Agent Skills: Pipeline Evaluator

Pipeline Evaluator

Purpose

Score a completed agent pipeline across 5 dimensions and produce a structured evaluation report with a composite verdict (EXCELLENT/GOOD/ACCEPTABLE/NEEDS_IMPROVEMENT) and ranked recommendations.

When to Invoke

Skill({ skill: 'pipeline-evaluator' })

Invoke when:

• A multi-task pipeline has completed and quality measurement is needed
• Router requests post-pipeline quality gate
• Reflection agent needs structured metrics for the rubric
• Continuous improvement loop requires baseline data

Workflow

Step 1: Gather Pipeline Data

1. Call TaskList() to retrieve all tasks associated with the pipeline.
2. For each task, call TaskGet({ taskId }) to fetch full metadata including:
   • status (completed/failed/blocked/cancelled)
   • metadata.summary
   • metadata.filesModified
   • metadata.deviations (array)
   • metadata.testResult (PASS/FAIL + counts)
   • metadata.completedAt vs task createdAt (for time efficiency)
3. Read any plan file referenced in task metadata to cross-check task completion against planned scope.

Step 2: Score 5 Dimensions

Calculate each dimension score (0–100 unless noted):

D1: Task Completion Rate (weight: 30%)

completionRate = (completedTasks / totalTasks) * 100

• Count tasks with status: "completed" as completed.
• Tasks with status: "failed" or status: "cancelled" count against.
• Blocked tasks that resolved and completed count as completed.

Score mapping:

• 100% → 100
• 90–99% → 85
• 75–89% → 70
• 60–74% → 50
• <60% → 20

D2: Deviation Count (weight: 20%)

Count total deviations logged across all task metadata deviations[] arrays.

Score mapping (inverse — fewer is better):

• 0 deviations → 100
• 1–2 deviations → 85
• 3–5 deviations → 65
• 6–10 deviations → 40

• 10 deviations → 10

D3: Test Pass Rate (weight: 25%)

Parse metadata.testResult fields. Extract pass counts and fail counts from strings like "PASS 42/42" or "FAIL 38/42".

testPassRate = (totalPassed / totalTests) * 100

If no test results reported: use 50 as neutral score.

D4: Time Efficiency (weight: 10%)

Compare actual pipeline duration vs estimated duration (if available in plan file).

efficiency = min(estimatedDuration / actualDuration, 1.0) * 100

If no estimate available: score 70 (neutral).

D5: Quality Score (weight: 15%)

Derived from code quality signals in task metadata:

• Each task that ran pnpm lint:fix with zero errors: +10 points base
• Each task that ran pnpm format with no changes: +5 points base
• Deduct 5 points per task that reported lint errors
• Deduct 10 points per task with unresolved TODOs in modified files

Cap at 100. Default to 60 if no quality signals present.

Step 3: Calculate Composite Score

composite = (D1 * 0.30) + (D2 * 0.20) + (D3 * 0.25) + (D4 * 0.10) + (D5 * 0.15)

Step 4: Determine Verdict

| Composite Score | Verdict | | --------------- | ----------------- | | > 90 | EXCELLENT | | > 75 | GOOD | | > 60 | ACCEPTABLE | | ≤ 60 | NEEDS_IMPROVEMENT |

Step 5: Generate Recommendations

For each dimension scoring below 80, generate a specific recommendation:

• Task Completion Rate < 80: "Investigate failed/cancelled tasks — review blocker patterns and add prerequisite checks to planner prompts."
• Deviation Count > 5: "High deviation count suggests scope creep. Add DR-3 architectural escalation gates and tighten task scoping in planner."
• Test Pass Rate < 80: "Failing tests indicate implementation gaps. Enforce TDD red-green cycle and add pre-completion test gate."
• Time Efficiency < 70: "Pipeline ran significantly over estimate. Profile slowest tasks and consider parallelization or task decomposition."
• Quality Score < 70: "Lint/format issues present. Add blocking lint gate to pre-completion validation hook."

Sort recommendations by severity (lowest score dimension first).

Step 6: Write Evaluation Report

Write the structured evaluation to .claude/context/reports/backend/pipeline-eval-{pipelineId}-{YYYY-MM-DD}.md:

<!-- Agent: pipeline-evaluator | Task: #{taskId} | Session: {date} -->

# Pipeline Evaluation: {pipelineId}

**Evaluated At**: {ISO timestamp}
**Verdict**: {VERDICT}
**Composite Score**: {score}/100

## Dimension Scores

| Dimension            | Score | Weight | Weighted   |
| -------------------- | ----- | ------ | ---------- |
| Task Completion Rate | {D1}  | 30%    | {D1\*0.3}  |
| Deviation Count      | {D2}  | 20%    | {D2\*0.2}  |
| Test Pass Rate       | {D3}  | 25%    | {D3\*0.25} |
| Time Efficiency      | {D4}  | 10%    | {D4\*0.1}  |
| Quality Score        | {D5}  | 15%    | {D5\*0.15} |

## Recommendations

{ranked recommendations list}

Also write the machine-readable JSON to .claude/context/reports/backend/pipeline-eval-{pipelineId}-{YYYY-MM-DD}.json conforming to pipeline-evaluation.schema.json.

Scoring Reference

| Verdict | Composite | Meaning | | ----------------- | --------- | ------------------------------------ | | EXCELLENT | > 90 | Pipeline executed near-perfectly | | GOOD | > 75 | Minor issues; no systemic problems | | ACCEPTABLE | > 60 | Notable gaps but goals met | | NEEDS_IMPROVEMENT | ≤ 60 | Systemic issues require intervention |

Anti-Patterns

• Never score a pipeline before all tasks have reached a terminal state (completed/failed/cancelled).
• Never use only task count as a proxy for quality — always check test pass rate.
• Never omit the JSON report — machine-readable output is required for trend tracking.
• Never assign EXCELLENT without verifying test pass rate > 90%.

Related Skills

• reflection — Uses pipeline evaluation scores in the rubric
• tdd — Informs the Test Pass Rate dimension
• verification-before-completion — Pre-completion gates that feed quality signals