doc-refresh
Orchestrates documentation updates after version bumps, feature additions, or periodic maintenance. Analyses git history since the last release, identifies which docs are affected, and delegates to existing skills (changelog, feature-benefits, docs-verify, llms-txt, user-guides) for selective refresh. Delegates AI context updates to ContextDocs if installed. Use when releasing a new version or refreshing stale docs.
Best use case
doc-refresh is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Orchestrates documentation updates after version bumps, feature additions, or periodic maintenance. Analyses git history since the last release, identifies which docs are affected, and delegates to existing skills (changelog, feature-benefits, docs-verify, llms-txt, user-guides) for selective refresh. Delegates AI context updates to ContextDocs if installed. Use when releasing a new version or refreshing stale docs.
Teams using doc-refresh should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/doc-refresh/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How doc-refresh Compares
| Feature / Agent | doc-refresh | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Orchestrates documentation updates after version bumps, feature additions, or periodic maintenance. Analyses git history since the last release, identifies which docs are affected, and delegates to existing skills (changelog, feature-benefits, docs-verify, llms-txt, user-guides) for selective refresh. Delegates AI context updates to ContextDocs if installed. Use when releasing a new version or refreshing stale docs.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Doc Refresh ## Philosophy Generation is solved — PitchDocs handles that. Maintenance is the unsolved problem. After the initial docs suite is created, every release needs a coordinated update: CHANGELOG entries enhanced with benefit language, README features refreshed, user guides amended, AI context files synced, and llms.txt kept current. `/doc-refresh` closes the maintenance loop. It works alongside release-please: release-please handles version strings and CHANGELOG scaffolding, `/doc-refresh` handles prose, features, context, and metrics. ## Change Detection Workflow ### Step 1: Identify the Boundary ```bash # Latest tag (the "since" point for change detection) git describe --tags --abbrev=0 2>/dev/null # If no tags exist, fall back to initial commit git rev-list --max-parents=0 HEAD # All commits since boundary git log $(git describe --tags --abbrev=0 2>/dev/null || git rev-list --max-parents=0 HEAD)..HEAD --oneline --no-merges # If a version argument was provided (e.g., v1.5.0..v1.7.0) git log v1.5.0..v1.7.0 --oneline --no-merges ``` If no tags exist at all, recommend running `/readme` and `/docs-audit fix` instead — a full generation is more appropriate than a refresh for a brand-new repo. ### Step 2: Parse Conventional Commits Classify each commit into categories that map to documentation impacts: | Commit Type | Doc Impact | |-------------|-----------| | `feat:` | CHANGELOG, README features, possibly user guides, release notes | | `fix:` | CHANGELOG, possibly troubleshooting guides | | `docs:` | Verify existing docs are consistent with changes | | `refactor:` | AI context files (if architecture changed) | | `perf:` | CHANGELOG, README metrics if benchmarks cited | | `chore:` | Usually none, unless dependencies changed significantly | | `BREAKING CHANGE:` | CHANGELOG with migration note, README, migration guide, release notes | If the repo does not use conventional commits, fall back to `git diff --stat` analysis — classify changes by which files they touch (source, tests, config, docs) rather than commit message prefix. ### Step 3: Detect File-Level Changes ```bash # Which areas of the project changed? git diff --name-only $(git describe --tags --abbrev=0 2>/dev/null || git rev-list --max-parents=0 HEAD)..HEAD | head -50 # Specifically check for structural changes (new commands, skills, agents, config) git diff --name-only $(git describe --tags --abbrev=0 2>/dev/null || git rev-list --max-parents=0 HEAD)..HEAD | grep -E '(commands/|skills/|agents/|rules/|\.config|package\.json|pyproject\.toml)' ``` ### Step 4: Build the Refresh Plan Map detected changes to specific doc files. Output a structured plan before executing: ``` 📋 Documentation Refresh Plan: [project-name] Boundary: v1.6.0..HEAD (15 commits: 8 feat, 4 fix, 2 docs, 1 chore) Docs to update: → CHANGELOG.md — 8 feat + 4 fix entries to enhance with benefit language → README.md — 2 new features detected, metrics need updating → docs/guides/getting-started.md — new command added, guide needs amendment → AGENTS.md — commands table out of date → llms.txt — 2 new files to add ⊘ .cursorrules — no drift detected ⊘ Package registry — no metadata changes ``` In `plan` mode, stop here and report. Otherwise, proceed to execution. ## Refresh Actions Table | What Changed | CHANGELOG | README Features | README Metrics | User Guides | AI Context | llms.txt | Release Notes | |-------------|-----------|-----------------|---------------|-------------|------------|----------|---------------| | New feature (`feat:`) | Append | Update/add | Update counts | Add/update relevant guide | If architecture changed | If new files added | Include | | Bug fix (`fix:`) | Append | No | No | Update troubleshooting if relevant | No | No | Include | | New command or skill | Append | Update tables | Update "By the Numbers" | Add to guides hub | Update | Update | Include | | Dependency change | Conditional | No | No | No | If major dependency | No | Conditional | | Performance improvement | Append | Update if metrics cited | Update benchmarks | No | No | No | Include | | Breaking change | Append with migration | Update | No | Add migration guide | Update | No | Include prominently | | File renamed/moved | No | Update if referenced | No | Update paths | Update paths | Update paths | No | ## Orchestration Workflow Execute in this order. Each step loads the relevant skill on demand. ### Step 1: Analyse (always runs first) Run the change detection workflow above. Produce the refresh plan. In `plan` mode, report and stop. ### Step 2: CHANGELOG Load the `changelog` skill. If release-please has already created CHANGELOG entries for this version, **enhance** them with benefit language rather than duplicating. If no release-please entries exist, generate from scratch using conventional commits. Detection: check if a version header (e.g., `## [1.7.0]`) or `## [Unreleased]` section already exists in CHANGELOG.md with entries for the commits in scope. ### Step 3: README Load the `feature-benefits` skill. Run a features audit to compare current README features against the codebase. Update: - Features section (add new, mark deprecated) - "By the Numbers" metrics table (command counts, skill counts, etc.) - Badge version references (note: release-please handles the version badge via `x-release-please-version` — do not duplicate) ### Step 4: User Guides Load the `user-guides` skill. Identify which guides are affected by checking if changed files relate to documented workflows. Update affected sections. Add new guides if a major new feature warrants one. Update the docs hub page if guides were added. ### Step 5: AI Context Files (ContextDocs) If [ContextDocs](https://github.com/littlebearapps/contextdocs) is installed (`[ -d ".claude/skills/ai-context" ]`), delegate to it: ```bash # Check if ContextDocs is available if [ -d ".claude/skills/ai-context" ]; then echo "ContextDocs detected — run /contextdocs:ai-context audit to check for drift" fi ``` If ContextDocs is not installed, print an advisory: ``` ℹ AI context file refresh skipped — install ContextDocs for AI context management: /plugin install contextdocs@lba-plugins ``` ### Step 6: llms.txt Load the `llms-txt` skill. Regenerate if files were added, removed, or renamed since the boundary. If no structural changes, skip. ### Step 7: Package Registry Load the `package-registry` skill. Verify that package.json/pyproject.toml metadata (description, keywords, repository, homepage) is still current. Flag any drift. ### Step 7.5: Plugin Manifest (if applicable) If the project has a `.claude-plugin/plugin.json`, verify the `description` and `keywords` fields still match the current README one-liner and features. CLAUDE.md notes "update on every release" — flag stale descriptions that no longer reflect the project's scope. ### Step 8: Verify (always runs last) Load the `docs-verify` skill. Run full verification: broken links, stale content, llms.txt sync, heading hierarchy, badge URLs, feature coverage, quality score. Report the score and any issues found. ### Step 9: Release Notes (optional) If `release-notes` argument was provided or running in `full` mode, generate a GitHub release body from the CHANGELOG entry for this version. Format with benefit-driven language and include migration notes for breaking changes. ## Release Automation Integration The table below shows the split of responsibilities between your release automation tool and `/doc-refresh`. release-please (GitHub Actions) is the default; for GitLab use `semantic-release` with GitLab CI or `release-it`; for Bitbucket use `semantic-release` with Bitbucket Pipelines. Load the `platform-profiles` skill for CI/CD equivalents. | Responsibility | Release automation tool | `/doc-refresh` | |---------------|------------------------|----------------| | Version strings in manifests | Yes | No | | Version badge in README | Yes (e.g. `x-release-please-version`) | No | | CHANGELOG scaffolding | Yes (from commit messages) | Enhance with benefit language | | README prose, features, metrics | No | Yes | | User guides | No | Yes | | AI context files | No | Yes | | llms.txt | No | Yes | | Release notes body | Basic (from commits) | Enhanced with benefit language | **Timing:** Run `/doc-refresh` before merging the release PR: 1. Your release tool creates a PR with version bumps and CHANGELOG skeleton 2. Run `/doc-refresh` to enhance CHANGELOG, update README, guides, context files 3. Commit the refreshed docs to the release branch 4. Merge the PR — the release tool creates the platform release ## Anti-Patterns - **Do not run `/doc-refresh` and `/readme` in the same session** — `/doc-refresh` updates README surgically (affected sections only), while `/readme` regenerates from scratch. Choose one. - **Do not duplicate CHANGELOG entries** — if release-please already generated entries, enhance them with benefit language rather than creating parallel entries. - **Do not update user guides for internal refactors** — only update guides when user-facing behaviour changes. - **Do not regenerate all AI context files** — audit first, update only the files with actual drift. - **Do not manually update the version badge** — release-please owns the `x-release-please-version` marker.
Related Skills
pitchdocs
Generate marketing-quality repository documentation from codebase analysis. Scans 10 signal categories, extracts features with file-level evidence, and produces README, CHANGELOG, ROADMAP, and 15+ more docs. Zero runtime dependencies. For AI context file management, see ContextDocs.
visual-standards
Visual formatting standards for repository documentation — emoji heading prefixes, horizontal rules, TOC anchors, callouts, screenshots (device dimensions, HTML patterns, captions, shadows), and image optimisation. Load when generating READMEs with visual elements or working with screenshots.
user-guides
Generates task-oriented user guides and how-to documentation for a repository. Creates docs/guides/ with step-by-step instructions for common workflows, integrations, and advanced usage. Links guides into README.md and CONTRIBUTING.md. Use when a project needs user-facing how-to documentation beyond the README quickstart.
roadmap
Generates ROADMAP.md from project milestones, issues, and boards (GitHub, GitLab, or Bitbucket). Structures content with mission statement, current milestone progress, upcoming milestones, and community involvement section. Use when creating or updating a project roadmap.
public-readme
Generates READMEs with the Daytona/Banesullivan marketing framework — hero section, benefit-driven features, quickstart, comparison tables, and compelling CTAs. Produces docs that sell as well as they inform. Use when creating or overhauling a project README.
platform-profiles
Platform-specific equivalents for GitLab and Bitbucket when generating repository documentation. Lookup tables for file paths, badges, Markdown rendering, CI/CD, and CLI tools. Load this skill when working on non-GitHub repos or generating cross-platform docs.
pitchdocs-suite
One-command generation and audit of the full public repository documentation set — README, CHANGELOG, ROADMAP, CONTRIBUTING, CODE_OF_CONDUCT, SECURITY, issue templates, PR template, and discussion templates. Use when setting up a new repo or auditing an existing one.
package-registry
Documentation guidance for projects published to npm and PyPI package registries. Covers metadata fields that affect registry pages, README cross-renderer compatibility, trusted publishing, provenance badges, and audit checks. Use when a project has package.json or pyproject.toml and is published publicly.
llms-txt
Generates llms.txt and llms-full.txt files following the llmstxt.org specification. Provides LLM-friendly content curation for AI coding assistants (Cursor, Windsurf, Claude Code) and AI search engines. Use when generating or updating llms.txt for a repository.
launch-artifacts
Transforms README and CHANGELOG into platform-specific launch content — Dev.to articles, Hacker News posts, Reddit posts, Twitter/X threads, and awesome list submission PRs. Keeps promotion tethered to code artifacts, not generic marketing. Use when launching or announcing a project release.
geo-optimisation
Generative Engine Optimisation (GEO) patterns for documentation that surfaces correctly in AI-generated answers — citation capsules, crisp definitions, atomic sections, comparison tables, statistics, and semantic scaffolding. Load when optimising docs for AI citation (ChatGPT, Perplexity, Google AI Overviews, Claude).
feature-benefits
Systematic codebase scanning for features and evidence-based feature-to-benefit translation. Extracts what a project does from its code and translates it into what users gain — generates features and benefits sections, "Why [Project]?" content, and feature audit reports. Use when writing a features table for a README, extracting features from code, auditing feature coverage, or answering "why should someone use this project?".