Conciseness

The body is dense with decision rules and numbers ('cap at 15', 'prototype for one week', '10-20%', '50-100 real tasks') and never explains concepts Claude already knows, so it is well above level 3. But it is not level 5: the 6-quote 'Receipts' section duplicates content already in references/quotes.md, and the per-claim name attributions ('(Droege, Inbal Shani)', '(Turley, Penn)') throughout the tables add tokens that could be trimmed to the reference files.

Actionability

Guidance is fully executable for an instruction skill: exactly four diagnostic questions with routing consequences, a situation-to-play routing table, numbered plays with concrete thresholds and owners, a copy-paste-ready deliverable markdown template, and a grading rubric with explicit 1/3/5 anchors. Nothing stays at the 'describes rather than instructs' level; per the scoring notes, absence of code is not penalized because the guidance is specific and actionable.

Workflow Clarity

The sequence is explicit (Step 1 Diagnose -> Step 2 Pick the play -> Step 3 Run it, with 'Play A — Bet triage (always run first)'), each play has numbered steps, and there are explicit validation/done checkpoints: 'Done when every feature has one verdict and one owner', ship criteria on the retest shelf, and the memo template acting as a completeness checklist. It sits above level 4 because checkpoints and completion criteria are explicit, not merely implied.

Progressive Disclosure

Structure is good: the operational core (diagnosis, routing, plays, deliverable) is inline while framework detail and quote verification are split into real, one-level-deep references, clearly signaled in 'Go deeper' with accurate descriptions ('references/frameworks.md — every framework in this skill, how to run it'). It falls short of level 5 because the 'Receipts' quotes are duplicated inline in the body and in references/quotes.md, i.e. some reference-appropriate content remains in SKILL.md.

Specificity

Multiple concrete actions are stated: 'Decide which AI features to build, delay, shelve or kill, and whether each one gets better as models improve' and 'Produces an AI Bet Memo (feature-by-feature verdicts, retest triggers, scaffolding ledger, moat and safety check) from a roadmap or idea list'. A named output artifact with its contents and the decision verbs give comprehensive, concrete coverage; the level-4 anchor ('minor gaps in coverage') does not apply since both the action set and the deliverable are fully specified.

Completeness

Both 'what' (build/delay/shelve/kill decisions plus a defined AI Bet Memo deliverable) and 'when' ('Use for ...' with eight concrete trigger phrases) are explicitly answered, matching the level-5 example structure. It is not level 4 because the 'when' clause is not merely present but lists specific trigger phrases rather than a generic condition.

Trigger Term Quality

The 'Use for' list covers natural phrasings with synonyms and variations users would actually say: 'AI strategy', 'AI roadmap', 'what should we build with AI', 'will the next model make this obsolete', 'build vs wait for the model', 'will OpenAI/Anthropic build this', 'agent or chatbot', 'is this feature worth shipping'. This matches the level-5 anchor (comprehensive natural terms including synonyms); nothing common is obviously missing.

Distinctiveness Conflict Risk

Mostly distinct: the model-improvement framing ('will the next model make this obsolete', 'build vs wait for the model', 'will OpenAI/Anthropic build this') is a clear niche unlikely to grab the wrong skill. However, the broad triggers 'AI strategy' and 'AI roadmap' overlap a general strategy/roadmapping skill (the body itself hands off to a separate 'strategy' skill), which is a minor overlap risk keeping it below the level-5 anchor's 'minimal conflict risk'.