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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huawei-functiongraph-serverless-operator
by RaishinDeploy and operate Huawei FunctionGraph functions (event triggers, cold start optimization, concurrency), ServiceStage application lifecycle management, and CSE (Cloud Service Engine) Spring Cloud/ServiceComb microservice governance.
huawei-serverless-production-readiness
by RaishinReview FunctionGraph production readiness on Huawei Cloud — VPC access configuration, concurrency limits and reserved instances, cold-start optimization, observability via LTS and AOM, timeout configuration, dependency package size, custom vs managed runtimes, and ServiceStage application lifecycle.
finops-cloud-price-advisor
by RaishinFetch live public prices and build cost estimates for AWS, Azure, OCI, Scaleway, Gandi, Alibaba Cloud, and Tencent Cloud using each provider's public pricing API or official documentation. Supports live-environment cost analysis and prototype cost planning. Currency defaults to USD; EUR and CNY supported natively.
aws-migration-cutover-architect
by RaishinPlan, review, and de-risk AWS migrations and cutovers across discovery, dependency mapping, wave planning, AWS Application Migration Service, Migration Hub, test launches, acceptance tests, downtime windows, rollback, DNS, data consistency, and post-cutover validation. Use for migration planning and cutover readiness.
backstage-scaffolder-template-review
by RaishinUse this skill when reviewing Backstage Scaffolder software templates. Trigger when the user asks whether a template is safe for developer self-service, whether template RBAC gates are in place, whether input parameters are validated, whether a step action has excessive blast radius, or whether template outputs expose secrets.
nvidia-ai-infrastructure-operations
by RaishinUse this skill when reviewing NVIDIA AI infrastructure deployments — DGX, HGX, MGX systems, GPU server install posture, BMC and out-of-band exposure, BIOS/firmware levels, vGPU host configuration, and rack-scale power/cooling/networking readiness. Trigger when the user asks whether a GPU host is provisioned per NVIDIA reference architecture, whether the BMC is segmented, whether driver/firmware versions match the AI Enterprise support matrix, or whether the deployment is in scope for NCA-AIIO or NCP-AII certification expectations.
nvidia-triton-inference-serving-review
by RaishinUse this skill when reviewing Triton Inference Server deployments statically — `model_repository/` layout and `config.pbtxt` files, dynamic batching configuration, ensemble and BLS pipelines, custom backend (Python, C++, ONNX, OpenVINO, vLLM) trust posture, gRPC and HTTP endpoint authentication, response cache configuration, rate-limit and metrics exposure. Trigger when the user asks whether a Triton model repository or `tritonserver` invocation follows NVIDIA's published guidance and security expectations.
nvidia-tensorrt-llm-deployment-review
by RaishinUse this skill when reviewing TensorRT or TensorRT-LLM deployment artifacts statically — ONNX/PyTorch export pipelines, precision selection (FP16/BF16/INT8/FP8/INT4), calibration cache integrity, dynamic shape profiles, custom plugin loading, engine cache and serialized engine provenance, runtime memory pool sizing. Trigger when the user asks whether a TensorRT build script, calibration pipeline, or trtexec invocation follows NVIDIA's published guidance.
nvidia-ngc-nim-supply-chain-governor
by RaishinUse this skill when reviewing NVIDIA NGC and NIM supply chain posture — NGC org and team boundaries, API key scope and rotation, NIM container cosign verification against NVIDIA's published identity, model card and weights provenance, AI Enterprise entitlement posture, and air-gap mirror integrity. Trigger when the user asks whether NIM images are verified before deployment, whether NGC keys are scoped per environment, or whether the deployment is procurement-defensible for a regulated tenant.
nvidia-model-promotion-gatekeeper
by RaishinUse this skill when an operator is about to promote an NVIDIA NIM container from staging to production and needs a runtime-evidence go/no-go decision. The skill executes a fixed allowlist of cosign/crane/oras/grype commands against the candidate image, then emits a signed attestation JSON whose verdict is one of promote, block, or manual-review. Trigger when the user asks "is this NIM safe to promote", "verify this container before deploy", or hands the agent a `nvcr.io/...` image reference and a current-prod digest. Live tier counterpart to `nvidia-ngc-nim-supply-chain-governor` (which is static-review only).
nvidia-maestro
by RaishinRoute NVIDIA tasks to the narrowest specialist or team of specialists from the NVIDIA agent catalog. Use when you do not already know the specialist. Not for direct NVIDIA answers; Maestro classifies, dispatches, and synthesizes only. Dispatches single agent for focused tasks, parallel team (max 4) for multi-domain tasks. Never auto-dispatches the live-runtime promotion gatekeeper — requires explicit human confirmation with blast-radius and rollback before routing to any runtime-evidence specialist.
nvidia-gpu-operator-kubernetes-hardening
by RaishinUse this skill when reviewing NVIDIA GPU Operator deployments on Kubernetes — device plugin, MIG manager, NFD labels, time-sliced GPU configuration, container toolkit, securityContext posture, namespace tenancy, and admission policy coverage. Trigger when the user asks whether GPUs are being shared safely across tenants, whether MIG profiles are enforced, or whether the GPU Operator is deployed per NVIDIA hardening guidance.
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.