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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pulumi-upgrade-provider
by pulumiAutomate Pulumi provider repo upgrades with the `upgrade-provider` tool. Use when upgrading a pulumi provider repository to a new upstream version, running `upgrade-provider`, and addressing its common failure modes like patch conflicts or missing module mappings.
new-doc
by pulumiCreate Pulumi documentation with proper frontmatter and menu structure.
blog-meta-image
by pulumiGenerate a feature image (1884x1256) and OpenGraph meta image (1200x628) for a blog post. Reads the blog post title, selects a feature template (neo, platform, rocket, shield, lightbulb, or logo variant), renders feature.png, then composites it with title text onto meta.png. Use when the user types /blog-meta-image or asks to create, generate, or regenerate a blog post's feature image, meta image, social card, or Open Graph image. Accepts optional arguments like feature template name or logo names.
pulumi-neo-handoff
by pulumiHand off the current thread to a new Pulumi Neo task as a one-way transfer. Use when the user explicitly asks to hand off, send, transfer, or continue current work in Pulumi Neo (e.g. "hand this to Neo", "continue in Neo", "/neo-handoff"). Do not load when the user only mentions Neo, asks what Neo can do, asks for an AI-written PR or preview explanation, or hands off to a different agent.
pulumi-overview
by pulumiUse this skill for any task that creates, modifies, inspects, or destroys cloud infrastructure or SaaS configuration, from one-off CLI operations to full multi-resource projects, across providers in the Pulumi ecosystem. A typical project spans many providers (AWS or Azure or GCP, Kubernetes, Cloudflare, Auth0, Datadog, Vercel, and others), and Pulumi drives them through one CLI, one state model, and one credential layer. Trigger even when the user does not name Pulumi; phrasings like "deploy this app," "provision a database," "stand up a VPC," "configure Auth0," "set up Datadog monitoring," or "tear down staging" qualify. Also trigger for tasks that migrate, port, or convert existing infrastructure code (Terraform, CloudFormation, CDK, Bicep, ARM) to Pulumi. Do not trigger for application runtime code that reads or writes data via cloud SDKs; that is application code, not infrastructure.
pulumi-esc
by pulumiGuidance for working with Pulumi ESC (Environments, Secrets, and Configuration). Use when users ask about managing secrets, configuration, environments, short-term credentials, configuring OIDC for AWS, Azure, GCP, integrating with secret stores (AWS Secrets Manager, Azure Key Vault, HashiCorp Vault, 1Password), or using ESC with Pulumi stacks.
pulumi-best-practices
by pulumiLoad when the user is writing, reviewing, or debugging Pulumi TypeScript/Python programs; asks about Output<T> or apply() usage; wants to create ComponentResource classes; needs to refactor resources without destroying them (aliases); is setting up secrets or config; or is configuring a pulumi preview/up CI workflow. Also load for questions about resource dependency order, parent/child resource relationships, or pulumi.interpolate.
pulumi-terraform-to-pulumi
by pulumiMigrate Terraform/OpenTofu projects to Pulumi, including translating HCL source code and/or importing Terraform state into a Pulumi stack. Use when a user wants to convert Terraform to Pulumi, migrate from HCL, or import tfstate into Pulumi. Do NOT trigger for general Terraform-vs-Pulumi comparisons or questions about using both tools side-by-side.
pulumi-cdk-to-pulumi
by pulumiLoad this skill when a user wants to migrate, convert, port, translate, or move an AWS CDK application (including CDK stacks, constructs, or CloudFormation-synthesized templates) to Pulumi. Phrases such as "convert CDK to Pulumi", "migrate CDK app", "port CDK stacks", "replace CDK with Pulumi", "stop using CDK". Do NOT load for general CDK questions, CDK-only help, or CDK vs Pulumi comparisons where no migration is requested.
pulumi-arm-to-pulumi
by pulumiConvert or migrate Azure ARM (Azure Resource Manager) templates, Bicep templates, or code to Pulumi, including importing existing Azure resources. This skill MUST be loaded whenever a user requests migration, conversion, or import of ARM templates, Bicep templates, ARM code, Bicep code, or Azure resources to Pulumi.
cloudformation-to-pulumi
by pulumiConvert, migrate, or import AWS CloudFormation stacks or templates into Pulumi programs. Load this skill whenever a user wants to move from CloudFormation to Pulumi, convert a CFN template, import existing CloudFormation-managed resources into Pulumi, or asks about CloudFormation-to-Pulumi migration in any form. Also load when the user mentions cdk-importer in a migration context.
pulumi-upgrade-provider
by pulumiAutomate Pulumi provider repo upgrades with the `upgrade-provider` tool. Use when upgrading a pulumi provider repository to a new upstream version, running `upgrade-provider`, and addressing its common failure modes like patch conflicts or missing module mappings.
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.