review-skill-improver

# Review Skill Improver

## Purpose

Analyzes structured feedback logs to:
1. Identify rules that produce false positives (high REJECT rate)
2. Identify missing rules (issues that should have been caught)
3. Suggest specific skill modifications

## Input

Feedback log in enhanced schema format (see `review-feedback-schema` skill).

## Analysis Process

### Step 1: Aggregate by Rule Source

```
For each unique rule_source:
  - Count total issues flagged
  - Count ACCEPT vs REJECT
  - Calculate rejection rate
  - Extract rejection rationales
```

### Step 2: Identify High-Rejection Rules

Rules with >30% rejection rate warrant investigation:
- Read the rejection rationales
- Identify common themes
- Determine if rule needs refinement or exception

### Step 3: Pattern Analysis

Group rejections by rationale theme:
- "Linter already handles this" -> Add linter verification step
- "Framework supports this pattern" -> Add exception to skill
- "Intentional design decision" -> Add codebase context check
- "Wrong code path assumed" -> Add code tracing step

### Step 4: Generate Improvement Recommendations

For each identified issue, produce:

```markdown
## Recommendation: [SHORT_TITLE]

**Affected Skill:** `skill-name/SKILL.md` or `skill-name/references/file.md`

**Problem:** [What's causing false positives]

**Evidence:**
- [X] rejections with rationale "[common theme]"
- Example: [file:line] - [issue] - [rationale]

**Proposed Fix:**
```markdown
[Exact text to add/modify in the skill]
```

**Expected Impact:** Reduce false positive rate for [rule] from X% to Y%
```

## Output Format

```markdown
# Review Skill Improvement Report

## Summary
- Feedback entries analyzed: [N]
- Unique rules triggered: [N]
- High-rejection rules identified: [N]
- Recommendations generated: [N]

## High-Rejection Rules

| Rule Source | Total | Rejected | Rate | Theme |
|-------------|-------|----------|------|-------|
| ... | ... | ... | ... | ... |

## Recommendations

[Numbered list of recom