Content
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is concise, actionable, and well-organized with concrete per-step commands and a useful Honesty caveat. The chief gap is the absence of explicit validation/verification checkpoints between the staged operations.
Suggestions
Insert explicit validation checkpoints after baseline and train-room (e.g. confirm baseline written / trained model artifacts present) before proceeding, plus a feedback loop on failure.
Briefly note how to verify room-watch output is sane before trusting presence/vitals numbers, tying into the existing `ruview_claim_check` recommendation.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean and efficient: it assumes Claude's competence, uses one-line invocations per step, and avoids explaining concepts Claude already knows; every line earns its place. | 3 / 3 |
Actionability | Each step ships a concrete, executable command (`ruview_calibrate {step: "baseline"}` etc.) with a fallback invocation path, giving copy-paste-ready guidance. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced as a numbered list, but there are no explicit validation checkpoints between stages (e.g. confirming baseline capture succeeded before enrolling, or verifying the trained room before room-watch), which leaves feedback loops implicit. | 2 / 3 |
Progressive Disclosure | Under 50 lines with no external bundle files, so the well-organized 'Sequence' + 'Honesty' sections satisfy the simple-skill carve-out; references to other skills/tools are clearly signaled and one level deep. | 3 / 3 |
Total | 11 / 12 Passed |