kpi-pr-throughput
KPI for measuring and improving PR throughput. Defines metrics, measurement methods, and improvement strategies. Use to optimize how many quality PRs get merged.
When & Why to Use This Skill
This Claude skill provides a comprehensive framework for measuring and optimizing Pull Request (PR) throughput. It defines key performance indicators (KPIs) such as merge rates, review cycles, and quality gates to help development teams and AI agents improve their coding efficiency and output quality through actionable metrics and improvement strategies.
Use Cases
- Measuring AI Agent Productivity: Track the number of quality PRs successfully merged during an agent session to evaluate and benchmark autonomous coding performance.
- Identifying Development Bottlenecks: Use metrics like 'Time to Merge' and 'Review Cycles' to pinpoint delays in the development pipeline and improve team velocity.
- Quality Assurance Integration: Implement strict quality gates to ensure that high throughput is maintained without compromising code stability or increasing revert rates.
- Workflow Optimization: Apply best practices such as aggressive scoping and front-loaded verification to reduce review friction and increase the first-pass approval rate.
| name | kpi-pr-throughput |
|---|---|
| description | KPI for measuring and improving PR throughput. Defines metrics, measurement methods, and improvement strategies. Use to optimize how many quality PRs get merged. |
KPI: PR Throughput
Definition: The rate at which quality PRs are merged to main.
Why This Matters
PR throughput measures productive output. High throughput with maintained quality indicates efficient development. Low throughput suggests blockers in the development pipeline.
Metrics
Primary Metric
PRs Merged Per Session - Count of PRs successfully merged during an agent session.
Supporting Metrics
| Metric | What It Measures |
|---|---|
| Time to Merge | Latency from PR creation to merge |
| Review Cycles | Number of review rounds needed |
| First-Pass Approval Rate | PRs approved without changes requested |
| Revert Rate | PRs reverted due to issues |
Measurement
During Session
Track:
# Count merged PRs by this agent
gh pr list --state merged --author @me --json number | jq length
Quality Gate
Throughput only counts if:
- PR was not reverted
- Main branch stayed green
- No follow-up fix PRs needed
Target Benchmarks
| Performance | PRs/Session |
|---|---|
| Struggling | 0 |
| Normal | 1-2 |
| High | 3-5 |
| Exceptional | 5+ |
Improvement Strategies
1. Scope Down Aggressively
Large PRs take longer to review and have higher failure rates. Target:
- <300 lines changed per PR
- Single logical change per PR
- Clear, focused description
2. Front-load Verification
Run ./verify.sh --ui=false before creating PR. Catching issues early avoids review cycles.
3. Write Good PR Descriptions
Clear descriptions reduce review time:
- What changed and why
- How to test
- Any risks or trade-offs
4. Stack PRs When Possible
For dependent changes, create stacked PRs that can be reviewed in parallel.
5. Address Feedback Promptly
When review feedback arrives, address it immediately while context is fresh.
Blockers to Watch
| Blocker | Detection | Mitigation |
|---|---|---|
| Review delay | PR open >1 hour | Request review explicitly |
| Verification failures | verify.sh fails | Fix before PR, not after |
| Scope creep | PR grows beyond original intent | Split into multiple PRs |
| Merge conflicts | Can't merge cleanly | Rebase frequently |
Anti-Patterns
- Quantity over quality - Merging broken code destroys throughput
- Mega-PRs - One 1000-line PR is worse than five 200-line PRs
- Skipping review - Unreviewed code accumulates debt
- Ignoring CI - Broken verification means broken code