Conciseness

The body is dense and almost entirely operational — concrete costs ("typically 3–9 real builds per candidate", "sonnet 38,401 / haiku 28,508 tokens"), specific tool functions, and no explanation of concepts Claude already knows. It falls at the 4 anchor ("Efficient; minor instances of over-explanation that could be trimmed") rather than 5 because of real duplication: the "never edits the shipped file" rule is stated in both Step 4 and "What this skill does NOT do", the `../../eval/tuning/` files are each linked inline and again in "Related", and asides like "this SKILL.md is the runbook; the design doc is the honest account" are editorial padding. It is clearly not 3 — no whole section is unnecessary.

Actionability

Guidance is mostly executable with named interfaces throughout: "`knobs.ALL_KNOBS`", "`optimizer.propose_neighbors`", "`optimizer.score_candidate(records)`", "`optimizer.coordinate_ascent_step(current_settings, current_score, knob_name, neighbor_evaluations)`", the concrete tuning/held-out task IDs, and a worked recovery reference for the stall case. It sits at 4 ("Mostly executable guidance; concrete code or commands with minor gaps") rather than 5 because there are no copy-paste-ready command lines — e.g. how to actually invoke `generate_variant.py` or `optimizer` from the CLI is never shown, so the operator must reconstruct invocations from function signatures.

Workflow Clarity

The process is explicitly sequenced (scope confirmation → Step 1 floors → Step 2 search loop → Step 3 held-out check → Step 4 report) with validation checkpoints throughout: a spend-confirmation gate before any builds ("ask before spending real build budget"), a convergence criterion ("if no knob moved, stop — converged"), a held-out overfit check whose failure is elevated to "the headline finding", and an explicit feedback loop for stall recovery ("diagnose via the run's `journal.jsonl` (`started` vs. `result` event counts) and output-file mtimes... recover by recomputing the missing set from the filesystem"). This matches the 5 anchor (explicit validation steps, feedback loops for error recovery, a checklist for the final report); the batch-operation cap does not apply because validation is present.

Progressive Disclosure

Structure and signaling are good — references are one level deep and clearly marked (DESIGN.md, knobs.py, optimizer.py, generate_variant.py, the sibling skill), and the body is well-sectioned. But the score falls to the 3 anchor rather than 4 because the runbook's load-bearing content is deferred to files that are not part of the skill bundle: no `references/`, `scripts/`, or `assets/` exist, and every "[`../../eval/tuning/...`]" path resolves outside the bundle (unresolvable in this layout), while the body insists "**read that file first**" and Step 4 requires restating "Every limitation named in DESIGN.md's 'Known limitations' section" — content the bundle does not ship. The duplicated "Related" link list adds organization noise but is secondary to the unshipped-dependency gap; it is not 2 because the SKILL.md itself is a coherent, well-organized runbook rather than an inlined dump.

Specificity

The description lists multiple concrete actions with comprehensive coverage: "Runs a discrete coordinate-ascent search (the finite-difference analog of gradient descent for prose) over four wording knobs", "never edits the agent file directly, only proposes the winning wording as a diff to review", and names the concrete knobs ("`token_ceiling` margin, how hard the effort dial leans down under difficulty-uncertainty, and the calibration/adherence knobs"). It clearly matches the 5 anchor ("Lists multiple specific concrete actions; comprehensive coverage") rather than 4, since no capability gaps remain — even the read-only safety boundary is stated.

Completeness

Both questions are answered explicitly: the 'what' ("Tune the exact WORDING of model-right-sizer's four already-shipped research-grounded layers to maximize real-execution accuracy") and the 'when' via a concrete "Use when someone says..." clause with four quoted trigger phrases. This matches the 5 anchor verbatim in structure; it is not 4 because the 'when' is explicit and multi-phrase, not merely present-but-imprecise.

Trigger Term Quality

Trigger phrases are natural and user-voiced: "tune model-right-sizer's wording for accuracy", "optimize the budget-ceiling wording", "run a gradient descent / hill-climbing search on the agent prompt", "which wording maximizes budget-adherence accuracy" — including the synonym pairing "gradient descent / hill-climbing". It sits at the 4 anchor ("Good keyword coverage; a few natural terms missing") rather than 5 because common variations a user might actually say, such as "improve the prompt", "make the agent more accurate", or "prompt optimization", are absent, and coverage is centered on this one sibling-skill vocabulary.

Distinctiveness Conflict Risk

It carves out a clear niche and actively disambiguates the closest sibling: "not whether to include a layer (see `model-right-sizer-layer-ablation`), but how an included layer should be phrased". The triggers are scoped to wording-tuning phrasing, so overlap risk with the ablation or benchmark skills is minimal — a clean fit for the 5 anchor ("Clear niche with distinct triggers; minimal conflict risk").