PROJECT MANAGEMENT

Capture Standup Commitments from Slack and Register Them as Linear Dependencies

Watches a team's daily standup channel for sentences where someone promises to deliver something to another team.

CategoryProject Management
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew message in standup Slack channelSlack
  • LogicLLM extracts deliverable promises (owner, recipient, due date)OpenAI
  • LogicFilter out messages with no commitment
  • ActionCreate Linear issue with assignee, due date, dependency labelLinearLinear
  • OutputReply in Slack thread confirming the tracked commitmentSlack

What it does

Reads new messages in your standup or daily-sync Slack channel, uses an LLM to spot commitments ("I'll have the API spec to the mobile team by Thursday"), and turns each one into a Linear issue labeled `cross-team-dependency` with the committer as assignee, the inferred due date, and the requesting team as a linked label. The original Slack message is linked back so context is never lost.

When to use it

When promises made verbally in standups quietly evaporate and the receiving team is left blocked with no paper trail. Best for orgs running async or written standups where one team's deliverable gates another's work.

How it works

  1. 1A new message posts in the designated standup channel.
  2. 2An LLM classifies the message: does it contain a deliverable promise (who owes what, to whom, by when)? Non-commitments are dropped.
  3. 3For each promise, the flow resolves the committer's Slack identity to a Linear user.
  4. 4A Linear issue is created with assignee, due date, the `cross-team-dependency` label, and a permalink to the source message.
  5. 5A threaded Slack reply confirms the captured commitment and links the new issue.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SlackChannels, DMs, threads, mentions.
  2. 2
    Connect LinearIssues, projects, cycles, triage.
  3. 3
    Connect OpenAIModels, embeddings, files.
  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.

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