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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otel-weaver
by ollygardenOpenTelemetry Weaver registry authoring, codegen, and CI enforcement. Use when adopting Weaver, authoring or reviewing a registry (manifest, attributes, metrics, spans, events), writing Jinja2 templates against the resolved schema, migrating hand-maintained telemetry constants, or wiring `weaver registry check`/`generate`/`diff` into CI.
otel-collector
by ollygardenOpenTelemetry Collector component configuration. Use when authoring, reviewing, or debugging Collector YAML for a specific receiver, processor, exporter, connector, or extension — config keys, defaults, validation rules, signal support, stability levels, and component-level gotchas. Triggers on questions about specific components such as `log_dedup` / `logdedup`, `interval` (metric aggregation), `tail_sampling`, `drain`, `redaction`, `filter`, `transform`, `probabilistic_sampler`, `attributes`, `resource`, `k8s_attributes` / `k8sattributes` (Kubernetes metadata enrichment), `routing` (connector — route telemetry to pipelines by OTTL condition), `memory_limiter` (OOM safety valve / backpressure), `load_balancing` / `loadbalancing` (trace-ID/service-aware exporter that pins related telemetry to one backend, for scaling `tail_sampling`), `otlp` receiver (the canonical OTLP/gRPC + OTLP/HTTP ingress; `localhost` vs `0.0.0.0` default), `otlp` / `otlp_grpc` exporter (OTLP/gRPC egress with `sending_queue.batch`, `ret
otel-declarative-config
by ollygardenOpenTelemetry declarative YAML configuration for SDK setup. Use when configuring OpenTelemetry SDK providers (tracer, meter, logger), setting up OTLP exporters, defining sampling strategies, or writing otel config files. Triggers on "otel config", "OpenTelemetry YAML", "declarative configuration", "otelconf", "OTEL_CONFIG_FILE", "file_format", "configure tracing/metrics/logs export", or when the user is setting up telemetry pipelines via config files rather than code.
otel-go
by ollygardenOpenTelemetry in Go — SDK setup, API surface, breaking changes, contrib instrumentation libraries (otelhttp, otelgrpc, otelmongo), and performance tuning. Use when adding, reviewing, or configuring OpenTelemetry in a Go service. Triggers on "setup otel in go", "go telemetry", "go tracing", "otelconf go", "otelhttp", "otelgrpc", "TracerProvider go", "MeterProvider go", or any Go-related OTel question.
otel-java
by ollygardenOpenTelemetry in Java — Javaagent zero-code instrumentation, Spring Boot Starter, manual autoconfigure SDK, declarative YAML configuration, BOM dependency management. Use when adding, reviewing, or configuring OpenTelemetry in a Java service. Triggers on "setup otel in java", "java telemetry", "javaagent", "Spring Boot otel", "GlobalOpenTelemetry", "AutoConfiguredOpenTelemetrySdk", "TracerProvider java", or any Java-related OTel question.
otel-js
by ollygardenOpenTelemetry in Node.js / JavaScript / TypeScript — NodeSDK, declarative YAML configuration, auto-instrumentations, ESM vs CJS import patterns. Use when adding, reviewing, or configuring OpenTelemetry in a Node.js service. Triggers on "setup otel in node", "js telemetry", "node tracing setup", "NodeSDK", "auto instrumentation node", "TracerProvider node", or any Node.js-related OTel question.
otel-ottl
by ollygardenOpenTelemetry Transformation Language (OTTL) expert for writing and debugging telemetry transformations in the OpenTelemetry Collector. Use when authoring or reviewing `transform`, `filter`, `routing`, or `tail_sampling` processor configs, debugging OTTL syntax or semantics, transforming traces, metrics, logs, or profiles, or converting data-processing requirements into OTTL statements.
otel-sdk-versions
by ollygardenOpenTelemetry SDK and package version lookup across languages. Use when choosing the latest compatible released OpenTelemetry SDK or package version and locating setup docs or examples.
otel-semantic-conventions
by ollygardenOpenTelemetry semantic convention lookup and naming guidance. Use when selecting released semantic convention groups, attributes, or span naming rules, or when checking semantic convention compliance.
otel-span-events-to-logs-migration
by ollygardenMigrate OpenTelemetry Span Events (AddEvent, RecordException) to the Logs API following the OTEP 4430 deprecation plan. Use when migrating instrumentation from span events to log-based events, reviewing code that still uses AddEvent or RecordException, or planning a migration across a codebase.
otel-telemetrygen
by ollygardenConstruct telemetrygen commands for generating synthetic OpenTelemetry traces, metrics, and logs via OTLP. Use this skill whenever the user wants to generate test telemetry, load test a collector or backend, create synthetic OTLP data, send sample traces/metrics/logs to an endpoint, test collector pipelines or processors, validate OTTL transforms, test tail sampling, or mentions telemetrygen in any context. Also trigger when the user asks how to simulate telemetry traffic, stress test an observability stack, or produce sample data for dashboards.
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.