You are helping me build the following AI agent workflow. ## Goal Generate Dynamic Sales Pipeline Metrics for Slack: Every Monday, fetches deal data from Airtable to calculate pipeline value, win rate, and top deals. ## Specification - What it does: Every Monday, you get a clear summary of your sales pipeline value, win rate, and top deals delivered straight to Slack. This automation pulls fresh data from Airtable and uses JavaScript logic to calculate these key metrics so your team stays informed without manual work. - Trigger: On a schedule (Scheduled ยท weekly) - Autonomy: Fully hands-off - Expected setup effort: under an hour - Tools/services involved: - Airtable: Fetch deal data and metrics - Code: Calculate sales metrics with JavaScript - Slack: Send formatted weekly report ## Known pitfalls, handle each one explicitly in your implementation 1. Mismatched stage names in Airtable will cause incorrect probability calculations so ensure your code handles case sensitivity and typos gracefully. 2. Slack message length restrictions may truncate long deal lists so limit the number of top deals displayed to fit within character limits. 3. Empty or null values in the amount field can break arithmetic operations so add default zero checks before performing any math. ## Reference implementation https://n8n.io/workflows/6463 (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/generate-dynamic-sales-pipeline-metrics-for-slack/ via usecasesforagents.com