agent hive

MARKET RESEARCH

Weekly Materiality Roll-Up to Notion

Every week aggregates all detected rule changes from the change log, ranks them by materiality and affected business lines.

CategoryMarket Research
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSchedule: weekly end-of-week run
  • ActionRead week's change recordsPostgreSQLPostgres
  • LogicGroup by rule; drop cosmetic changes
  • ActionRank by materiality and tag business linesOpenAI
  • ActionPublish prioritized executive pageNotionNotion
  • OutputPost readout link to leadershipSlack

What it does

Closes the loop on a busy week of regulatory monitoring. It reads the week's accumulated change records, has an LLM rank them by materiality and which business lines they touch, and publishes a single prioritized executive page in Notion — the top movers up top, lower-priority changes collapsed below — so leadership reads one page instead of fifty alerts.

When to use it

Use it when day-to-day alerting is already running and you need a periodic synthesis for executives or a compliance committee. Best as a companion to the diff/alert workflows, turning their raw output into a digestible weekly readout.

How it works

  1. 1A weekly scheduled trigger fires at end of week.
  2. 2The pipeline reads the week's change records from Postgres.
  3. 3A logic step groups changes by rule and drops anything already marked cosmetic.
  4. 4OpenAI ranks the remaining items by materiality and tags affected business lines.
  5. 5Notion receives a new page with a prioritized table and an executive summary at the top.
  6. 6Slack posts the link to the leadership channel.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect NotionPages, databases, comments.
  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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