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Get Started Free →Automates release management with changelog generation, semantic versioning, and release readiness checks. Use when preparing releases, generating changelogs, bumping versions, or validating release candidates.
.claude/skills/borghei-release-manager/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -22% | 0% |
| case-12 | ✓→✓ | = Same ✓ | 50% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 277% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 158% | 0% |
| case-06 | ✓→✓ | = Same ✓ | 158% | 0% |
The agent automates release management by parsing conventional commits into structured changelogs, determining semantic version bumps, and assessing release readiness with checklists, rollback runbooks, and stakeholder communication plans.
Before preparing the release, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
| Tool | Purpose | Command | |------|---------|---------| | changelog_generator.py | Generate Keep-a-Changelog from conventional commits | git log --oneline v1.0.0..HEAD \| python changelog_generator.py --version 1.1.0 --format both | | version_bumper.py | Determine SemVer bump + emit bump commands | git log --oneline v1.0.0..HEAD \| python version_bumper.py --current-version 1.0.0 --analysis | | release_planner.py | Assess release readiness, checklist, rollback, comms | python release_planner.py --input release-plan.json --include-checklist --include-rollback |
Load the reference that matches the task — keep this file lean and pull detail on demand:
This skill covers:
This skill does NOT cover:
engineering/ci-cd-pipeline-generator)engineering/monitoring-alerting-setup)engineering/code-review-automation)engineering/infrastructure-as-code)| Skill | Integration | Data Flow | |-------|-------------|-----------| | engineering/ci-cd-pipeline-generator | Embed changelog generation and version bumping as pipeline stages | Git log output flows into changelog_generator.py; version bump output feeds pipeline tagging steps | | engineering/code-review-automation | Validate that PR commits follow conventional commit format before merge | Commit messages validated upstream ensure clean input for changelog generation | | engineering/monitoring-alerting-setup | Define rollback triggers based on monitoring thresholds from the rollback runbook | Rollback trigger thresholds (error rate >2x, latency >50%) feed into alert rule configuration | | engineering/api-design-reviewer | Breaking API changes flagged by the reviewer map to MAJOR version bumps | API review findings populate breaking_changes arrays in the release plan JSON | | engineering/infrastructure-as-code | Deployment steps in the rollback runbook reference infrastructure rollback commands | Rollback runbook command fields contain infrastructure-specific commands (kubectl, DNS, load balancer) | | project-management/release-planning | Release plan JSON structure aligns with PM release tracking artifacts | PM feature lists and approval statuses feed directly into release_planner.py input format |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-12 | pass→pass | 16,534 | 14,873 | -10% | 1 | 1 | 0% | 2,543 | 3,814 | +50% | 0 | 0 | — |
case-01 | fail→pass | 23,008 | 10,094 | -56% | 1 | 1 | 0% | 3,729 | 2,909 | -22% | 0 | 0 | — |
case-02 | fail→fail | 13,881 | 16,774 | +21% | 1 | 1 | 0% | 2,464 | 4,120 | +67% | 0 | 0 | — |
case-03 | pass→pass | 4,412 | 8,836 | +100% | 1 | 1 | 0% | 755 | 2,850 | +277% | 0 | 0 | — |
case-04 | pass→pass | 5,085 | 6,664 | +31% | 1 | 1 | 0% | 929 | 2,396 | +158% | 0 | 0 | — |
case-05 | fail→fail | 5,779 | 7,350 | +27% | 1 | 1 | 0% | 958 | 2,568 | +168% | 0 | 0 | — |
case-06 | pass→pass | 5,915 | 6,835 | +16% | 1 | 1 | 0% | 960 | 2,477 | +158% | 0 | 0 | — |
case-07 | fail→fail | 6,511 | 6,444 | -1% | 1 | 1 | 0% | 1,117 | 2,437 | +118% | 0 | 0 | — |
case-08 | pass→pass | 3,661 | 4,006 | +9% | 1 | 1 | 0% | 612 | 2,056 | +236% | 0 | 0 | — |
case-09 | pass→pass | 3,913 | 5,369 | +37% | 1 | 1 | 0% | 600 | 2,135 | +256% | 0 | 0 | — |
case-10 | pass→pass | 2,870 | 2,557 | -11% | 1 | 1 | 0% | 441 | 1,681 | +281% | 0 | 0 | — |
case-11 | pass→pass | 17,706 | 22,446 | +27% | 1 | 1 | 0% | 2,698 | 5,133 | +90% | 0 | 0 | — |
case-13 | pass→pass | 10,341 | 12,274 | +19% | 1 | 1 | 0% | 1,611 | 3,232 | +101% | 0 | 0 | — |
case-14 | pass→pass | 11,726 | 12,860 | +10% | 1 | 1 | 0% | 1,814 | 3,345 | +84% | 0 | 0 | — |
case-15 | pass→pass | 10,956 | 9,300 | -15% | 1 | 1 | 0% | 1,608 | 2,773 | +72% | 0 | 0 | — |
case-16 | pass→pass | 3,767 | 6,545 | +74% | 1 | 1 | 0% | 627 | 2,450 | +291% | 0 | 0 | — |
case-17 | pass→pass | 3,152 | 5,209 | +65% | 1 | 1 | 0% | 530 | 2,229 | +321% | 0 | 0 | — |
case-18 | pass→pass | 4,132 | 3,470 | -16% | 1 | 1 | 0% | 632 | 1,827 | +189% | 0 | 0 | — |
case-19 | pass→pass | 10,058 | 3,579 | -64% | 1 | 1 | 0% | 1,466 | 1,813 | +24% | 0 | 0 | — |
case-20 | fail→fail | 15,767 | 20,037 | +27% | 1 | 1 | 0% | 3,076 | 5,031 | +64% | 0 | 0 | — |
case-21 | fail→fail | 14,136 | 13,384 | -5% | 1 | 1 | 0% | 2,758 | 3,903 | +42% | 0 | 0 | — |
case-22 | fail→fail | 19,483 | 24,533 | +26% | 1 | 1 | 0% | 3,709 | 6,507 | +75% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +5 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.