SUMMARIZATION

On-Demand Slack TL;DR to Notion Decision Log

A /tldr slash command summarizes the current Slack thread into a decision and key points, appends a timestamped entry to a Notion decision-log database.

CategorySummarization
Enginesim
Difficultybeginner
Triggerevent
Steps5
Setup~5 min

How it runs

The automated pipeline, trigger to output.

  • Trigger/tldr slash command invoked on a threadSlack
  • ActionFetch thread messages and participant listSlack
  • ActionLLM summarizes into decision plus key pointsOpenAI
  • ActionAppend entry to Notion decision-log databaseNotionNotion
  • OutputReply in-thread with the Notion page linkSlack

What it does

This is the on-demand companion to automatic thread digesting. Anyone in a channel runs a /tldr slash command on a thread, and the workflow summarizes it into a one-paragraph decision plus bulleted key points. Instead of creating tasks, it writes a permanent, searchable entry into a Notion decision-log database with the date, channel, participants, and a link back to the Slack thread.

When to use it

Use it when the goal is institutional memory rather than task tracking: leadership decisions, architecture choices, policy calls. Teams that ask "why did we decide this?" months later get a clean, queryable Notion record instead of scrolling Slack history.

How it works

  1. 1A user runs the /tldr slash command, which delivers the command payload and thread reference.
  2. 2The thread's parent and replies are pulled from Slack along with participant names.
  3. 3The LLM produces a decision summary and key points in structured form.
  4. 4A new page is appended to the Notion decision-log database with metadata fields populated.
  5. 5The workflow replies in-thread (visible to the invoker) with the Notion page link confirming the decision was logged.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SlackChannels, DMs, threads, mentions.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect NotionPages, databases, comments.
  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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