SUMMARIZATION
Voice-Note Backlog to Linear Follow-Up Issues
Drains a backlog of voice memos by transcribing each, classifying it as decision vs. action.
How it runs
The automated pipeline, trigger to output.
- TriggerManual run over memo backlog folder
- ActionTranscribe each memo with ElevenLabsElevenLabs
- ActionClassify decision vs action + extract ownerOpenAI
- LogicBranch on action vs decision
- ActionOpen Linear issue for each actionLinear
- OutputArchive decisions to NotionNotion
What it does
Processes a queue of voice memos and splits them by intent: anything that needs to be done becomes a tracked Linear issue assigned to the named owner, while recorded decisions are filed to a Notion archive. Nothing spoken gets lost between "decided" and "done."
When to use it
When voice notes mix "we decided X" with "someone needs to do Y" and the action items never make it into the tracker. Run it on demand to clear a built-up backlog and convert talk into tickets.
How it works
- 1Triggered manually against a folder of pending audio memos.
- 2ElevenLabs transcribes each memo.
- 3An LLM classifies each into decision or action item and pulls out owner and due context.
- 4A logic branch routes actions versus decisions.
- 5Actionable items open Linear issues with owner and description.
- 6Pure decisions are appended to a Notion decision archive.
Set it up
What you configure once, before turning it on.
- 1Connect ElevenLabsText-to-speech, voice cloning.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect LinearIssues, projects, cycles, triage.
- 4Connect NotionPages, databases, comments.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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