381,784 Collected SKILL.md files

Explore AI Agent Skills & Claude Prompts

Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.

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RHEcosystemAppEng
Showing 12 of 30 skills
RHEcosystemAppEng

cluster-creator

by RHEcosystemAppEng
star 39

End-to-end OpenShift cluster creation using Red Hat Assisted Installer. Handles Single-Node OpenShift (SNO) and HA multi-node clusters on baremetal, vsphere, oci, nutanix. Use when: - "Create a new OpenShift cluster" - "Install OpenShift on my servers" - "Set up a single-node cluster for edge deployment" - "Deploy a production HA cluster" Complete workflow: cluster definition, ISO generation, host discovery/validation, role assignment, network configuration (VIPs, static networking), installation monitoring, credential retrieval. NOT for: - Listing existing clusters → Use `/cluster-inventory` skill - Modifying running clusters → Out of scope (Day-2 operations require direct cluster access) - Cluster upgrades (not yet supported)

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schedule Updated 1 month ago
RHEcosystemAppEng

cluster-inventory

by RHEcosystemAppEng
star 39

List and inspect OpenShift clusters across self-managed (OCP, SNO) and managed service (ROSA, ARO, OSD) deployments. Returns cluster name, ID, version, status, platform, and creation date. Use when: - "List all clusters" - "Show cluster status" - "What clusters are available?" - "Get details of cluster [name]" - "Show cluster events for diagnostics" Read-only operations. Does NOT modify clusters.

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schedule Updated 1 month ago
RHEcosystemAppEng

remediation-verifier

by RHEcosystemAppEng
star 39

**CRITICAL**: This skill must be used for remediation verification. DO NOT use raw MCP tools like get_cve or get_host_details directly for verification. Verify CVE remediation success by checking Red Hat Lightspeed CVE status, validating package versions, and confirming service health. Use when: - "Verify CVE remediation was successful" - "Confirm package updates were applied" - "Check if CVE-X is fixed on target systems" - "Validate remediation after playbook execution" This skill orchestrates MCP tools (get_cve, get_cve_systems, get_host_details) for remediation verification. **IMPORTANT**: ALWAYS use this skill instead of calling verification MCP tools directly.

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schedule Updated 1 month ago
RHEcosystemAppEng

remediation

by RHEcosystemAppEng
star 39

**CRITICAL**: Use this skill for ALL CVE remediation workflows. DO NOT use individual skills piecemeal for end-to-end remediation. Use when users request: - CVE remediation playbooks or security patch deployment - Multi-step remediation (validation → context → playbook → execution) - Batch remediation across multiple systems or CVEs - End-to-end CVE management (analysis + remediation + verification) - Prioritizing and remediating CVEs (not just listing them) - Emergency security response with immediate remediation plans DO NOT use for simple queries: - "List critical CVEs" → Use `/cve-impact` skill - "What's the CVSS score for CVE-X?" → Use `/cve-impact` or `/cve-validation` - Standalone impact analysis without remediation → Use `/cve-impact` This skill orchestrates 6 specialized skills (cve-impact, cve-validation, system-context, playbook-generator, playbook-executor, remediation-verifier) for complete remediation workflows.

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schedule Updated 1 month ago
RHEcosystemAppEng

nim-setup

by RHEcosystemAppEng
star 39

Configure NVIDIA NIM platform on OpenShift AI for optimized model inference. Use when: - "Set up NIM on my cluster" - "Configure NGC credentials for NIM" - "I want to deploy a NIM model but haven't set up the platform" - "Create the NIM Account CR" One-time prerequisite before deploying models with NVIDIA NIM runtime via /model-deploy. NOT for deploying models (use /model-deploy instead). NOT for vLLM or Caikit deployments (NIM-specific only).

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schedule Updated 1 month ago
RHEcosystemAppEng

s2i-build

by RHEcosystemAppEng
star 39

Create BuildConfig and ImageStream resources on OpenShift and trigger a Source-to-Image (S2I) build. Use this skill after /detect-project to build container images from source code on the cluster. Handles namespace verification, resource creation with user confirmation, build monitoring with log streaming, and failure recovery. Triggers on /s2i-build command. Run before /deploy.

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schedule Updated 1 month ago
RHEcosystemAppEng

vm-rebalance

by RHEcosystemAppEng
star 39

Orchestrate VM migrations across cluster nodes for load balancing, maintenance, and resource optimization. Use when: - "Move VM database-01 to worker-03" - "Rebalance VMs to optimize CPU load" - "Drain worker-02 for maintenance" - "Automatically rebalance the cluster" Supports Manual (user-driven) and Automatic (AI-driven) modes. NOT for creating VMs (use vm-create) or lifecycle only (use vm-lifecycle-manager).

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schedule Updated 1 month ago
RHEcosystemAppEng

vm-snapshot-delete

by RHEcosystemAppEng
star 39

Permanently delete virtual machine snapshots to free storage space. Use when: - "Delete snapshot [snapshot-name]" - "Remove old snapshots for VM [name]" - "Free up snapshot storage" Requires user confirmation before deletion. NOT for restoring VMs (use vm-snapshot-restore instead).

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schedule Updated 1 month ago
RHEcosystemAppEng

vm-lifecycle-manager

by RHEcosystemAppEng
star 39

Manage virtual machine lifecycle operations including start, stop, and restart. Use when: - "Start VM [name]" - "Stop the virtual machine [name]" - "Restart VM [name]" - "Power on/off VM [name]" This skill handles VM state transitions safely with user confirmation for each action. NOT for creating VMs (use vm-create) or deleting VMs (use vm-delete).

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schedule Updated 1 month ago
RHEcosystemAppEng

vm-snapshot-create

by RHEcosystemAppEng
star 39

Create virtual machine snapshots for backup and recovery. Use when: - "Create a snapshot of VM [name]" - "Backup VM [name] before upgrade" - "Take a snapshot of [vm]" Validates storage class snapshot support, CSI driver capabilities, and guest agent status before snapshot creation. NOT for VM cloning (use vm-clone to create independent copies).

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schedule Updated 1 month ago
RHEcosystemAppEng

vm-snapshot-restore

by RHEcosystemAppEng
star 39

Restore virtual machines from snapshots with strict safety confirmations to prevent data loss. Use when: - "Restore VM [name] from snapshot [snapshot-name]" - "Roll back VM [name] to snapshot" - "Recover VM [name] from backup" CRITICAL: Requires VM to be stopped and typed snapshot name confirmation before restore. NOT for creating snapshots (use vm-snapshot-create instead).

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schedule Updated 1 month ago
RHEcosystemAppEng

vm-snapshot-list

by RHEcosystemAppEng
star 39

List virtual machine snapshots across namespaces with status, age, and recovery information. Use when: - "List snapshots for VM [name]" - "Show snapshots in namespace [name]" - "What snapshots exist for [vm]?" Read-only operation - no user confirmation required. NOT for creating/deleting snapshots (use vm-snapshot-create/delete instead).

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schedule Updated 1 month ago
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Browse Agent Skills by Occupation

23 major groups · 867 SOC occupations

Browse by Category

Explore agent skills organized by their primary use case

SKILLMD / CREATORS AND OCCUPATION CATEGORIES

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.

SEO KNOWLEDGE HUB & TECHNICAL OVERVIEW

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.

8 QUESTIONS

Frequently Asked Questions

A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.