PERSONAL PRODUCTIVITY
Capture OKR Check-In Replies into a Confidence-Rated Coda Table
Listens for owners' Slack replies to the weekly check-in, parses each reply into a progress note and confidence rating.
How it runs
The automated pipeline, trigger to output.
- TriggerOwner replies in Slack check-in threadSlack
- ActionParse reply into KR, progress, confidenceOpenAI
- LogicValidate confidence; flag unclear replies
- ActionUpsert structured row into Coda tableCoda
- OutputAdd checkmark reaction confirming captureSlack
What it does
Turns free-form Slack check-in replies into clean, structured data. When an owner responds in the check-in thread or DM, the workflow extracts the key result they're referring to, their narrative update, and a confidence signal (green/yellow/red), then logs it as a row in Coda so the status board builds itself in real time.
When to use it
Use this when owners answer check-ins in their own words and someone is manually copy-pasting those replies into a tracker. It removes the transcription step and keeps Coda current the moment people respond.
How it works
- 1An incoming Slack message in the check-in thread triggers the flow.
- 2An OpenAI step interprets the reply: which KR it maps to, a one-line progress summary, and a normalized confidence rating.
- 3A logic step validates the parse — if confidence can't be determined, it flags the row as 'needs clarification' instead of guessing.
- 4The workflow upserts a row in the Coda check-in table keyed by KR and owner so a re-reply overwrites the stale entry.
- 5It reacts to the Slack message with a checkmark so the owner knows their update landed.
Set it up
What you configure once, before turning it on.
- 1Connect SlackChannels, DMs, threads, mentions.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect CodaDocs, packs, automations.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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