ago
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terraform-module

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Scaffold a new Terraform module with the canonical file layout — terraform.tf, main.tf, variables.tf, outputs.tf, README.md — and house style. Use when creating a new Terraform module, adding a module under a modules/ directory, or restructuring an existing module to the standard layout.

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schedule Updated 18 days ago
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go-release

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Release engineering for Go projects — GoReleaser v2 with version ldflags into an internal/version package, SBOMs and multi-arch container images, tag-triggered GitHub Actions releases, CI running build/vet/test -race/govulncheck with SHA-pinned actions, and Dependabot coverage for gomod, tools, npm, and Actions. Use when setting up releases or GoReleaser for a Go project, writing GitHub Actions CI or release workflows for a Go repository, stamping version metadata into a Go binary at build time, or adding Dependabot coverage to a Go repo.

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schedule Updated 18 days ago
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go-style

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Modern Go code style for stdlib-first programs — error wrapping with %w, sentinel errors, structured logging with log/slog, context threading, consumer-defined interfaces, nil-safe constructors, net/http servers with method-pattern routing, flag/env configuration for services, and cobra commands with viper configuration for CLI tools. Use when writing, reviewing, or refactoring Go code (.go files, packages, services), adding error handling or logging to a Go program, building a Go CLI tool or subcommands (cobra, viper), or deciding how to shape interfaces, constructors, configuration, or HTTP handlers in Go.

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schedule Updated 16 days ago
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Browse Agent Skills by Occupation

23 major groups · 867 SOC occupations

Browse by Category

Explore agent skills organized by their primary use case

SKILLMD / CREATORS AND OCCUPATION CATEGORIES

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.

SEO KNOWLEDGE HUB & TECHNICAL OVERVIEW

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.

8 QUESTIONS

Frequently Asked Questions

A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.