anthropic-docs-updater
Anthropic Docs Updater
Overview
anthropic-docs-updater automatically keeps the anthropic-expert skill current by detecting, fetching, and integrating Anthropic documentation updates.
Purpose: Automated documentation maintenance for anthropic-expert
Update Workflow (5 steps):
- Check for Updates - Detect new releases and documentation changes
- Fetch Documentation - Download updated content from official sources
- Process Content - Convert to skill reference format
- Update Skill - Integrate new content into anthropic-expert
- Validate Updates - Ensure quality maintained, no regressions
Automation: 80% automated (manual review for breaking changes)
Update Sources:
- GitHub Releases (SDK version updates)
- docs.claude.com/en/release-notes (API updates)
- code.claude.com/docs/en/changelog (Claude Code updates)
- anthropic.com/news (model announcements)
When to Use
- Weekly/monthly update checks (stay current)
- After Anthropic announces new features
- Before starting new Anthropic project (ensure latest docs)
- When anthropic-expert seems outdated
- Automated scheduled updates (cron job)
Prerequisites
- anthropic-expert skill installed
- Python 3.7+ with requests library
- GitHub API access (for release checking)
- Internet access (for fetching docs)
Update Workflow
Step 1: Check for Updates
Purpose: Detect new releases, documentation changes, feature announcements
Process:
-
Check GitHub Releases
python scripts/check-updates.py --github- Queries GitHub API for latest releases
- Checks: anthropic-sdk-python, claude-agent-sdk-python
- Compares to current versions in changelog.md
- Reports new releases found
-
Check Release Notes
python scripts/check-updates.py --docs- Fetches docs.claude.com/en/release-notes
- Compares to last check date
- Identifies new entries
-
Check Claude Code Changelog
python scripts/check-updates.py --claude-code- Fetches code.claude.com/docs/en/changelog
- Detects new versions or features
-
Generate Update Report
python scripts/check-updates.py --all- Runs all checks
- Aggregates findings
- Outputs: update-report.txt with detected changes
Validation:
- GitHub releases checked
- Release notes checked
- Claude Code changelog checked
- Update report generated
- New updates detected (or confirmed current)
Outputs:
- update-report.txt (what's new)
- List of detected changes
- Recommended update actions
Time Estimate: 10-15 minutes (automated)
Example Output:
Anthropic Documentation Update Check
=====================================
Date: 2025-11-15
GitHub Releases:
✅ anthropic-sdk-python: v0.45.0 (current: v0.42.0) - UPDATE AVAILABLE
✅ claude-agent-sdk-python: v1.12.0 (current: v1.10.0) - UPDATE AVAILABLE
Release Notes (docs.claude.com):
✅ New feature: Batch API cost reduction increased to 60%
✅ New model: Claude Sonnet 4.6 announced
Claude Code Changelog:
- No new updates since last check
Recommendation: UPDATE AVAILABLE
- 2 SDK updates
- 2 API feature updates
- Proceed to Step 2 (Fetch Documentation)
Step 2: Fetch Documentation
Purpose: Download updated content from official sources
Process:
-
Fetch SDK Documentation
python scripts/fetch-docs.py --github-readmes- Downloads README.md from SDK repositories
- Gets changelog/release notes from GitHub
- Saves to temp/sdk-docs/
-
Fetch API Documentation
python scripts/fetch-docs.py --api-docs- Fetches updated pages from docs.claude.com
- Downloads release notes
- Saves to temp/api-docs/
-
Fetch Claude Code Documentation
python scripts/fetch-docs.py --claude-code-docs- Fetches updated pages from code.claude.com
- Downloads changelog
- Saves to temp/claude-code-docs/
-
Verify Downloads
- Check all files downloaded successfully
- Validate file integrity
- Confirm no download errors
Validation:
- SDK docs fetched successfully
- API docs fetched successfully
- Claude Code docs fetched (if updates)
- All files saved to temp directory
- No download errors
Outputs:
- temp/sdk-docs/ (SDK documentation)
- temp/api-docs/ (API documentation)
- temp/claude-code-docs/ (Claude Code documentation)
- fetch-log.txt (download log)
Time Estimate: 15-30 minutes (automated, depends on amount of content)
Step 3: Process Documentation
Purpose: Convert fetched content to skill reference format
Process:
-
Parse Fetched Documentation
python scripts/process-docs.py --input temp/ --output processed/- Parses markdown from temp/
- Extracts relevant sections
- Identifies code examples
- Structures by product
-
Convert to Reference Format
- Organize by product/capability
- Format consistently with existing references
- Extract code examples properly
- Add navigation headers
-
Merge with Existing Content
- Compare new vs existing documentation
- Identify additions, changes, removals
- Preserve custom examples/notes
- Generate diff report
-
Validate Processed Content
- Check markdown syntax
- Verify code examples
- Ensure consistent formatting
Validation:
- All fetched docs processed
- Content converted to reference format
- Organized by product/capability
- Code examples extracted correctly
- Diff report generated (what changed)
- Processed content validated
Outputs:
- processed/ (processed documentation)
- diff-report.txt (what changed)
- Formatted content ready for integration
Time Estimate: 20-40 minutes (automated with manual review of diff)
Step 4: Update anthropic-expert Skill
Purpose: Integrate new content into anthropic-expert skill safely
Process:
-
Backup Current Skill
python scripts/update-skill.py --backup- Creates backup of anthropic-expert
- Saves to anthropic-expert.backup-YYYYMMDD/
- Preserves all files
-
Integrate New Content
python scripts/update-skill.py --integrate processed/- Updates relevant reference files
- Adds new features to appropriate sections
- Preserves custom content
- Updates changelog.md with changes
-
Update Version
- Increments version number
- Updates changelog with:
- Version number
- Date
- Changes summary
- New features
- Updated documentation
-
Review Changes
- Display diff of what changed
- Prompt for confirmation (if manual mode)
- Allow rollback if issues
Validation:
- Current skill backed up
- New content integrated successfully
- Changelog updated with version and changes
- No merge conflicts
- All reference files valid markdown
- Ready for validation step
Outputs:
- Updated anthropic-expert skill
- Backup in anthropic-expert.backup-*/
- Updated changelog.md
- Integration log
Time Estimate: 15-30 minutes (automated, quick review)
Step 5: Validate Updates
Purpose: Ensure updates maintain quality and don't introduce regressions
Process:
-
Run Structure Validation
python ../../review-multi/scripts/validate-structure.py ../anthropic-expert- Validates YAML frontmatter
- Checks file structure
- Verifies naming conventions
- Ensures progressive disclosure
- Must pass (5/5 or 4/5)
-
Test Search Functionality
python ../anthropic-expert/scripts/search-docs.py "test query"- Verify search still works
- Check can find content in updated files
- Ensure no search errors
-
Manual Spot Check
- Review 2-3 updated sections
- Verify accuracy of new content
- Check code examples valid
- Ensure formatting consistent
-
Validation Decision
- PASS: All validations successful → Finalize update
- FAIL: Issues found → Rollback and investigate
-
Rollback if Failed (if validation fails)
python scripts/update-skill.py --rollback- Restores from backup
- Reverts to previous version
- Logs failure for investigation
Validation:
- Structure validation passes (≥4/5)
- Search functionality works
- Spot check confirms accuracy
- No regressions detected
- Quality maintained
- Update finalized OR rolled back if issues
Outputs:
- Validation report
- Final updated skill (if passed)
- OR restored backup (if failed)
- Update success/failure status
Time Estimate: 20-30 minutes
Post-Workflow: Update Complete
If Successful:
- ✅ anthropic-expert updated with latest documentation
- ✅ Changelog.md updated with changes
- ✅ Quality validated (structure 5/5)
- ✅ Ready to use with latest Anthropic features
If Failed:
- ❌ Updates rolled back
- 📋 Investigation needed (check logs)
- 🔄 Manual review of changes
- 🛠️ Fix issues and retry
Next Check: Weekly or when Anthropic announces updates
Best Practices
1. Schedule Regular Updates
Practice: Weekly automated check for updates
Implementation: Cron job or scheduled task
# Weekly check (Mondays at 9am)
0 9 * * 1 cd /path/to/skills && python anthropic-docs-updater/scripts/check-updates.py --all
2. Review Breaking Changes Manually
Practice: For major version updates, review changes before applying
Why: Breaking changes may require manual updates to examples
3. Backup Before Updating
Practice: Always backup (Step 4 does this automatically)
Why: Can rollback if updates cause issues
4. Validate After Updates
Practice: Always run Step 5 (validation)
Why: Ensures updates don't break skill quality
5. Track Update History
Practice: Maintain detailed changelog
Why: Understand what changed when, aids troubleshooting
Quick Reference
The 5-Step Update Workflow
| Step | Focus | Time | Automation | Output |
|---|---|---|---|---|
| 1. Check Updates | Detect changes | 10-15m | 100% | update-report.txt |
| 2. Fetch Docs | Download content | 15-30m | 100% | temp/docs/ |
| 3. Process Content | Convert format | 20-40m | 90% | processed/docs/ |
| 4. Update Skill | Integrate content | 15-30m | 95% | Updated skill |
| 5. Validate | Ensure quality | 20-30m | 70% | Validation report |
Total Time: 1.5-2.5 hours (mostly automated)
Update Sources
| Source | What It Tracks | Check Method |
|---|---|---|
| GitHub Releases | SDK versions | GitHub API |
| Release Notes | API features | Web scraping |
| Claude Code Changelog | CLI updates | Web scraping |
| Anthropic News | Model announcements | Manual/RSS |
Common Commands
# Check for updates
python scripts/check-updates.py --all
# Fetch new documentation
python scripts/fetch-docs.py --all
# Process fetched docs
python scripts/process-docs.py --input temp/ --output processed/
# Apply updates
python scripts/update-skill.py --integrate processed/
# Validate
python scripts/update-skill.py --validate
# Rollback if needed
python scripts/update-skill.py --rollback
Automation Schedule
Recommended: Weekly checks, monthly comprehensive updates
Cron Example (check weekly, update monthly):
# Check for updates every Monday
0 9 * * 1 python check-updates.py --all > /tmp/anthropic-updates.log
# Full update first Monday of month
0 10 1-7 * 1 bash run-full-update.sh
anthropic-docs-updater ensures anthropic-expert stays current with the latest Anthropic products, features, and documentation through automated update detection, fetching, processing, and integration.