Announce the mode in one line and proceed — never ask the user to pick.

Branch A — First-time Full-Scan

1. Measure scale, pick a scanning tier

git ls-files | grep -cE '\.(js|ts|jsx|tsx|vue|py|go|rs|java|kt|rb|php|cs|swift|c|cpp|h)$'

2. Scan along 4 dimensions

1. File Anatomy — in-file declaration order (imports → types → main logic → helpers → export)
2. State & Control Flow — naming conventions for async state, pagination, flags
3. Infrastructure — where cross-cutting utils live (interceptors, formatters, middleware)
4. Error Handling — try/catch vs global interceptor vs Result return; null-check habits

3. Apply signal-threshold noise reduction

Before interrupting the user, evaluate signal strength:

• Weak signal → auto-suppress: minority <5% AND count <10 → majority wins, minority goes to DONTs
• Strong signal → grill: near-even split, or semantic fork on a core dimension
• Small-project exception: sources ≲50, "3 vs 2" is NOT a majority → grill it

4. Resolve conflicts one at a time (Grilling Protocol)

For each strong-signal conflict, present exactly ONE question with 4 options:

Evidence: pathA uses style X, pathB uses style Y WARNING: Risk: mixing both fractures the project style Choose: 1 follow X 2 follow Y 3 this is evolution, update rules 4 I have a new rule

Suspend until the user answers, then proceed to the next conflict. Never stack questions.

5. Generate .ai-style-rules.md with three mandatory sections:

• [Golden Files] — real exemplar paths annotated with what they demonstrate
• [Naming & State-Control Rules] — concrete, checkable conventions
• [DONTs] — anti-patterns that must not propagate

6. Install the persistent hook

Ask the user for enforcement strength (use AskUserQuestion):

Branch B — Incremental Sniff

1. Read existing .ai-style-rules.md; if it has a commit fingerprint, git diff <last_hash> HEAD --stat to pinpoint delta
2. Read recent Git changes (git log -3 --stat → inspect suspect files on demand)
3. For oversized diffs (>hundreds of files): --stat summary only + sample the largest changes

…

quarkus-verification

为 Quarkus 项目执行发布前验证闭环，涵盖构建、测试、扫描与差异审查。

code-me-brand

将 Code-Me 品牌语气与视觉规范即时注入模型上下文，统一生成内容风格。