Conciseness

Mostly efficient, but the same points are argued repeatedly: the intro's 'It does not run new experiments, dispatch sub-agents, or generate any new data' is restated nearly verbatim in 'What this does NOT do', and the 'rejected findings get equal prominence' principle is explained three separate times ('limitations stated as prominently as wins', 'a rejected/negative finding is not a lesser row', 'It does not average away or soften a rejected finding'). These could be tightened without losing content.

Actionability

For an instruction-only skill this is copy-paste-level specific: an exact ledger schema '{name, date, question asked, headline metric before → after, verdict (adopted / rejected / promising-not-proven / structural finding), source file}', a section-by-section report shape with sentence/row budgets, named source file paths, and a concrete figure list anchored to real numbers ('0.333 was later corrected to the true 0.167', '~16.7% vs. 94.4%'). Flexibility ('adjust to whatever the ledger actually supports') is explicitly justified.

Workflow Clarity

A clear sequence (load dataviz/artifact-design → read the full source inventory → build the ledger → group into narrative arc → write the report → figures → publish) with real checkpoints: 'Do not proceed to writing charts or prose from memory', 'run the palette validator before finalizing', and a recovery path for missing data ('say so in the figure's caption rather than interpolate invented points'). Not 5 because what to do when the palette validator fails is deferred entirely to the dataviz skill with no local guidance.

Progressive Disclosure

No bundle files exist, and the body's external references (eval DESIGN.md/results files, four sibling companion skills) are one level deep, clearly signaled as links, and the body explicitly refuses to restate them ('link to the source file for anyone who wants the proof'). Minor gap: step 2's per-experiment narrative descriptions inline substantial numeric detail that duplicates what the mandated ledger step already pulls from the source files.

Specificity

The description lists multiple concrete actions in third person ('Package up every result... into one condensed, research-paper-style EXECUTIVE report', 'Publishes a self-contained HTML report... with an abstract, a key-findings table, a handful of figures, limitations... and a reproducibility appendix'), matching the comprehensive-coverage anchor. It falls short of 5 only because the dense enumerations of internal experiment names ('coordinate-ascent passes', 'averaged-vs-additive token_ceiling_formula.py pivot') pad the length without adding action clarity.

Completeness

It explicitly answers both: what ('Package up every result... into one condensed, research-paper-style EXECUTIVE report with real charts, built entirely from numbers already recorded in this repo's own dated results files') and when ('Use when someone says "write up all the tuning results"...'), with concrete trigger phrases — a clear match to the top anchor.

Trigger Term Quality

The quoted triggers are natural phrases a user would actually say: "write up all the tuning results", "executive summary of the research", "package the findings into a report", "research report with charts", "summarize everything we've found so far for leadership". Not 5 because common variations (e.g. 'report the findings', 'write-up', 'brief leadership on the results') are missing and much of the remaining keyword mass is internal jargon rather than user-said terms.

Distinctiveness Conflict Risk

It carves a clear niche — synthesizing already-committed research results into an executive report, explicitly 'never invented or rounded up' — that is cleanly separated from the companion experiment-running skills, and its triggers ('executive summary of the research', 'research report with charts') would not naturally fire for those. Minimal conflict risk.