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Response Rater

  • 58 installs
  • 36 repo stars
  • Updated July 14, 2026
  • oimiragieo/agent-studio

Helps with ai & agent building tasks.

About

response-rater is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • response-rater
  • AI & Agent Building
  • AI-coding skill

Response Rater by the numbers

  • 58 all-time installs (skills.sh)
  • Ranked #6,517 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 4, 2026 (Skillselion catalog sync)
npx skills add https://github.com/oimiragieo/agent-studio --skill response-rater

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Listed on Skillselion
Installs58
repo stars36
Last updatedJuly 14, 2026
Repositoryoimiragieo/agent-studio

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

Response Rater Skill

<identity> Response Rater - Rates responses and plans against quality rubrics. Provides scores, feedback, and improvement suggestions. </identity>

<capabilities>

  • Rating responses against rubrics
  • Validating plan quality
  • Providing improvement feedback
  • Generating quality reports

</capabilities>

<instructions> <execution_process>

Step 1: Define Rating Rubric

Use appropriate rubric for the content type:

For Plans:

DimensionWeightDescription
Completeness20%All required sections present
Feasibility20%Plan is realistic and achievable
Risk Mitigation20%Risks identified with mitigations
Agent Coverage20%Appropriate agents assigned
Integration20%Fits with existing systems

For Responses:

DimensionWeightDescription
Correctness25%Technically accurate
Completeness25%Addresses all requirements
Clarity25%Easy to understand
Actionability25%Provides clear next steps

Step 2: Evaluate Each Dimension

Score each dimension 1-10:

## Dimension Scores

### Completeness: 8/10

- Has objectives, steps, and timeline
- Missing risk assessment section

### Feasibility: 7/10

- Most steps are achievable
- Step 3 timeline is aggressive

### Risk Mitigation: 5/10

- Only 1 risk identified
- No mitigation strategies

### Agent Coverage: 9/10

- All steps have assigned agents
- Good agent-task matching

### Integration: 8/10

- Uses existing APIs
- Minor compatibility concerns

Step 3: Calculate Overall Score

Average the weighted scores:

Overall = (8×0.2) + (7×0.2) + (5×0.2) + (9×0.2) + (8×0.2) = 7.4/10

Step 4: Generate Recommendations

Provide actionable improvements:

## Recommendations

### High Priority

1. Add risk assessment section with 3-5 risks
2. Include mitigation strategies for each risk

### Medium Priority

3. Extend Step 3 timeline by 2 days
4. Add fallback plan for external API dependency

### Low Priority

5. Add success metrics for each step

Step 5: Make Pass/Fail Decision

Apply minimum score thresholds:

Task TypeMinimum Score
Standard7/10
Enterprise8/10
Critical9/10
## Decision

**Score**: 7.4/10
**Threshold**: 7/10 (Standard)
**Result**: PASS ✅

Plan approved with recommendations for improvement.

</execution_process>

<best_practices>

1. Consistent Rubrics: Use same dimensions for comparable content 2. Clear Thresholds: Define pass/fail criteria upfront 3. Actionable Feedback: Provide specific improvements 4. Prioritize Issues: Order recommendations by impact 5. Document Rationale: Explain score justifications

</best_practices> </instructions>

<examples> <usage_example> Plan Rating Request:

Rate this plan for user authentication implementation

Response:

## Plan Rating: User Authentication

### Scores

- Completeness: 8/10 - Good structure, missing tests section
- Feasibility: 9/10 - Realistic timeline and approach
- Risk Mitigation: 6/10 - Only 1 risk identified
- Agent Coverage: 9/10 - Good agent assignments
- Integration: 8/10 - Compatible with existing auth

### Overall: 8.0/10 ✅ PASS

### Top Recommendations

1. Add risk section with security and dependency risks
2. Include test plan for each authentication flow
3. Add rollback procedure for failed deployment

</usage_example> </examples>

Iron Laws

1. ALWAYS use the same rubric dimensions when rating comparable content — inconsistent dimensions make scores meaningless and prevent valid comparison across sessions. 2. NEVER issue a pass/fail decision without documenting score justification for each dimension — unjustified scores cannot be reviewed, challenged, or improved. 3. ALWAYS apply defined minimum thresholds (7/10 standard, 8/10 enterprise, 9/10 critical) — ad-hoc thresholds produce inconsistent approval gates that erode trust in the rating system. 4. NEVER provide vague recommendations — every recommendation must reference the specific dimension it addresses and state the concrete change required. 5. ALWAYS prioritize recommendations by impact — high-priority items that would materially improve the score must be clearly distinguished from low-impact suggestions.

Anti-Patterns

Anti-PatternWhy It FailsCorrect Approach
Using different rubric dimensions for comparable contentScores cannot be compared across sessions; the rating loses its evaluative valueAlways use the same rubric (plans rubric for plans, responses rubric for responses)
Omitting score justification for individual dimensionsScores without justification cannot be reviewed, verified, or acted uponDocument specific evidence for each dimension score (what was present, what was missing)
Setting thresholds arbitrarily per sessionInconsistent thresholds invalidate the pass/fail gate; teams lose confidence in approvalsAlways apply the defined thresholds: 7/10 standard, 8/10 enterprise, 9/10 critical
Providing vague recommendations ("improve quality", "add more detail")Vague feedback cannot be acted upon; no change results from the reviewReference the specific dimension, score gap, and required concrete change for each recommendation
Listing recommendations without priority orderingEqual-weight feedback causes raters to address low-impact items firstAlways order by impact: High (affects pass/fail threshold) before Medium before Low

Memory Protocol (MANDATORY)

Before starting:

cat .claude/context/memory/learnings.md

After completing:

  • New pattern -> .claude/context/memory/learnings.md
  • Issue found -> .claude/context/memory/issues.md
  • Decision made -> .claude/context/memory/decisions.md
ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.

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