| name | success-metrics-evaluation |
| description | Evaluate delivery outcomes against defined success metrics and acceptance goals. Activate after Delivery to verify that delivered work creates real business and technical impact, not just output. |
| license | MIT |
| compatibility | Works with any filesystem-based AI coding agent |
| metadata | {"author":"gaai-framework","version":"1.0","category":"cross","track":"cross-cutting","id":"SKILL-SUCCESS-METRICS-EVALUATION-001","updated_at":"2026-02-26T00:00:00.000Z","status":"future"} |
| inputs | ["contexts/artefacts/stories/**","acceptance_criteria","delivered_artefacts","defined_success_metrics","runtime_or_usage_data (optional)"] |
| outputs | ["metric_results","story_level_success_report","gap_analysis","improvement_recommendations"] |
Success Metrics Evaluation
Purpose / When to Activate
Activate after Delivery to verify outcomes, not just outputs. Prevents "output without outcome."
Use when:
- Success metrics were defined in the PRD or Story
- Delivery is complete and runtime data is available
- Objective quality gates are required
Process
- Map each Story to its defined success metrics
- Measure artefacts and runtime results against targets
- Detect underperformance and partial success
- Generate actionable improvement insights
Outputs
- Story-by-story KPI report
- Metric vs target comparisons
- Identified gaps with root signals
- Improvement suggestions linked to backlog items
Quality Checks
- Each metric is measured against a defined target
- Gaps are identified with root cause signals
- Recommendations are linked to specific backlog items
- No invented metrics — only those defined in artefacts
Non-Goals
This skill must NOT:
- Redefine success metrics post-delivery
- Make product decisions about gaps
- Substitute for
qa-review
Ensures delivery creates real impact. Makes scaling predictable.
