Agent Evaluation
Overview
LLM-as-judge evaluation framework that scores AI-generated content on 5 dimensions using a 1-5 rubric. Agents evaluate outputs, compute a weighted composite score, and emit a structured verdict with evidence citations.
Core principle: Systematic quality verification before claiming completion. Agent-studio currently has no way to verify agent output quality — this skill fills that gap.
When to Use
Always:
- Before marking a task complete (pair with
verification-before-completion) - After a plan is generated (evaluate plan quality)
- After code review outputs (evaluate review quality)
- During reflection cycles (evaluate agent responses)
- When comparing multiple agent outputs
Don't Use:
- For binary pass/fail checks (use
verification-before-completioninstead) - For security audits (use
security-architectskill) - For syntax/lint checking (use
pnpm lint:fix)
The 5-Dimension Rubric
Every evaluation scores all 5 dimensions on a 1-5 scale:
| Dimension | Weight | What It Measures | | ---------------- | ------ | --------------------------------------------------------------------------------- | | Accuracy | 30% | Factual correctness; no hallucinations; claims are verifiable | | Groundedness | 25% | Claims are supported by citations, file references, or evidence from the codebase | | Coherence | 15% | Logical flow; internally consistent; no contradictions | | Completeness | 20% | All required aspects addressed; no critical gaps | | Helpfulness | 10% | Actionable; provides concrete next steps; reduces ambiguity |
Scoring Scale (1-5)
| Score | Meaning | | ----- | ------------------------------------------------------------- | | 5 | Excellent — fully meets the dimension's criteria with no gaps | | 4 | Good — meets criteria with minor gaps | | 3 | Adequate — partially meets criteria; some gaps present | | 2 | Poor — significant gaps or errors in this dimension | | 1 | Failing — does not meet the dimension's criteria |
Execution Process
Step 1: Load the Output to Evaluate
Identify what is being evaluated:
- Agent response (text)
- Plan document (file path)
- Code review output (text/file)
- Skill invocation result (text)
- Task completion claim (TaskGet metadata)
Step 2: Score Each Dimension
For each of the 5 dimensions, provide:
- Score (1-5): The numeric score
- Evidence: Direct quote or file reference from the evaluated output
- Rationale: Why this score was given (1-2 sentences)
Dimension 1: Accuracy
Checklist:
- [ ] Claims are factually correct (verify against codebase if possible)
- [ ] No hallucinated file paths, function names, or API calls
- [ ] Numbers and counts are accurate
- [ ] No contradictions with existing documentation
Dimension 2: Groundedness
Checklist:
- [ ] Claims cite specific files, line numbers, or task IDs
- [ ] Recommendations reference observable evidence
- [ ] No unsupported assertions ("this is probably X")
- [ ] Code examples use actual project patterns
Dimension 3: Coherence
Checklist:
- [ ] Logical flow from problem → analysis → recommendation
- [ ] No internal contradictions
- [ ] Terminology is consistent throughout
- [ ] Steps are in a rational order
Dimension 4: Completeness
Checklist:
- [ ] All required aspects of the task are addressed
- [ ] Edge cases are mentioned (if relevant)
- [ ] No critical gaps that would block action
- [ ] Follow-up steps are included
Dimension 5: Helpfulness
Checklist:
- [ ] Provides actionable next steps (not just observations)
- [ ] Concrete enough to act on without further clarification
- [ ] Reduces ambiguity rather than adding it
- [ ] Appropriate for the intended audience
Step 3: Compute Weighted Composite Score
composite = (accuracy × 0.30) + (groundedness × 0.25) + (completeness × 0.20) + (coherence × 0.15) + (helpfulness × 0.10)
Step 4: Determine Verdict
| Composite Score | Verdict | Action | | --------------- | --------- | ----------------------------------- | | 4.5 – 5.0 | EXCELLENT | Approve; proceed | | 3.5 – 4.4 | GOOD | Approve with minor notes | | 2.5 – 3.4 | ADEQUATE | Request targeted improvements | | 1.5 – 2.4 | POOR | Reject; requires significant rework | | 1.0 – 1.4 | FAILING | Reject; restart task |
Step 5: Emit Structured Verdict
Output the verdict in this format:
## Evaluation Verdict
**Output Evaluated**: [Brief description of what was evaluated]
**Evaluator**: [Agent name / task ID]
**Date**: [ISO 8601 date]
### Dimension Scores
| Dimension | Score | Weight | Weighted Score |
| ------------- | ----- | ------ | -------------- |
| Accuracy | X/5 | 30% | X.X |
| Groundedness | X/5 | 25% | X.X |
| Completeness | X/5 | 20% | X.X |
| Coherence | X/5 | 15% | X.X |
| Helpfulness | X/5 | 10% | X.X |
| **Composite** | | | **X.X / 5.0** |
### Evidence Citations
**Accuracy (X/5)**:
> [Direct quote or file:line reference]
> Rationale: [Why this score]
**Groundedness (X/5)**:
> [Direct quote or file:line reference]
> Rationale: [Why this score]
**Completeness (X/5)**:
> [Direct quote or file:line reference]
> Rationale: [Why this score]
**Coherence (X/5)**:
> [Direct quote or file:line reference]
> Rationale: [Why this score]
**Helpfulness (X/5)**:
> [Direct quote or file:line reference]
> Rationale: [Why this score]
### Verdict: [EXCELLENT | GOOD | ADEQUATE | POOR | FAILING]
**Summary**: [1-2 sentence overall assessment]
**Required Actions** (if verdict is ADEQUATE or worse):
1. [Specific improvement needed]
2. [Specific improvement needed]
Usage Examples
Evaluate a Plan Document
// Load plan document
Read({ file_path: '.claude/context/plans/auth-design-plan-2026-02-21.md' });
// Evaluate against 5-dimension rubric
Skill({ skill: 'agent-evaluation' });
// Provide the plan content as the output to evaluate
Evaluate Agent Response Before Completion
// Agent generates implementation summary
// Before marking task complete, evaluate the summary quality
Skill({ skill: 'agent-evaluation' });
// If composite < 3.5, request improvements before TaskUpdate(completed)
Evaluate Code Review Output
// After code-reviewer runs, evaluate the review quality
Skill({ skill: 'agent-evaluation' });
// Ensures review is grounded in actual code evidence, not assertions
Batch Evaluation (comparing two outputs)
// Evaluate output A
// Save verdict A
// Evaluate output B
// Save verdict B
// Compare composites → choose higher scoring output
Integration with Verification-Before-Completion
The recommended quality gate pattern:
// Step 1: Do the work
// Step 2: Evaluate with agent-evaluation
Skill({ skill: 'agent-evaluation' });
// If verdict is POOR or FAILING → rework before proceeding
// If verdict is ADEQUATE or better → proceed to verification
// Step 3: Final gate
Skill({ skill: 'verification-before-completion' });
// Step 4: Mark complete
TaskUpdate({ taskId: 'X', status: 'completed' });
Iron Laws
- NO COMPLETION CLAIM WITHOUT EVALUATION EVIDENCE — If composite score < 2.5 (POOR or FAILING), rework the output before marking any task complete.
- ALWAYS score all 5 dimensions — never skip dimensions to save time; each dimension catches different failure modes (accuracy ≠ completeness ≠ groundedness).
- ALWAYS cite specific evidence for every dimension score —
"Evidence: [file:line or direct quote]"is mandatory, not optional. Assertions without grounding are invalid. - ALWAYS use the weighted composite —
accuracy×0.30 + groundedness×0.25 + completeness×0.20 + coherence×0.15 + helpfulness×0.10. Never use simple average. - NEVER evaluate before the work is complete — evaluating incomplete outputs produces falsely low scores and wastes context budget.
Anti-Patterns
| Anti-Pattern | Why It Fails | Correct Approach | | ---------------------------------- | ------------------------------------------------------------ | -------------------------------------------------------- | | Skipping dimensions to save time | Each dimension catches different failures | Always score all 5 dimensions | | No evidence citation per dimension | Assertions without grounding are invalid | Quote specific text or file:line for every score | | Using simple average for composite | Accuracy (30%) matters more than helpfulness (10%) | Use the weighted composite formula | | Only checking EXCELLENT vs FAILING | ADEQUATE outputs need targeted improvements, not full rework | Use all 5 verdict tiers with appropriate action per tier | | Evaluating before work is done | Incomplete outputs score falsely low | Evaluate completed outputs only | | Treating evaluation as binary gate | Quality is a spectrum; binary pass/fail loses nuance | Use composite score + per-dimension breakdown together |
Assigned Agents
This skill is used by:
qa— Primary: validates test outputs and QA reports before completioncode-reviewer— Supporting: evaluates code review qualityreflection-agent— Supporting: evaluates agent responses during reflection cycles
Memory Protocol (MANDATORY)
Before starting:
cat .claude/context/memory/learnings.md
Check for:
- Previous evaluation scores for similar outputs
- Known quality patterns in this codebase
- Common failure modes for this task type
After completing:
- Evaluation pattern found ->
.claude/context/memory/learnings.md - Quality issue identified ->
.claude/context/memory/issues.md - Decision about rubric weights ->
.claude/context/memory/decisions.md
ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.