agentic-plugins

Validate that required AAP MCP servers are accessible before executing automation skills. Use when: - Before any skill that requires AAP MCP access - "Validate AAP MCP", "Check if AAP is configured" - "Verify AAP connection", "Test AAP MCP servers" NOT for: actual automation tasks (use specialized skills instead).

Interactive skill creation and import with automated validation and marketplace compliance. Use when: - "Create a new skill" - "Import an existing skill" - "Create a new agent plugin" - "Add skill to <plugin>" - "Build skill for <rh-product>" - User mentions "skill builder", "contribute", "new skill", "import skill", or "new plugin" Two modes: create from scratch or import existing SKILL.md. Guides through discovery, definition, generation, and validation. Enforces SKILL_DESIGN_PRINCIPLES.md and agentskills.io spec.

Analyze AI model performance, GPU utilization, and cluster health on OpenShift AI. Use when: - "How is my model performing?" - "What GPUs are available in the cluster?" - "Show me inference latency for Llama" - "Check OpenShift cluster health metrics" - "Trace a slow inference request" - "Correlate errors across my inference stack" Query-driven, read-only analysis. Routes to the appropriate observability domain based on user intent. NOT for deploying models (use /model-deploy). NOT for debugging failed deployments (use /debug-inference).

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)

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.

Generate a consolidated health report across multiple OpenShift clusters. Verifies each kubeconfig context is a genuine OpenShift cluster before reporting. Non-OpenShift contexts are skipped by default. Collects node resources (CPU, memory, GPUs), namespace counts, and pod status into a single comparison view. Use when: - "Show me a report across all clusters" - "Compare cluster health" - "Multi-cluster status overview" - "How are my clusters doing?" - "Include all clusters including non-OpenShift" (override default filter) NOT for single-cluster deep-dives or troubleshooting specific pods.

Diagnose and fix `.catalog/` validation failures (schema, roster, banners, sample workflows, JSON mirror). Use when: - `make validate` or CI reports collection compliance errors - A PR adds skills but catalog was not updated - `collection.json` is out of sync with `collection.yaml` - Catalog metadata/fragments might have drifted from README/CLAUDE/SKILL golden sources Remediation is via the create-collection workflow and `catalog_yaml_to_json.py`—not by weakening checks.

Author and maintain Compass catalog-info.yaml manifests for agent plugins (skills, plugins, Locations, MCP inverse relations). Use when: - Adding or updating a skill and its Compass manifest - Registering an existing plugin in Compass (manifests only; not creating a new agent plugin) - MCP usage or orchestration changed in SKILL.md - Auditing bidirectional dependsOn/dependencyOf drift against repo Compass conventions File-based only: Read/Glob/Grep/Bash. For `.catalog/` marketplace metadata use create-collection. NOT for: `.catalog/` metadata (use create-collection) or automated Compass registration.

Run skill design compliance validation for agentic collections. Use when the user asks to: - "Check skill compliance" / "Validate skill design" / "Run compliance check" - "Verify my skills follow design principles" - Before committing skill changes Runs the validate_skills_tier2.py script against SKILL_DESIGN_PRINCIPLES.md.

Validate a CVE against a Red Hat container image using official SBOM attestations, Red Hat VEX data, and CVE metadata from MITRE/OSV.dev.

Complete end-to-end workflow for containerizing and deploying applications to OpenShift or standalone RHEL systems. Orchestrates /detect-project, /s2i-build, /deploy, /helm-deploy, and /rhel-deploy skills with user confirmation checkpoints at each phase. Supports S2I, Podman, Helm deployment strategies for OpenShift, and Podman/native deployments for RHEL hosts. Use this skill when user wants to go from source code to running application in one guided workflow. Supports resume after interruption and rollback on failure. Triggers on /containerize-deploy command.

Validate a CVE against Red Hat Enterprise Linux CoreOS (RHCOS) in a specific OCP release by extracting RPM packages and checking Red Hat VEX data.

Author or refresh `<plugin>/.catalog/collection.yaml` and related `.catalog/` artifacts from golden sources (SKILL.md, README, AGENTS.md, Lola marketplace). Use when: - Adding a new plugin or refreshing the collection catalog for GitHub Pages / tooling - Aligning catalog narrative, sample workflows, and decision guide with skills on disk - Preparing a PR after changing skills or marketplace metadata Outputs only under `<plugin>/.catalog/` (never overwrite README, SKILL, CLAUDE, or marketplace YAML).

**CRITICAL**: Use for ALL CVE discovery and listing. DO NOT call get_cves directly. Use when: "show critical CVEs", "CVEs on hostname X", "remediatable vulnerabilities", "impact of CVE-X", risk assessment. NOT for remediation (use `/remediation`). System-level: FIRST reply = pagination prompt (Step -1). Parsing: scripts/01-cve-response-parser.py.

Query MITRE, OSV.dev, and Go vulnerability database to produce a structured report of affected packages, ecosystems, and vulnerable version ranges for a CVE.

Diagnose OpenShift build failures including S2I builds, Docker/Podman builds, and BuildConfig issues. Automates multi-step diagnosis: BuildConfig validation, build pod logs, registry authentication, and source repository access. Use this skill when builds fail, hang, or produce unexpected results. Triggers on /debug-build command or phrases like "build failed", "S2I error", "can't pull builder image", "can't push to registry", "build timeout".

Diagnose local container issues with Podman/Docker including image pull errors, container startup failures, OOM kills, and networking problems. Automates multi-step diagnosis: container inspect, logs retrieval, image analysis, and resource constraint checking. Use this skill when containers fail to run locally before deployment. Triggers on /debug-container command or phrases like "container won't start", "podman run fails", "local container crashing", "container exits immediately".

Troubleshoot failed or slow InferenceService deployments on OpenShift AI. Use when: - "My InferenceService won't start" - "Model deployment is stuck" - "Inference endpoint returns errors" - "Model is slow / high latency" - "GPU scheduling failed for my model" Progressive diagnosis: status conditions, events, pod logs, GPU health, and observability analysis. NOT for deploying models (use /model-deploy). NOT for creating runtimes (use /serving-runtime-config).

Create and configure Data Science Projects on OpenShift AI with namespace setup, S3 data connections, pipeline server, and model serving enablement. Use when: - "Create a data science project" - "Set up a new namespace for ML work" - "Add an S3 data connection to my project" - "Configure the pipeline server" - "Enable model serving on my project" Bootstraps an RHOAI Data Science Project with proper labels, data connections, pipeline infrastructure, and model serving configuration. NOT for deploying models (use /model-deploy). NOT for creating workbenches (use /workbench-manage). NOT for managing pipelines after setup (use /pipeline-manage).

Analyze execution risk by classifying inventory, scanning extra_vars for secrets, and assessing scope. Use when: - "Execute on production", "Deploy to production" (as first step before launch) - "Is this execution safe?" - "Check execution risk" - "Validate the execution target" NOT for: launching jobs (use governed-job-launcher) or troubleshooting failures (use job-failure-analyzer).