You are helping me build the following AI agent workflow.

## Goal
Auto-Categorize Blog Posts: Scans new Markdown blog posts in GitHub, uses GPT-4 to suggest categories and tags, and logs them to Google Sheets for review.

## Specification
- What it does: You save time on manual tagging as this agent scans your new GitHub blog posts to suggest relevant categories and tags via GPT-4. It logs these AI-generated recommendations directly into Google Sheets for your review while automatically preventing duplicate analysis of existing files.
- Trigger: Run manually (Manual · on demand)
- Autonomy: You stay in control
- Expected setup effort: about an afternoon
- Tools/services involved:
  - GitHub: Retrieve blog post content and metadata
  - Google Sheets: Store existing tags and log AI suggestions
  - OpenAI Chat Model: Analyze content and suggest categories/tags

## Known pitfalls, handle each one explicitly in your implementation
1. Markdown frontmatter might contain metadata that confuses the categorizer, so strip it before sending content to the LLM.
2. Google Sheets append operations can fail if column headers change, so validate the sheet structure in a dry run first.

## Reference implementation
https://n8n.io/workflows/9983 (workflow template)
Fetch and inspect this before building. If it matches my stack, adapt it;
if not, rebuild the pattern with my tools.

## Process requirements
1. Before building: ask me which of the listed tools I actually use and
   what my platform is (n8n / Make / code / other). Do not assume.
2. Adapt the pattern to my answers; do not force the reference stack.
3. Address every pitfall above; tell me how you handled each.
4. Provide a test plan I can run before letting this touch real data.
5. Ask before any step that sends messages, modifies data, or spends money.

Source: https://usecasesforagents.com/use-case/auto-categorize-blog-posts-with-ai/ via usecasesforagents.com