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agentic-starter-kits-skills
agentic-starter-kits-skills contains 14 collected skills from red-hat-data-services, with repository-level occupation coverage and site-owned skill detail pages.
Skills in this repository
Deploy agents to OpenShift with auto-detected cluster config and refresh MLflow tracking tokens.
Add integration deployment tests (health-check on OpenShift) to any agent in the agentic-starter-kits repo — standard, external-registry, or pre-deployed. Creates conftest.py, test_deployment.py, __init__.py, adds test-integration Makefile target, and updates the CI workflow matrix. Use when implementing integration tests, deployment tests, or health-check tests for a new agent.
Validate whether a new agent template or example belongs in the agentic-starter-kits repo. Two modes: idea mode (interactive questionnaire, no code yet) or existing agent mode (auto-extract from code). Produces a GitHub Discussion draft with fit score and recommendations. Use when proposing a new agent, reviewing an existing contribution's fit, or before writing code for a new template.
Add behavioral testing (pytest + EvalHub) to an agent in the agentic-starter-kits repo. Covers runner compatibility, test files, golden queries, thresholds, EvalHub fixture, Containerfile, docs, and MLflow tracing verification. Use when implementing behavioral tests for a new agent or when the user mentions btest, behavioral tests, eval coverage, or test harness integration.
Adds manual MLflow trace wrapping for tool and agent spans in Level B and C agents where autolog doesn't cover everything.
Researches and classifies a framework's MLflow autolog support level (A, B, or C) to determine what manual tracing is needed.
Creates the tracing.py module with enable_tracing(), health check, and framework-specific autolog configuration.
Orchestrates end-to-end MLflow tracing integration into an agent template, coordinating all sub-skills from research through verification.
Deploy A2A-compliant agents to OpenShift/Kubernetes with kagenti integration. Use this skill when the user wants to deploy an agent with kagenti, mentions "kagenti deployment", "A2A agent deployment", "Agent Runtime", "Agent Card", or asks to deploy an agent that should be visible in kagenti UI. Also use when they mention the /.well-known/agent-card.json or /.well-known/agents.json endpoints, or ask about agent discovery and cataloging in OpenShift.
Reviews tracing integration code for correctness against the repo's established patterns and checklists.
Tests MLflow tracing end-to-end by starting servers, sending requests, and verifying spans appear correctly in the MLflow API.
Verifies tracing works correctly after integration by running both code review and live trace testing.
Wires enable_tracing() into the FastAPI lifespan and adds manual trace wrapping imports to main.py.
Run and validate behavioral tests (pytest + EvalHub E2E) for an agent in the agentic-starter-kits repo. Executes pytest with MLflow enrichment, verifies trace structure, runs EvalHub E2E, cross-agent consistency check, and generates a validation report. Use when running behavioral tests, validating btest setup, or re-running validation after fixes.