PERSONAL PRODUCTIVITY

Commitment ledger with overdue escalation

Logs every promise from your sent mail into a Postgres ledger, then on a schedule checks for commitments past their due date and pages you in Slack.

CategoryPersonal Productivity
Enginesim
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew sent emailGmailGmail
  • ActionExtract commitmentsOpenAI
  • ActionInsert into Postgres commitment ledgerPostgreSQLPostgres
  • ActionScheduled query for overdue open itemsPostgreSQLPostgres
  • LogicSkip if nothing overdue
  • OutputEscalate overdue promises to SlackSlack

What it does

This two-part workflow first records each commitment you make in outgoing email into a durable Postgres ledger (recipient, promise, due date, status). Separately, a scheduled sweep queries the ledger for anything still open and past due, then escalates those overdue items to you in Slack. It tracks not just what you promised but whether you actually delivered.

When to use it

Use it when a one-time reminder isn't enough and you need a persistent, queryable record of obligations with real escalation. Good for high-volume senders who want an audit trail and a nag when something is late.

How it works

  1. 1A sent email triggers extraction of any commitments via OpenAI.
  2. 2Each commitment is inserted into the Postgres ledger with status "open."
  3. 3On a daily schedule, a query selects open rows whose due date has passed.
  4. 4A logic step formats the overdue set and exits quietly if empty.
  5. 5Overdue commitments are posted to Slack as an escalation with their age in days.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GmailRead, draft, send, label.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect PostgresAny Postgres URL — query, write, migrate.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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