Content
68%Weight 40%Scale 1-5Reviews 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-structured, giving Claude a runnable calibration sequence. Its main weakness is the absence of validation checkpoints between batch training stages, which caps workflow clarity.
Suggestions
Insert a validation checkpoint after 'train-room' (e.g., verify the specialist models were produced and sanity-check held-out accuracy before running 'room-watch').
Add expected outputs or a quick check for each step (e.g., what a successful baseline capture looks like) so failures surface early in the pipeline.
Split the 'Honesty' caveats from the `ruview_claim_check` verification pointer into separate subsections so navigation is cleaner.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean (~30 lines), assumes Claude's competence, and does not explain concepts Claude already knows; only minor phrasing (e.g., 'Pure-Rust, edge-deployable (ADR-151)') could be trimmed, keeping it just below a 5. | 4 / 5 |
Actionability | Each step ships a concrete, copy-paste-ready tool invocation (e.g., `ruview_calibrate {step: "baseline"}`) plus the fallback `cargo run -p wifi-densepose-cli`; minor gaps (no flag/value examples) keep it just short of fully executable 5. | 4 / 5 |
Workflow Clarity | Four steps are clearly sequenced (baseline → enroll → train-room → room-watch), but training a bank of specialists is a batch operation with no explicit validation checkpoint between stages, capping workflow clarity at 3 per the batch-operation guideline. | 3 / 5 |
Progressive Disclosure | No bundle files exist, so all guidance is inline and organized into clear 'Sequence' and 'Honesty' sections at one level; structure is good with only minor organization gaps (the Honesty section mixes caveats with a verification pointer). | 4 / 5 |
Total | 15 / 20 Passed |