engineering/skills/changelog-generator/SKILL.md
Produce consistent, auditable release notes from Conventional Commits. Separates commit parsing, semantic-bump logic, and changelog rendering for automated releases with editorial control. Use when cutting a release, generating CHANGELOG.md from git history, or automating release notes in CI.
npx skillsauth add alirezarezvani/claude-skills changelog-generatorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Tier: POWERFUL
Category: Engineering
Domain: Release Management / Documentation
Use this skill to produce consistent, auditable release notes from Conventional Commits. It separates commit parsing, semantic bump logic, and changelog rendering so teams can automate releases without losing editorial control.
major, minor, patch) from commit streamAdded, Changed, Fixed, etc.)python3 scripts/generate_changelog.py \
--from-tag v1.3.0 \
--to-tag v1.4.0 \
--next-version v1.4.0 \
--format markdown
git log v1.3.0..v1.4.0 --pretty=format:'%s' | \
python3 scripts/generate_changelog.py --next-version v1.4.0 --format markdown
python3 scripts/generate_changelog.py --input commits.txt --next-version v1.4.0 --format json
CHANGELOG.mdpython3 scripts/generate_changelog.py \
--from-tag v1.3.0 \
--to-tag HEAD \
--next-version v1.4.0 \
--write CHANGELOG.md
python3 scripts/commit_linter.py --from-ref origin/main --to-ref HEAD --strict --format text
Or file/stdin:
python3 scripts/commit_linter.py --input commits.txt --strict
cat commits.txt | python3 scripts/commit_linter.py --format json
Supported types:
feat, fix, perf, refactor, docs, test, build, ci, choresecurity, deprecated, removeBreaking changes:
type(scope)!: summaryBREAKING CHANGE:SemVer mapping:
majorfeat -> minorpatchpython3 scripts/generate_changelog.py --help
--inputpython3 scripts/commit_linter.py --help
--strict mode on violationsfeat(api): ...) in multi-package repos.[Unreleased] section for manual curation when needed.Use this release flow for predictability:
Security section.commit_linter.py --strict on all PRs.CHANGELOG.md on main branch.tools
Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin, C#, .NET, Java, C, C++, Rust, Ruby, PHP, and Dart/Flutter. Analyzes PRs for complexity and risk, checks code quality for SOLID violations and code smells, generates review reports. Use when reviewing pull requests, analyzing code quality, identifying issues, generating review checklists.
tools
Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing/surveys, or product/user research. Triggers on "design this clinical study", "what sample size", "R&D budget", "burn rate", "capitalize or expense", "TAM SAM SOM", "market sizing", "survey design", "segment the market", "plan user interviews", "usability test", "synthesize research insights". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), and marketing-skill (campaign analytics).
development
Use when managing the money for an internal R&D program or portfolio — building a multi-period program budget with the F&A (indirect) split, tracking burn rate and runway against value-inflection milestones, or routing R&D cost items to a capitalize-vs-expense determination. Every budget output surfaces its assumptions block; capitalize-vs-expense is decision-support only and routes to a named finance owner — it never books an entry or decides accounting treatment. Distinct from finance/financial-analysis (corporate DCF, close, valuation) and research/grants (funding discovery — this manages money already won).
development
Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.