review-skill-improver

name	review-skill-improver
description	Analyzes feedback logs to identify patterns and suggest improvements to review skills. Use when you have accumulated feedback data and want to improve review accuracy.
user-invocable	false

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).

Hard gates

Run in order; do not emit the final Review Skill Improvement Report until each gate passes.

1. Input on record — The log is loaded from a stated path in the repo or from an attached artifact, not from memory or paraphrase. Pass: the report header or Summary names that path or states “attached feedback blob” with byte/line count.
2. Schema / shape — Entries match the enhanced schema (rule_source, verdict, rationale, etc. per review-feedback-schema). Pass: either all rows parse, or skipped malformed rows are counted and listed by row index (not silently dropped).
3. Aggregation before thresholds — Complete Step 1 (per–rule_source totals, ACCEPT vs REJECT, rejection rate, rejection rationales) for the full parsed set before labeling any rule “high-rejection” or writing recommendations. Pass: Summary includes “Unique rules triggered” consistent with the aggregation table.
4. Evidence-bound recommendations — Every recommendation includes at least one concrete evidence pointer (log row(s), or file:line + short quote) before Proposed Fix. Pass: Evidence is non-empty for each recommendation.

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:

## 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 recommendations in format above]

## Rules Performing Well

[Rules with <10% rejection rate - preserve these]

Usage

# Analyze feedback and generate improvement report
/review-skill-improver --output improvement-report.md

Example Analysis

Given this feedback data:

rule_source,verdict,rationale
python-code-review:line-length,REJECT,ruff check passes
python-code-review:line-length,REJECT,no E501 violation
python-code-review:line-length,REJECT,linter config allows 120
python-code-review:line-length,ACCEPT,fixed long line
pydantic-ai-common-pitfalls:tool-decorator,REJECT,docs support raw functions
python-code-review:type-safety,ACCEPT,added type annotation
python-code-review:type-safety,ACCEPT,fixed Any usage

Analysis output:

# Review Skill Improvement Report

## Summary
- Feedback entries analyzed: 7
- Unique rules triggered: 3
- High-rejection rules identified: 2
- Recommendations generated: 2

## High-Rejection Rules

| Rule Source | Total | Rejected | Rate | Theme |
|-------------|-------|----------|------|-------|
| python-code-review:line-length | 4 | 3 | 75% | linter handles this |
| pydantic-ai-common-pitfalls:tool-decorator | 1 | 1 | 100% | framework supports pattern |

## Recommendations

### 1. Add Linter Verification for Line Length

**Affected Skill:** `commands/review-python.md`

**Problem:** Flagging line length issues that linters confirm don't exist

**Evidence:**
- 3 rejections with rationale "linter passes/handles this"
- Example: amelia/drivers/api/openai.py:102 - Line too long - ruff check passes

**Proposed Fix:**
Add step to run `ruff check` before manual review. If linter passes for line length, do not flag manually.

**Expected Impact:** Reduce false positive rate for line-length from 75% to <10%

### 2. Add Raw Function Tool Registration Exception

**Affected Skill:** `skills/pydantic-ai-common-pitfalls/SKILL.md`

**Problem:** Flagging valid pydantic-ai pattern as error

**Evidence:**
- 1 rejection with rationale "docs support raw functions"

**Proposed Fix:**
Add "Valid Patterns" section documenting that passing functions with RunContext to Agent(tools=[...]) is valid.

**Expected Impact:** Eliminate false positives for this pattern

## Rules Performing Well

| Rule Source | Total | Accepted | Rate |
|-------------|-------|----------|------|
| python-code-review:type-safety | 2 | 2 | 100% |

Future: Automated Skill Updates

Once confidence is high, this skill can:

1. Generate PRs to beagle with skill improvements
2. Track improvement impact over time
3. A/B test rule variations

Feedback Loop

Review Code -> Log Outcomes -> Analyze Patterns -> Improve Skills -> Better Reviews
     ^                                                                    |
     +--------------------------------------------------------------------+

This creates a continuous improvement cycle where review quality improves based on empirical data rather than guesswork.