Conciseness

The body is dense with repo-specific operational knowledge and never explains generic concepts, but it carries real over-explanation: a session-history narrative ('this exact loop is how `dispatch_floor_awareness`... went from level 0 to level 4 across several real sessions — first a novel-use-case validation exposed the gap...') and repeated restatements of the blind/no-calibration-access discipline across multiple sections. Not 4: these are whole passages of rationale and history that could be trimmed or moved, not minor instances.

Actionability

Concrete and specific throughout — exact scripts with flags (`generate_variant.py --settings "..."`), exact functions (`statistics.mean`, `reasoning_budget.budget_adherence_ratio`, `classify_budget_adherence`, `optimizer.score_candidate`), exact parameters (`mode: "dry_run"`, Pass A only), and the exact test to run (`tests/model_right_sizer/test_tuning_knobs.py`). Not 5: the agent-dispatch mechanics and the rendered comparison table are described but not given as executable commands or templates, leaving minor gaps.

Workflow Clarity

Ten clearly sequenced steps with explicit validation checkpoints (step 4 validates the blueprint before mapping, step 10 runs the full validator list and pytest suite before committing) and a built-in feedback loop (step 8 re-render/re-dispatch/re-score/compare; step 9 explicit iterate/stop/escalate decision rules). The destructive/batch cap does not apply because validation steps are present and explicit.

Progressive Disclosure

No bundle files exist, and the body is well-sectioned ('Before starting', 'What to do', 'What this does NOT do', 'Related') with clearly signaled one-level-deep markdown links to repo files. Not 5: substantial inline content — the 'Two specific wording pitfalls' subsection and the origin narrative — is arguably reference material that a leaner SKILL.md would point to rather than inline; not 3: structure and reference signaling are good, not merely present.

Specificity

Lists multiple concrete actions end-to-end — 'Picks a task from `HOLDOUT_TASKS`', 'dispatches 3 INDEPENDENT BLIND dry-runs per candidate', 'averages the budget across draws', 'scores via `classify_budget_adherence` + `score_candidate`', 'proposes ONE wording change, and re-runs to check improvement' — with comprehensive coverage of the tuning loop. Not 4: there are no gaps in the action coverage; every phase of the workflow is explicitly named.

Completeness

Clearly answers both 'what' (a fully specified tuning loop against a real build's actuals) and 'when' (an explicit 'Use when someone says...' clause with concrete quoted trigger phrases). Matches the anchor for a complete what+when description with concrete triggers.

Trigger Term Quality

The 'Use when' clause quotes four genuinely natural user utterances ('tune the knobs against this blueprint/build', 'keep tuning until N%', 'how close does a blind estimate get to the actual cost'), giving good keyword coverage. Not 5: the phrases lean technical and miss simpler synonyms/variations a user might state the same request with; not 3: multiple natural trigger phrases are explicitly present.

Distinctiveness Conflict Risk

Explicitly disambiguates from its nearest neighbor — 'not the synthetic benchmark `model-right-sizer-prompt-tuning` searches, nor a fresh build per candidate' — carving out a clear niche with distinct triggers and minimal conflict risk.