DOCUMENT OPS

AI-assisted spreadsheet repair that proposes corrections for malformed rows

On upload, an agent validates each row and for fixable errors proposes a normalized correction (dates, casing, formats).

CategoryDocument Ops
Enginepaperclip
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew spreadsheet uploaded to DriveGoogle DriveGoogle Drive
  • ActionAgent parses and validates each rowOpenAI
  • LogicPropose corrections; separate fixable from unfixable
  • ActionSend owner original-vs-proposed diff for approvalSlack
  • OutputWrite approved cleaned file back to DriveGoogle DriveGoogle Drive

What it does

Beyond catching bad rows, this agent-driven workflow tries to repair them. For each failing row it reasons about the likely intended value — reformatting dates, normalizing state codes, trimming and casing names, coercing currency — and produces a suggested correction. The owner receives a Slack message showing original versus proposed values and approves or rejects; on approval the corrected file is saved back to Drive.

When to use it

Use it when most upload errors are formatting noise rather than missing data, and you'd rather have a human approve auto-fixes than manually retype rows. It pairs an agent's judgment with a required human checkpoint.

How it works

  1. 1A new spreadsheet in Drive triggers the run.
  2. 2The agent parses and validates each row.
  3. 3For fixable rows the agent proposes a normalized correction; unfixable rows are flagged separately.
  4. 4The owner gets a Slack diff of original vs. proposed and approves.
  5. 5On approval, the corrected rows are merged and the cleaned file is written back to Drive.

Set it up

What you configure once, before turning it on.

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

Run this workflow in your colony.

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