You are helping me build the following AI agent workflow.

## Goal
Analyze Customer Survey Feedback: Daily workflow that groups survey responses by sentiment, uses AI to extract themes and recommendations.

## Specification
- What it does: You get daily summaries of customer feedback grouped by sentiment with AI-generated themes and actionable recommendations. This workflow automatically pulls data from Google Sheets, processes it through OpenRouter Chat Model, and posts the key highlights directly to your Slack channel for immediate review.
- Trigger: On a schedule (Scheduled · daily)
- Autonomy: Fully hands-off
- Expected setup effort: about an afternoon
- Tools/services involved:
  - Google Sheets: Survey data source and summary storage
  - Slack: Posting highlights and notifications
  - OpenRouter Chat Model: AI analysis of survey sentiment and themes
  - Code: Batching data and aggregating results

## Known pitfalls, handle each one explicitly in your implementation
1. Large batches of survey data can exceed token limits so split your chunks to ensure complete processing.
2. Inconsistent formatting in raw survey responses may confuse the parser so add a cleaning step before analysis.

## Reference implementation
https://n8n.io/workflows/9809 (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/analyze-customer-survey-feedback-with-ai/ via usecasesforagents.com