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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Nikovsky
Showing 8 of 8 skills
Nikovsky

hss-nginx-tls

by Nikovsky
star 2

nginx reverse-proxy and TLS hardening skill for HSS. Use when configuring local or production-like ingress, security headers, host/proxy trust, correlation ID propagation, and routing for web/api/auth/minio endpoints.

navigation main article SKILL.md
schedule Updated 3 months ago
Nikovsky

hss-architecture-security

by Nikovsky
star 2

HSS architecture and security guardrail skill for planning and reviewing cross-cutting changes across Next.js, NestJS, Prisma/PostgreSQL, Keycloak, MinIO, and nginx. Use when tasks span multiple services, touch security-critical flows, affect scaling safety, or require explicit threat/control verification.

navigation main article SKILL.md
schedule Updated 3 months ago
Nikovsky

hss-auth-keycloak

by Nikovsky
star 2

HSS authentication and authorization skill for Keycloak-based OIDC/JWT flows. Use when implementing login/session behavior, token handling, role mapping, refresh lifecycle, API auth guards, or security hardening of identity boundaries.

navigation main article SKILL.md
schedule Updated 3 months ago
Nikovsky

hss-docker-infra

by Nikovsky
star 2

Docker infrastructure operations skill for HSS local stack. Use when starting, stopping, validating, or troubleshooting PostgreSQL, Keycloak, nginx, and MinIO services, or when checking production-like local readiness.

navigation main article SKILL.md
schedule Updated 3 months ago
Nikovsky

hss-minio-storage

by Nikovsky
star 2

MinIO object-storage integration skill for HSS. Use when implementing or reviewing S3-compatible adapters, bucket bootstrap logic, private-by-default storage controls, presigned URL boundaries, and upload/download safety in API flows.

navigation main article SKILL.md
schedule Updated 3 months ago
Nikovsky

hss-next-web

by Nikovsky
star 2

Next.js web implementation skill for HSS. Use when working on routing, SSR protection, UI component placement, i18n with next-intl, data-fetching boundaries, and frontend security/privacy in `apps/web`.

navigation main article SKILL.md
schedule Updated 3 months ago
Nikovsky

hss-nest-api

by Nikovsky
star 2

NestJS API delivery skill for HSS. Use when implementing or reviewing controllers, services, DTO validation, error handling, RBAC enforcement, external adapters, and API-side security controls in `apps/api`.

navigation main article SKILL.md
schedule Updated 3 months ago
Nikovsky

hss-prisma-postgres

by Nikovsky
star 2

Prisma and PostgreSQL implementation skill for HSS. Use when changing schema, migrations, indexes, constraints, transaction behavior, query safety, or data consistency in `packages/database` and API persistence paths.

navigation main article SKILL.md
schedule Updated 3 months 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.