PROJECT MANAGEMENT

Monday At-Risk Dependency Weekly Digest to Sheet

Builds a weekly digest of every at-risk dependency on a Monday board, logs each one to a Google Sheet for trend tracking, and emails a ranked summary of the top risks via Gmail.

CategoryProject Management
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule
  • ActionRead dependencies, progress, and slackmonday.com
  • LogicScore and rank at-risk dependencies
  • ActionAppend scored rows to tracking sheetGoogle DriveGoogle Drive
  • ActionCompose ranked top-risks summaryOpenAI
  • OutputEmail digest to project leadGmailGmail

What it does

Once a week this workflow inventories every dependency relationship on a Monday board, scores each for slip risk based on predecessor slack and current progress, and records the full list in a Google Sheet so risk can be tracked week over week. It then emails a ranked top-risks summary to the project lead.

When to use it

Use it when you want a durable, auditable record of how dependency risk trends over a project's life, not just a one-time snapshot, plus a digestible weekly email of where to focus.

How it works

  1. 1A weekly schedule starts the run.
  2. 2The flow reads all dependency links, progress, and timeline slack from the Monday board.
  3. 3A logic step scores each dependency and ranks the at-risk ones.
  4. 4Every scored dependency is appended as a dated row to the tracking Google Sheet.
  5. 5OpenAI composes a ranked top-risks summary from the highest scores.
  6. 6The summary is emailed to the project lead via Gmail.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect monday.comVisual work management for teams.
  2. 2
    Connect Google DriveDocs, sheets, slides, files.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect GmailRead, draft, send, label.
  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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