Use when cutting a release, writing a CHANGELOG entry, or summarizing what changed since a tag — when the repo's commits follow (or mostly follow) Conventional Commits and a mechanically-accurate section breakdown beats a model's reading of the diff.
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Parses git log against the Conventional Commits spec and prints a categorized changelog: Features, Fixes, Performance, Refactoring, Docs, Tests, Build, CI, Chore, Other, plus a BREAKING CHANGES section pulled from ! markers and BREAKING CHANGE: footers. Deterministic — the categorization is a fact about the commit message, not a judgment call, so a script produces it more reliably than a model re-reading commit subjects.
Core principle: A changelog section is either correct (matches the commit's own declared type) or it silently drops/miscategorizes a change. Parsing is not the place for approximation.
bash ${CLAUDE_SKILL_DIR}/scripts/generate.sh [from-ref] [to-ref]
from-ref defaults to the most recent tag, or the repo's root commit if there are no tags.to-ref defaults to HEAD.CHANGELOG.md.bash ${CLAUDE_SKILL_DIR}/scripts/generate.sh v1.2.0 HEAD > /tmp/new-entries.md
bash ${CLAUDE_SKILL_DIR}/scripts/generate.sh # since the last tag, to HEAD
Reliable: type(scope)!: subject parsing, breaking-change detection (! or a BREAKING CHANGE: footer), section grouping, short-hash linking, merge commits excluded automatically.
Falls back to "Other": any commit whose subject doesn't start with a known Conventional Commits type (feat/fix/perf/refactor/docs/test/build/ci/chore) — including free-text subjects like "Initial plan" or "Add X". A repo that doesn't consistently use Conventional Commits will produce a large "Other" section; that's an honest signal about the commit history, not a script bug. See it happen: run this against this library's own pre-2.0 history and most entries land in Other because they predate the Conventional Commits convention adopted later.
Not attempted: rewriting or improving commit subjects, deduplicating near-identical entries, or inferring a type the author didn't declare.
The script produces raw categorized entries — it does not write prose framing, a version header, or a date. Add those yourself (or ask Claude to), and edit any "Other" entries that deserve reclassification before they ship in a release. Don't paste the raw output into CHANGELOG.md as the final section without a pass over "Other" and "BREAKING CHANGES" — those two sections are exactly where a human or model read adds real value on top of the deterministic parse.
Categorization is deterministic; the narrative framing around it (a version header, a "why" sentence, what to call out) is not. Do the first with the script, the second with judgment.
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Use when evaluating, comparing, installing, or recommending a Claude Code plugin, Agent Skill, MCP server, agent toolkit, or GitHub-hosted AI extension, especially when popularity is weak evidence or the candidate requests shell, network, filesystem, or credential access.
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Use before committing, opening a PR, or reporting a task done — when you want ground-truth confirmation there are no conflict markers, live-looking credentials, debug leftovers, untracked TODOs, or oversized files, without spending a model turn re-reading every file yourself.
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Use before publishing documentation, merging a docs PR, or reporting a Markdown-heavy repo's navigation as complete — when you want ground-truth confirmation that internal links, heading anchors, and directory links resolve, without opening every file yourself.
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Use when the user names a coding or Claude Code task ("add OAuth", "set up hooks", "review this PR", "pick a model"), asks which prompt to use, says to "use the right prompt", or when you enter this repo and need to orient without reading every file.
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Use when the user names a coding, Claude Code, research, documentation, or technology-selection task; asks which prompt to use; says to use the right prompt; or when an agent enters this repository and needs to orient without reading every file.
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Use when a skill isn't triggering as expected, before adding a new skill (to check for a name collision), during periodic cleanup of an accumulated skills directory, or when asked to review skill quality — checks frontmatter validity, description quality against Skill Discovery Optimization conventions, body size, and cross-scope name collisions.
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