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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install-zymtrace-backend
by zystem-ioUse when installing the zymtrace backend (the AI optimization platform that ingests CPU/GPU profiling data). Covers Kubernetes (Helm) and single-node Docker Compose. Handles license setup, choosing in-cluster vs external ClickHouse/Postgres/object storage, ingress with gRPC and TLS, and air-gapped installs via a custom image registry. Trigger phrases: "install zymtrace", "install zymtrace backend", "deploy zymtrace", "set up zymtrace on kubernetes", "set up zymtrace on EKS / GKE / AKS / on-prem", "helm install zymtrace", "docker compose zymtrace", "stand up the zymtrace platform", "first zymtrace install", "deploy the backend services".
configure-zymtrace-mcp
by zystem-ioUse when connecting a coding agent — Claude Code, OpenAI Codex, or Cursor — to the zymtrace MCP server so the user can analyze CPU and GPU flamegraphs through natural-language queries. Walks through finding the gateway URL, generating an auth token (if service-token auth is on), adding the server with the right command for the user's client, and verifying the connection. This skill is plumbing only — the analytical workflow lives in `optimize-cpu-workloads` and `optimize-gpu-workloads`. Trigger phrases: "connect zymtrace MCP", "set up zymtrace MCP", "configure zymtrace MCP", "add zymtrace to /mcp", "connect Claude/Codex/Cursor to zymtrace", "/mcp doesn't show zymtrace", "zymtrace MCP token", "Cursor zymtrace integration", "set up the zymtrace AI assistant".
expose-zymtrace-backend
by zystem-ioUse when configuring external/internal network exposure for an already-installed zymtrace backend — adding NodePort, LoadBalancer, or Ingress (NGINX or AWS ALB) with TLS. Edits the customer's canonical values file in place and applies via `helm upgrade --install`. Trigger phrases: "expose zymtrace", "expose the gateway", "make zymtrace accessible", "add ingress to zymtrace", "set up ALB for zymtrace", "internal ALB for zymtrace", "set up NGINX ingress for zymtrace", "add TLS to zymtrace", "get a real hostname for zymtrace", "add NodePort to zymtrace", "connect agents from another cluster".
install-zymtrace-profiler
by zystem-ioUse when installing the zymtrace profiler agent on Kubernetes (Helm DaemonSet), Docker, or as a binary with systemd. Covers CPU-only profiling, CUDA GPU profiling (CUDA 12.x or higher required; CUDA 11.x and below not supported), GPU metrics (utilization, memory, temperature, SM efficiency, Tensor Core, PCIe), framework-specific metrics (vLLM, SGLang, NVIDIA Dynamo-Triton), and air-gapped installs via a custom image registry. Connects the agent to an existing backend gateway. Trigger phrases: "install profiler", "install zymtrace profiler", "install zymtrace agent", "deploy the profiler", "deploy zymtrace DaemonSet", "set up GPU profiling", "set up CUDA profiling", "profile my GPU workloads", "install profiler on EKS / GKE / Slurm / bare-metal", "install profiler on every node", "start collecting profiles".
troubleshoot-zymtrace-backend
by zystem-ioUse when a deployed zymtrace backend isn't working as expected — no data appearing in the UI, profiles not arriving, ingest errors, ClickHouse storage full, license / auth failures, slow queries. Walks symptom → diagnosis → fix. Routes between backend (ingest, ClickHouse) and profiler (DaemonSet, CUDA injection) checks. Trigger phrases: "zymtrace not working", "no data in zymtrace UI", "zymtrace UI is empty", "no profiles appearing", "profiles not showing up", "ingest is failing", "clickhouse disk full", "license error in zymtrace", "license expired", "zymtrace queries are slow", "zymtrace broken after upgrade", "fix zymtrace", "diagnose zymtrace".
troubleshoot-zymtrace-profiler
by zystem-ioUse when the zymtrace profiler agent is misbehaving — pods crash-looping, image pull errors, OOMKilled, CPU profiles working but no GPU profiles, NVML library not found, PC sampling not producing SASS-level data, or license errors on the profiler side. Walks symptom → diagnosis → fix. Focused on the agent itself; for "no data anywhere" use troubleshoot-zymtrace-backend. Trigger phrases: "profiler not working", "profiler pods crashing", "profiler CrashLoopBackOff", "profiler ImagePullBackOff", "agent OOMKilled", "no GPU profiles", "CPU profiles work but GPU doesn't", "NVML library not found", "PC sampling not working", "profiler license rejected", "agent restart cycle", "zymtrace agent unhealthy", "fix the profiler".
upgrade-zymtrace-backend
by zystem-ioUse when upgrading a zymtrace backend that's already deployed via Helm. Covers image-tag-only bumps, chart-version bumps, and combined upgrades. Handles the migration job, --reset-then-reuse-values requirement, rollback when --atomic fails, and post-upgrade verification. Trigger phrases: "upgrade zymtrace", "upgrade zymtrace backend", "bump zymtrace version", "update zymtrace to 26.5.0 / latest", "helm upgrade zymtrace", "upgrade the chart", "bump backend image tag", "move zymtrace to a new release", "patch zymtrace", "roll back zymtrace upgrade".
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.