Conciseness

The body is dense and domain-specific with almost no padding — formulas, ARR estimation heuristics, and an attribution checklist that Claude cannot reconstruct from general knowledge. It sits at 4 rather than 5 because the ZIRP '~2–5x artificial premium' rule and the April 2026 meltdown context are stated inline in the Macro and AI Premium sections while also appearing in references/benchmarks.md, a small duplication that could be trimmed. Not 3 because there is no over-explanation of concepts Claude already knows.

Actionability

Concrete, executable guidance dominates: exact metric formulas ('multiple_compression_pct = (later_multiple - earlier_multiple) / earlier_multiple × 100'), a data-model table with per-field extraction methods, numeric ARR heuristics by stage, a rated cause checklist, and five handled edge cases. It falls short of 5 because step 5 directs 'Use the Visualizer tool' and 'Follow the CSS variable system' without any concrete example or spec, leaving the visualization step the least executable.

Workflow Clarity

Six clearly sequenced steps (gather → model → compute → attribute → visualize → summarize) with an explicit cause-rating checklist and data-integrity checkpoints ('note when a figure is estimated', 'flag your data confidence when ARR had to be estimated'). No error-recovery loop is specified — e.g., what to do when round data or ARR figures conflict or a search comes up empty for multiples — which keeps it below 5; the workflow is not destructive or batch-oriented, so no lower cap applies.

Progressive Disclosure

The dated benchmark tables are correctly split into references/benchmarks.md (verified to exist), referenced inline at three well-signaled points and again in a 'Reference Files' section, one level deep with no nesting. The body reads as overview-plus-workflow and navigation is easy; the minor inline restatement of macro context is a hint rather than a duplicated data dump.

Specificity

Lists multiple concrete actions — 'Analyze how a private SaaS company's ARR valuation multiple changed across funding rounds', 'attribute the compression or expansion to rate cycles and macro selloffs, growth deceleration, narrative shifts... competition, and investor demand', 'benchmarked against private-market medians and peers', and 'Research the rounds rather than answering from memory' — comprehensive coverage. Not 4 because there are no coverage gaps; every capability is named as a concrete action.

Completeness

Explicitly answers what ('Analyze how... ARR valuation multiple changed across funding rounds, and attribute the compression or expansion to...') and when ('Use this skill whenever the user asks about valuation compression, ARR multiples, round-to-round valuation or multiple changes, down rounds...'). Mirrors the anchor-5 example structure; not 4 because both halves are explicit and use concrete trigger phrases.

Trigger Term Quality

Covers natural user phrasings with synonyms and variations: 'valuation compression', 'ARR multiples', 'round-to-round valuation or multiple changes', 'down rounds', 'compare a VC-backed software company's funding rounds'. Matches the anchor-5 pattern of comprehensive natural-term coverage; no common variant of the ask is missing.

Distinctiveness Conflict Risk

A clear niche — ARR-multiple compression analysis for VC-backed SaaS funding rounds — with distinct triggers that would not plausibly fire for a generic finance or web-research skill. Third-person voice ('Analyze', 'attribute', 'Research') is used throughout.