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...
bazel-central-registry
by sluongngInspect Bazel Central Registry (BCR) modules and bzlmod dependencies. Use when Codex needs to find BCR modules or versions, audit which direct deps in a repo are upgradeable, compare current pins to live BCR releases, update `bazel_dep` or `single_version_override` entries in `MODULE.bazel` (including `include()` files), or inspect module dependency trees.
bazel-target-open
by sluongngResolve Bazel labels from terminal output and open the matching BUILD file target with the local bazel-target-open CLI, especially when tmux-fingers selected labels such as //pkg:target or @repo//pkg:target.
ask-bazel
by sluongngResearch and answer Bazel questions by reading the Bazel source tree, commit history, and GitHub context (Issues, PRs, Discussions). Use when the user asks how Bazel works, why behavior changed, when a feature/regression appeared, or asks version-specific Bazel questions tied to bazelbuild/bazel.
buildbuddy-flaky-tests
by sluongngFetch and triage recent BuildBuddy flaky-test data with BuildBuddyService RPCs. Use when Codex needs to list the latest flaky targets, match the Test Analytics flakes UI, get sample flaky invocations/log pointers, rank recent flakes, check whether a PR is already addressing a flaky target, or start reproducing and fixing the top flaky test.
buildbuddy-invocation-compare
by sluongngCompare two BuildBuddy invocation URLs or IDs to troubleshoot hermeticity/reproducibility and cache invalidation. Use when asked to diff canonical Bazel flags, find the first shared action-cache misses, inspect Action/ActionResult differences (inputs/command/env/platform/outputs), or analyze compact execution logs with `bb explain`.
buildbuddy-invocation-troubleshoot
by sluongngTroubleshoot BuildBuddy invocations via BuildBuddyService (HTTP JSON or gRPC). Use when asked to debug a BuildBuddy invocation, find failed targets and stdout/stderr, inspect remote cache vs RBE metadata, retrieve execution details/profiles/logs, download raw build events or Bazel profiles, or fetch cache scorecard data.
buildbuddy-log-root-cause
by sluongngDiagnose BuildBuddy or bb CLI log snippets and customer reports when there is no clean invocation URL, especially FastCDC capability warnings, BES upload failures, confusing invocation IDs, cache proxy behavior, executor UI questions, and source-backed root-cause analysis.
buildbuddy
by sluongngRoute public BuildBuddy troubleshooting, source research, invocation comparison, action reproduction, log-root-cause, flaky-test, and usage-analysis requests to the focused BuildBuddy skills bundled in this plugin.
buildbuddy-usage-analysis
by sluongngAnalyze BuildBuddy billing usage and trends data, fetch GetUsage/GetTrend/GetStatHeatmap/GetStatDrilldown/SearchInvocation/SearchExecution data, compare periods, spot anomalies, and recommend cost-saving actions while protecting API keys.
buck2-adoption-research
by sluongngResearch and implement Buck2 adoption work that bridges Bazel ecosystem expectations, including rules_go or Gazelle comparisons, BCR strategy, Starlark API compatibility, BuildBuddy Workflow bootstrap, release assets, and fixture-backed validation.
buck2-stack-maintainer
by sluongngMaintain the Buck2 BuildBuddy patch stack in a local Buck2 checkout by rebasing fork/stack onto origin/main, publishing bazel-rbe-style merge commits to fork/main with origin/main as first parent, triggering or polling BuildBuddy Workflows, and fixing failed stack commits.
buildbuddy-action-reproduce
by sluongngReproduce a specific Bazel remote action from a BuildBuddy invocation and generate a customizable `bb execute` replay command. Use when a user provides an invocation URL/ID and wants to re-run one action, modify command args/env/exec properties, or pin execution to a specific executor using scheduler debug properties.
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.