القائمة
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.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| 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 |
Analyzes structured feedback logs to:
Feedback log in enhanced schema format (see review-feedback-schema skill).
For each unique rule_source:
- Count total issues flagged
- Count ACCEPT vs REJECT
- Calculate rejection rate
- Extract rejection rationales
Rules with >30% rejection rate warrant investigation:
Group rejections by rationale theme:
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]
# Analyze feedback and generate improvement report
/review-skill-improver --output improvement-report.md
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% |
Once confidence is high, this skill can:
Review Code -> Log Outcomes -> Analyze Patterns -> Improve Skills -> Better Reviews
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This creates a continuous improvement cycle where review quality improves based on empirical data rather than guesswork.
Create Claude Code skills with best practices, structure, validation, and testing. Use when designing or refining skills, prompts, references, or supporting files.
Use when the user wants a cited, structured read of local documents and project knowledge. Triggers on: "analyze these docs", "scan my project for context", "read the docs folder", "summarize what's in .beagle/concepts/", "extract context from docs/", "what's in this folder", "go read everything in X and tell me what's there". Also invoked programmatically by other beagle skills (prfaq-beagle Ignition, brainstorm-beagle reference points, strategy-interview context grounding) via the companion contract. Does NOT trigger on codebase lookups ("find this function", "search the repo"), web research (use web-research), LLM-as-judge evaluation (use llm-judge), or document editing (use humanize-beagle). Produces a written scan plan, parallel-subagent findings, and a cited synthesis report on disk — never inline prose, never unsourced claims.
Use when the user has a fuzzy idea and wants to shape it into a concrete project spec before planning or building. Triggers on: "brainstorm this", "I have an idea for...", "help me think through this project", "what should I build", "spec this out". Also catches vague feature descriptions needing structured questioning to clarify scope. Does NOT write code, plan implementation, review strategy docs, or run strategy interviews — produces a WHAT/WHY spec through dialogue, not a HOW plan.
Use when the user wants to pressure-test a product, internal-tool, or OSS concept against Amazon's Working Backwards PRFAQ gauntlet before committing to a spec. Triggers on: "work backwards", "write a PRFAQ", "press release first", "is this idea worth building", "pressure-test this concept", "filter this before brainstorm", "is this a real product". Also catches solution-first pitches ("I want to build X that does Y") and technology-first pitches ("use AI to...") that need customer-first filtering. Produces a binary pass/fail verdict, not a polished doc. Hardcore coaching — direct, skeptical, concrete. On pass, hands off to brainstorm-beagle with a concept brief. Does NOT write code, plan implementation, scaffold projects, or draft specs.
Use when the user wants web research: gathering cited, multi-angle evidence on a specific question. Triggers on: "research X for me", "do web research on", "look up sources for", "find citations for", "gather evidence on", "what does the web say about X". Also invoked programmatically by other beagle skills (prfaq-beagle Ignition, brainstorm-beagle reference points, strategy-interview context grounding) via the companion contract. Does NOT trigger on codebase lookups ("find this function", "search the repo"), local file search, LLM-as-judge evaluation, or paywalled/auth-gated scraping. Produces a written plan, parallel-subagent findings, and a cited synthesis report on disk — never inline prose, never unsourced claims.
generate release notes for changes since a given tag