success-metrics-evaluation

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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.

AI & Automation 155 stars 28 forks Updated 2 days ago NOASSERTION

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Quality Score: 80/100

Stars 20%
73
Recency 20%
100
Frontmatter 20%
70
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57
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# 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 1. Map each Story to its defined success metrics 2. Measure artefacts and runtime results against targets 3. Detect underperformance and partial success 4. 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.**

Details

Author
Fr-e-d
Repository
Fr-e-d/GAAI-framework
Created
5 months ago
Last Updated
2 days ago
Language
Shell
License
NOASSERTION

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