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...
must-gather-investigation
by scylladbInvestigate failed e2e tests from Ginkgo JSON reports and must-gather artifacts, systematically analyzing logs, events, and resource states to identify root causes.
release-notes-generator
by scylladbAn experienced Kubernetes operator developer agent that generates structured, concise release notes for the ScyllaDB Operator by analyzing git history and PR context.
fix-backport-conflicts
by scylladbFix inline merge conflict markers in backport PRs by resolving conflicts and recommitting cleanly with original metadata preserved. Use when a backport PR has unresolved conflict markers, a cherry-pick produced merge conflicts, or a PR has the 'conflicts' label and needs to be made ready for review. Supports bulk mode for multiple PRs.
commit-summary
by scylladbGenerate weekly commit summary reports for SCT repository. Use when asked to create a commit summary, weekly report, changelog, or "last week in SCT" issue. Applies to summarizing git commits from scylla-cluster-tests master branch for developer audiences. Covers running sct_commits_summary.py, filtering commits by importance, and writing prose summaries with embedded GitHub links.
code-review
by scylladbGuides AI-assisted code review of SCT pull requests. Use when reviewing a PR, checking a diff for correctness, evaluating method signature changes across class hierarchies, verifying override compatibility, checking import conventions, error handling patterns, backend impact, test coverage, or provision label requirements. Covers inheritance safety, polymorphic method audits, and SCT-specific review criteria.
writing-plans
by scylladbUse when asked to generate an implementation plan, draft a plan, save a plan, or design a feature rollout for the SCT repository. Supports two formats: full 7-section plans for multi-phase work (1K+ LOC, tracked in MASTER.md) and lightweight mini-plans for single-PR changes (under 1K LOC, stored in docs/plans/mini-plans/). Routes automatically based on PR plans label, user input, or task size estimate.
package-installation
by scylladbGuides writing remote package installation commands (apt-get, yum, dnf, zypper) in SCT code. Use when adding apt-get install/update, yum install, dnf install, or zypper install calls via remoter.run/remoter.sudo. Ensures timeouts, retries, and non-interactive flags are always present to prevent hangs in CI.
migrate-to-sizing
by scylladbMigrate SCT test configs and pipeline jobs from literal instance_type params to constraint-based sizing_db/sizing_loader/sizing_monitor. Use when converting instance_type_db, gce_instance_type_db, azure_instance_type_db, instance_type_loader, instance_type_monitor to sizing constraints. Covers identifying instance specs, choosing vcpu/memory/arch/disk constraints, running sizing preview, and handling multi-DC or special params like nemesis_grow_shrink_instance_type.
designing-skills
by scylladbGuides the design and structuring of AI agent skills for the SCT repository with multi-step phases, progressive disclosure, and dual-platform compatibility (GitHub Copilot and Claude Code). Use when creating new skills, reviewing existing skills, restructuring AI guidance into modular skill directories, editing SKILL.md files, or improving agent instructions.
writing-unit-tests
by scylladbGuides writing and debugging unit tests for the SCT framework using pytest conventions. Use when creating new test files in unit_tests/, adding test cases, mocking external services, setting up fixtures, or reviewing test coverage. Covers network-blocking patterns, FakeRemoter, moto for AWS mocking, monkeypatch, and common pitfalls.
writing-integration-tests
by scylladbGuides writing and debugging integration tests for the SCT framework that interact with real external services. Use when creating tests requiring Docker, AWS, GCE, Azure, OCI, or Kubernetes backends. Covers service labeling, credential skip patterns, Docker Scylla fixtures, resource cleanup, and common pitfalls.
profiling-sct-code
by scylladbProfile Python code in SCT to find CPU, memory, and concurrency bottlenecks using cProfile, scalene, memray, and py-spy. Use when a test or framework operation is unexpectedly slow, memory usage grows unbounded, you need to find which functions dominate CPU time, or you want to verify that an optimization actually improved performance. Covers profiling unit tests and full SCT test runs.
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.