SOCIAL MEDIA
Drain an Airtable queue of LinkedIn posts into rendered audiograms
On a schedule, pulls every Airtable row marked 'Queued', narrates and renders an audiogram for each, then flips the row to 'Done' with its clip URL.
How it runs
The automated pipeline, trigger to output.
- TriggerSchedule every few hours
- ActionFetch Airtable rows marked Queued (batch-capped)Airtable
- ActionGenerate voiceover per post with ElevenLabsElevenLabs
- ActionRender audiogram and upload to R2Cloudflare R2
- LogicBranch on render success vs failure
- OutputMark Airtable rows Done with clip URL or flag for reviewAirtable
What it does
It treats an Airtable table as a work queue: each pending LinkedIn post gets narrated, rendered into an audiogram, and marked complete with its finished file link, so a backlog drains itself on a steady cadence.
When to use it
When you've collected dozens of posts to repurpose and want a reliable batch processor instead of one-off runs. Good for agencies or solo operators clearing a backlog, where Airtable is already the source of truth for the content pipeline.
How it works
- 1A schedule runs every few hours.
- 2The flow queries Airtable for rows whose status is "Queued," capped at a safe batch size per run.
- 3For each row, the post text is shaped into a narration script.
- 4ElevenLabs generates the voiceover and the flow renders the audiogram.
- 5The clip is uploaded to R2 for a durable URL.
- 6Each processed Airtable row is updated to "Done" with the audio duration and clip link; failures are flagged "Needs review" so nothing silently disappears.
Set it up
What you configure once, before turning it on.
- 1Connect AirtableBases, tables, views, automations.
- 2Connect ElevenLabsText-to-speech, voice cloning.
- 3Connect Cloudflare R2Object storage, S3-compatible.
- 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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