Turn a running agent system into rubric-backed evidence, stored evaluation artifacts, and the next improvement backlog.
This skill treats AI agent evaluation as a repeatable workflow, not a one-off opinion. It reads implementation artifacts, writes or updates the test plan, initializes run folders, logs inputs and outputs, scores results with a rubric, and captures the next improvement loop.
Use $hwamony-agent-system-evaluator to create a test plan for my research agent with run folders, rubric criteria, and failure notes.Use $hwamony-agent-system-evaluator to score 5 sample runs, preserve the evidence, and summarize the biggest weaknesses for the next implementation pass.Use $hwamony-agent-system-evaluator to turn these runs into a backlog of prompt, routing, and implementation fixes.
- it focuses on per-run evidence, not vague quality judgments
- it stores evaluation artifacts so iteration has a paper trail
- it combines scoring, notes, and user feedback into one loop
- it is built to feed implementation backlogs, not just generate a report
- your agent or prompt system already exists and you want to test it properly
- you need run-by-run evidence instead of vague impressions
- you want rubric-based scoring and structured feedback
- you are preparing for the next implementation iteration
- per-run artifacts under
07-runs/run-###/ - evaluation summaries
- rubric-based scores
- feedback logs
- a clearer handoff back to implementation
- systems that already run but are still improving
- teams that need evidence before changing prompts, code, or routing
- workflows where evaluation needs to create the next implementation backlog
This skill is not for giving a thumbs-up or thumbs-down impression. If the system does not exist yet, use the builder or implementer first and come back when there is something real to evaluate.