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.
Querying local SQLite index...
dev-env-private-config-symlink
by baneeishaqueIndustrial protocol for symlinking developer-private configuration files (.env, JSON, HTTP-client envs) from a sibling configurations-private repository into an application's working directory across multiple environments (Gitpod, Google Cloud Shell, NeverInstall, Ubuntu, macOS, Windows), with broken-target detection, case-mismatch diagnosis, and per-consumer audit.
ide-renderer-freeze-prevention
by baneeishaqueIndustrial protocol for preventing VS Code / Anti Gravity / Eclipse renderer freezes caused by unbounded bash output, chained shell calls, recursive tree walks, multi-path arguments, large heredocs, inverted-match assumptions, and large Edit/Create tool operations. Owns the ten recurring freeze patterns, the eleven-item per-call self-audit checklist, and the recovery protocol after a freeze occurs.
eclipse-pde-telemetry-resilience
by baneeishaqueAdd a fire-and-forget HTTP telemetry exporter to an Eclipse PDE / Tycho / OSGi plugin without coupling tool exit code to telemetry availability — vendored library source, native dependencies via target-platform Maven location, daemon-thread bulkhead, layered timeouts, store-and-forward retry files, flushed timestamped lifecycle logging, and defensive IApplication.start integration.
eclipse-pde-jdk-migration
by baneeishaqueEclipse PDE/Tycho workspace migration across JDK major versions — surveys JRE pins in `.classpath`/`MANIFEST.MF`/`*.launch`/`pom.xml`, drops obsolete PermGen flags, injects JDK 9+ `--add-opens` flags, and resolves IDE-only side issues (target-platform symbolic-name mismatches via PDE Maven `<location>` + bnd `<instructions>`, m2e proxy via `~/.m2/settings.xml`, empty source-folder paradox via `.gitkeep`) without touching CI/Tycho-consumed files.
eclipse-launch-stdout-capture
by baneeishaquePatch an Eclipse PDE `.launch` configuration so the runtime JVM's stdout and stderr are captured to a file adjacent to the `.launch` file, surfacing telemetry / lifecycle output (e.g., `[telemetry ...]` lines) that bypasses application-managed log appenders.
python-venv-repair
by baneeishaqueIndustrial protocol for detecting, diagnosing, and repairing broken Python virtual environments including circular symlinks, missing interpreters, and orphaned package installations.
python-package-version-update
by baneeishaqueIndustrial protocol for updating Python package versions in requirements files, installing updated packages in virtual environments, and verifying compatibility with existing codebase.
text-lines-sort-by-length
by baneeishaqueSort the lines of a text file by physical line length (ascending by default, descending with `--reverse`), with an optional preserved header block and stable tie-breaking. Useful for arranging onboarding command lists, log lines, or any line-oriented artifact from smallest to largest unit.
archive-size-preflight-and-extract
by baneeishaquePre-flight a zip / 7z / tar / tar.gz archive with 7-Zip before extracting — report compressed-on-disk size, uncompressed total, file count, folder count, and compression ratio so the user can decide if there is enough free disk space; then extract reliably to a sibling folder of the same name with progress-only output and verify the extracted size matches the pre-flight report. Includes NTFS cluster-overhead awareness for many-small-file archives.
vscode-user-settings-symlink
by baneeishaqueProtocol for relocating VS Code Insiders User folder to configurations-private and creating a direct symlink without intermediate subdirectories.
loan-foreclosure-analysis
by baneeishaqueIndustrial protocol for analyzing loan repayment schedules, calculating foreclosure savings, and making optimal financial decisions.
text-to-markdown
by baneeishaqueConvert structured plain-text data (delimiter-separated status trackers, lists, tables) into well-formatted Markdown with emoji status indicators, proper tables, and file renaming.
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.