Conciseness

The ~540-line body is noticeably verbose and redundant: it restates the v2.0-vs-v2.1 changes repeatedly (Key Revisions, Phase 3, Phase 4, Important Principles, Summary, Version History all re-explain the +3 cap), and even contradicts itself (Important Principles and the Implementation Checklist still say '+5 point limit' despite the body reducing it to +3), padding the token budget with content Claude could infer.

Actionability

Provides concrete, executable guidance — specific scoring thresholds (e.g. 'P/C < 0.70 = 2 points', 'VIX < 12 AND within 5% of 52-week high'), named data sources with URLs, and a verified companion script (scripts/bubble_scorer.py) — with only minor gaps in how to invoke the script and how to fetch each metric.

Workflow Clarity

The four-phase process (Data Collection -> Quantitative -> Qualitative -> Final Judgment) is clearly sequenced with explicit validation checkpoints (the confirmation-bias checklist, the Phase 1 'Do NOT proceed without data' gate, double-counting self-checks), missing a 5 only because feedback-loop error recovery after a failed fetch is implicit rather than spelled out.

Progressive Disclosure

Structure is good: SKILL.md is an overview pointing one level deep to real, verified reference files (implementation_guide.md, bubble_framework.md, historical_cases.md, quick_reference.md, quick_reference_en.md) and a scoring script, each with a 'When to Load References' navigation guide; kept below 5 because substantial scoring detail is inlined in SKILL.md that could live in the references.

Specificity

Lists several concrete actions ('Evaluates market bubble risk', 'quantitative data-driven analysis', 'mechanical scoring', 'mandatory data collection') with named indicators (Put/Call, VIX, margin debt, breadth, IPO), though the actions are framed at the skill-process level rather than as a checklist of discrete capabilities, leaving minor coverage gaps.

Completeness

Explicitly answers both 'what' (evaluates bubble risk via quantitative Minsky/Kindleberger scoring) and 'when' ('Use when user asks about bubble risk, valuation concerns, or profit-taking timing'), but the 'when' clause could enumerate more of the trigger scenarios detailed in the body.

Trigger Term Quality

Includes natural trigger phrases a user would say ('bubble risk', 'valuation concerns', 'profit-taking timing'), with good coverage of the bubble-detection domain, though a few common phrasings ('are we in a bubble', 'is the market overheated') are absent from the description itself.

Distinctiveness Conflict Risk

The niche (US market bubble detection via a specific quantitative framework) is mostly distinct with a clear trigger ('Use when user asks about bubble risk'), with only minor overlap risk against generic valuation or market-analysis skills.