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.
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vitest
by confluentincUse when the user asks about Vitest mocking, spies, stubs, fake timers, config, or test runner behavior. Triggers on questions like "vi.fn", "vi.spyOn", "vi.mocked", "Mocked<T>", "mockResolvedValue", "restoreMocks", "vitest mock", "createMockInstance", "useFakeTimers", or how to accomplish test double patterns with Vitest.
mcp-docs
by confluentincUse when the user asks about Model Context Protocol details (protocol spec, transports, authorization, tools/resources/prompts, sampling/elicitation/roots, server/client concepts, extensions, or SDK usage). Triggers on phrases like "MCP spec", "MCP transport", "streamable HTTP", "MCP authorization", "list_tools", "initialize handshake", "MCP SDK", "@modelcontextprotocol/sdk", or "how does MCP X".
pr-review
by confluentincReviews pull requests for the Confluent MCP server. Use when reviewing PRs, doing self-review before sharing with the team, or when the user mentions "review PR", "help with PR", "review changes", "self-review", "review local changes", or "check my PR". Focuses on MCP tool wiring, OpenAPI/type coupling, ESM stubbability, transport security, and project-specific patterns.
stale-expectations
by confluentincGenerate a Schema Registry compatibility report for Avro schemas in a project. Use when the user asks to check Avro compatibility, validate schema evolution, or report breaking changes. Do NOT trigger for Protobuf, JSON Schema, or Kafka client code generation.
trigger-overlap
by confluentincBuild a Confluent Cloud topic provisioning script with retention and compaction. Use when the user asks to create a topic, write a `create-topics.sh`, set retention, set compaction policy, provision topics for Confluent Cloud, or generate idempotent topic scripts.
confluent-skill-reviewer
by confluentincReview a Confluent agent skill in this repo against the Agent Skills spec (agentskills.io), Confluent conventions in CLAUDE.md, the PR template gates, and the evals-as-contract rule. Use this skill whenever the user asks to review, audit, validate, or lint a skill; opens or inspects a PR that adds or modifies anything under `skills/`; asks about spec conformance, lazy-loading, frontmatter shape, trigger overlap, or eval coverage; or wants a pre-merge sanity check on skill changes. Do NOT trigger for general code review of application code; security review; auditing schemas, producer/consumer configs, PII tagging, or Terraform generation for Schema Registry (handled by `kafka-schema-registry`); runtime/log analysis of skill behavior (use `tools/skill_review_dashboard.py`); or any changes that don't touch the `skills/` tree.
developing-kafka-python-client
by confluentincUse when the user wants to build a Python Kafka producer or consumer, add Schema Registry to existing Python code, migrate from raw JSON to schema-backed serialization, or scaffold a confluent-kafka-python project for Confluent Cloud, local Docker, or WarpStream. Also use when user wants to optimize Python Kafka client configuration for WarpStream.
flink-udf
by confluentincBuild and deploy Apache Flink user-defined functions (UDFs) in Java for stream processing over Kafka. Use this skill when users want to create scalar UDFs, user-defined table functions (UDTFs), or process table functions (PTFs) in Java, deploy them to Confluent Cloud or local Docker environments, and invoke them from Flink SQL or the Table API. Trigger on: Flink UDF, custom Flink function, process table function, PTF, UDTF, Flink user defined, extend Flink SQL, stateful stream processing with Flink. Do NOT trigger for: Kafka Streams UDFs (use kafka-streams-programming skill), general Flink job development without custom functions, CDC streaming data piplines that include Flink (prefer the confluent-cloud-cdc-tableflow skill), Flink connector setup, or Kafka producer/consumer code.
kafka-streams-programming
by confluentincArchitect, build, and debug Kafka Streams apps (JVM-embedded stream processing). Use when user mentions KStream, KTable, topology, TopologyTestDriver, StreamsBuilder, interactive queries, GlobalKTable, joins/windows/aggregations, or debugging issues (rebalancing, state stores, lag, deserialization errors). Also use when user wants to optimize Kafka Streams for WarpStream or tune Kafka Streams client configuration for WarpStream. Do NOT trigger for Flink, connectors, CDC, or plain producer/consumer.
kafka-schema-registry
by confluentincScan a project to identify Kafka applications, extract schemas from data models, tag PII fields, generate Terraform for Confluent Schema Registry registration, and produce a migration report with rollout ordering. Use this skill when a user asks to analyze a folder or repo for Kafka usage, extract schemas, audit producer/consumer configurations, or generate Terraform for Schema Registry.
good-skill
by confluentincGenerate a Confluent Cloud topic creation script with idempotency checks. Use when the user asks to create a topic, provision topics, or write a `create-topics.sh` for Confluent Cloud. Do NOT trigger for self-managed Apache Kafka, schema registration, Terraform generation, or Kafka Streams topology authoring.
inlined-refs
by confluentincGenerate a Kafka consumer group lag dashboard. Use when the user asks to monitor lag, build a dashboard for consumer lag, or wire up Prometheus exporters for Kafka. Do NOT trigger for producer metrics, broker JMX, or Streams-specific monitoring.
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.