How to Automate Podcast-to-LinkedIn Repurposing With the SparkVox API

Every podcast host knows the post-publish checklist: upload to the host, write show notes, clip a quote for social, schedule LinkedIn posts, notify the guest. Most of that still happens by hand because repurposing tools treat automation as an afterthought - or hide it behind an enterprise tier.

The SparkVox Developer API is built for the opposite case: your episode drops, your stack creates a project, Sparky runs the same transcription and sprout extraction as the web app, and a webhook tells you when drafts are ready. Same credits. Same voice pipeline. No sales call.

What the API actually does

At a high level, the API mirrors what you do in the app. You submit a podcast URL, audio file, or transcript as a project. SparkVox transcribes (if needed), extracts insight-rich moments, and writes one LinkedIn post per moment. When processing finishes, you get a project.ready webhook with the project ID and post count.

From there you can open the sprout tree for human review, or use the Posts endpoints to list drafts, request AI images, edit copy, and schedule to LinkedIn, Buffer, or Publer. The full reference lives at sparkvox.io/docs/api.

A minimal automation in four steps

1. Create an API key. In the SparkVox app, go to Settings → Developer. Keys start with sk_sparkvox_. Treat them like passwords.

2. Register a webhook. POST to /api/v1/webhooks with your endpoint URL and subscribe to project.ready and project.failed. Verify HMAC signatures on incoming payloads so only SparkVox can trigger your handler.

3. Create a project when a new episode publishes. Wire this to your RSS feed (Zapier, n8n, or a cron job), or call the API directly from your post-production script. For YouTube episodes, resolve duration first with the public /api/url-metadata helper, then POST the project with source_type: "url".

4. Act on the webhook. When project.ready fires, notify your team in Slack, open a review queue, or auto-schedule posts you have already approved via template rules. Most teams keep a human in the loop for the first few episodes, then tighten automation once they trust the sprout output.

Example: RSS to project

Pseudocode for the happy path:

on new_rss_item(episode):
  duration = fetch_url_metadata(episode.audio_url).duration_seconds
  project = POST /api/v1/projects {
    title: episode.title,
    source_type: "url",
    source_url: episode.audio_url,
    duration_seconds: duration,
    platforms: ["linkedin"]
  }
  log("Created project", project.id)

on webhook project.ready(payload):
  notify_slack("#content", "Sprout tree ready: " + payload.post_count + " posts")
  # or: GET /api/v1/projects/{id}/posts and schedule via Buffer/Publer

Billing is the same as the app

API projects consume 3 credits per minute of content ($0.27/min) - same rate for transcript, audio, or video. A 45-minute episode is about 135 credits ($12.15 at default rates) and typically yields many LinkedIn posts. Generation and sprout-tree images are included. There is no separate API surcharge.

Where to go next

Read the quickstart, browse the automate podcast ops page for pipeline ideas, or see how to build a hands-off pipeline with n8n and webhooks. If you are evaluating tools, compare what enterprise API access costs elsewhere in why repurposing tools hide their APIs.