knowledge-updater
Automated skill for keeping AI knowledge bases current with latest model versions, framework updates, and best practices
When & Why to Use This Skill
The Knowledge Updater is an automated Claude skill designed to keep technical knowledge bases, documentation, and version tracking files synchronized with the latest AI industry developments. It systematically monitors updates for LLM models (like GPT and Claude), agent frameworks, and frontend technologies, ensuring that architectural recommendations and development tools remain accurate and credible in a rapidly evolving ecosystem.
Use Cases
- Automated Version Tracking: Periodically scanning official releases of AI frameworks like Vercel AI SDK, LangGraph, or Next.js to update internal dependency documentation and version-tracking files.
- Model Capability Auditing: Monitoring major AI providers for new model releases to automatically update pricing tables, performance benchmarks, and selection logic within system prompts.
- Documentation Maintenance: Automatically updating metadata, last-updated timestamps, and changelogs across a suite of AI skills to reflect the current state of the technology stack without manual intervention.
- Architectural Integrity: Ensuring that AI-driven design solutions always reference the most recent stable versions of libraries to prevent deprecated code recommendations.
| name | Knowledge Updater |
|---|---|
| description | Automated skill for keeping AI knowledge bases current with latest model versions, framework updates, and best practices |
| version | 1.0.0 |
| last_updated | 2026-01-06 |
| external_version | "Self-updating" |
Knowledge Updater Skill
Purpose
Automate the process of keeping AI architecture knowledge current by systematically checking and updating version references across the toolkit.
Why This Matters
AI evolves rapidly:
- GPT-5.2 released December 2025 (just weeks ago)
- Claude Opus 4.5 released November 2025
- Next.js moved from 15 → 16 in months
- Framework versions change weekly
Stale knowledge = bad recommendations = lost credibility.
Update Workflow
Step 1: Discover Current Versions
LLM Models (check monthly)
Search: "[Provider] latest model [current year]"
- OpenAI: GPT-5.x series
- Anthropic: Claude Opus/Sonnet versions
- Google: Gemini versions
- Meta: Llama versions
- Mistral: Latest releases
Agent Frameworks (check weekly)
Sources:
- Vercel AI SDK: https://github.com/vercel/ai/releases
- OpenAI Agents: https://github.com/openai/openai-agents-python/releases
- LangGraph: https://github.com/langchain-ai/langgraph/releases
- Claude SDK: Check Anthropic announcements
Frontend (check monthly)
Sources:
- Next.js: https://github.com/vercel/next.js/releases
- React: https://github.com/facebook/react/releases
AI Gateways (check monthly)
- OpenRouter: Check model count at openrouter.ai/models
- New providers/features
Step 2: Compare Against VERSION-TRACKING.md
Read current versions from dev-docs/VERSION-TRACKING.md and identify deltas.
Step 3: Update Files
If changes detected:
VERSION-TRACKING.md
- Update version numbers
- Update
Last Updateddate - Add any new technologies
Affected Skills
# In skills/*/SKILL.md frontmatter: version: X.Y.Z → X.Y.(Z+1) # Increment patch last_updated: [today] external_version: "[new version]"CLAUDE.md (if model changes)
- Update model selection table
- Update pricing if changed
CONTEXT.md
- Add entry to Recent Changes table
Step 4: Generate Report
## Knowledge Update Report - [DATE]
### Summary
- Technologies checked: X
- Updates found: Y
- Files modified: Z
### Version Changes
| Technology | Previous | Current | Source |
|------------|----------|---------|--------|
| GPT | 5.1 | 5.2 | openai.com |
| ... | ... | ... | ... |
### Skills Updated
- skills/openai-agentkit/SKILL.md
- skills/azure-ai-services/SKILL.md
### Action Required
- [ ] Review changes
- [ ] Commit: `git commit -m "chore: update versions [date]"`
- [ ] Push to remote
Automation Patterns
Cron-Style Schedule
Weekly: Every Monday 9 AM
- Run full version check
- Update if changes found
- Generate report
Monthly: First of month
- Deep check including benchmarks
- Update pricing if changed
- Review deprecated technologies
Event-Triggered
Triggers:
- User says "outdated", "latest", "current version"
- Major AI announcement detected
- Before `/design-solution` command
Best Practices
DO
- Always cite sources for version claims
- Preserve existing skill content, only update metadata
- Generate diff-style report showing changes
- Include benchmark scores when available
DON'T
- Update without verification
- Change skill content beyond metadata
- Skip the report generation
- Forget to update CONTEXT.md changelog
Version Numbering Convention
For skills:
- Major (X.0.0): Complete rewrite, breaking changes
- Minor (X.Y.0): New sections, significant additions
- Patch (X.Y.Z): Version updates, typo fixes, metadata only
Integration with Other Commands
/update-knowledge → /design-solution
↓
(ensures latest versions used in recommendations)
/update-knowledge → /commit
↓
(saves updates to git)
Fresh knowledge = accurate recommendations = trusted architect