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.

search
expand_more
Active:
dstmrk
Showing 4 of 4 skills
dstmrk

stripe-webhooks

by dstmrk
star 0

Use when working with Stripe billing — handling API version `2026-04-22.dahlia` breaking changes (Invoice.subscription moved to invoice.parent?.subscription_details?.subscription, Subscription.current_period_end moved to items[0]), registering the 8 required webhook events (checkout.session.completed/expired, customer.subscription.updated/deleted, invoice.paid/payment_failed/payment_action_required, charge.dispute.created), debugging "pending" subscription rows after checkout, or implementing stale-pending recovery thresholds for idempotent AdE mutations (getStalePendingThresholdMs in receipt-service/void-service). Files: src/server/stripe/, src/app/api/stripe/, webhook handler.

navigation main article SKILL.md
schedule Updated 22 days ago
dstmrk

testing-patterns

by dstmrk
star 0

Use when writing or fixing Vitest tests — avoiding SonarCloud S6661 Blocker (every it()/test() must have at least one expect()), mocking classes correctly with function/class keyword (never arrow), prefixing vi.mock factory variables with "mock" for hoisting, mocking Drizzle's db.transaction() callback with a passthrough, stubbing NODE_ENV with vi.stubEnv, updating mocks after refactoring N queries into a JOIN, INSERT ON CONFLICT DO NOTHING for race conditions, sanitizing context before Sentry.captureException via sanitizeForTelemetry(), auth-first ordering in deleteAccount, conditional last_used_at writes to prevent write-amplification, react/cache deduplication across RSC and Route Handlers, simulating a hostile browser (sessionStorage/localStorage throwing SecurityError, in-app webview, cookies disabled) for UI components that read Web Storage, or mocking Sentry.withScope + scope.setFingerprint when testing logger.ts fingerprint-aware capture. Also lists the consolidated rate-limit thresholds for server ac

navigation main article SKILL.md
schedule Updated 21 days ago
dstmrk

sonar-quality-gate

by dstmrk
star 0

Use when fixing SonarCloud or Gitleaks findings — Cognitive Complexity > 15, S6861 readonly React props, S6772 ambiguous JSX spacing, S7780 escape sequences in template literals (use String.raw), S5852 ReDoS or S5122 CORS wildcard Security Hotspots (NOSONAR does not suppress hotspots), or curl-auth-header / generic-api-key false positives in docs requiring .gitleaksignore fingerprints. Also covers coverage exclusions in sonar-project.properties + vitest.config.ts, service worker exclusions, and the rule "ask the user when CI failure is opaque" instead of blind-fixing.

navigation main article SKILL.md
schedule Updated 22 days ago
dstmrk

react-patterns

by dstmrk
star 0

Use when writing or modifying React 19 / Next.js 16 App Router code in src/app/ or src/components/ — choosing between Server Components and Client Components, marking the client boundary with "use client", reading params/searchParams as Promises, calling cookies()/headers() (async in Next 16), passing server-computed values as props to client components (e.g. appHref() from src/lib/marketing-to-app-href.ts), composing shadcn/ui + Radix primitives, wiring TanStack Query/Table with the providers in src/components/providers.tsx, building forms with react-hook-form + Zod, applying optimistic UI / useTransition / useOptimistic for AdE flows that look instant despite 2-5s latency, ordering Tailwind 4 classes (prettier-plugin-tailwindcss), composing variants with cva + cn (tailwind-merge), and avoiding hydration mismatches with next-themes / Date / locale-sensitive output.

navigation main article SKILL.md
schedule Updated 21 days ago
Page 1 of 1

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.