A weekly podcast has roughly 14 hours of post-production work baked into every episode. Recording is the easy part. The grind is everything after: transcribing the conversation, writing show notes that do not read like a robot summarized them, chopping the audio into chapter markers, pulling quotes for social, researching the next guest, and getting the RSS feed updated before your Tuesday deadline. Automating most of that pipeline with OpenClaw can drop post-production time from 14 hours to about 90 minutes of human review per episode.
This guide walks through building that system. You will need a working OpenClaw installation (follow our setup guide if you do not have one) and a podcast that records audio files you can point OpenClaw at. The pipeline handles transcript processing, show notes, chapter markers, social clips, guest research, episode scheduling, and RSS feed management. Each piece is a separate OpenClaw skill, wired together with cron jobs and the heartbeat.
The Full Podcast Production Pipeline
Before building individual skills, here is the sequence of events from the moment you finish recording to the moment the episode goes live:
- You drop the audio file into a designated folder (local or cloud). This is the only manual trigger in the pipeline.
- Transcription runs within minutes, producing a timestamped transcript with speaker labels.
- Show notes, chapter markers, and social quotes are generated in parallel from the transcript.
- Guest research for next week’s episode runs overnight as a separate cron job.
- Morning heartbeat sends you a Telegram summary: episode ready for review, show notes draft, three social quotes, and chapter markers. One tap to approve.
- After approval, OpenClaw updates your RSS feed, schedules social posts, and files everything in your episode archive.
The whole thing takes about 20 minutes of active human time: reviewing the show notes, picking which social quotes to use, and scanning the chapter markers for accuracy. Everything else happens in the background.
Stage 1: Transcript Processing
Transcription is the foundation. Every downstream skill depends on having a clean, timestamped transcript with speaker labels. Get this wrong and everything else produces garbage.
Choosing a Transcription Approach
OpenClaw does not transcribe audio natively. It orchestrates transcription by calling an external service. The two approaches that work best:
Whisper API (OpenAI): Best accuracy for English-language podcasts. Handles crosstalk and filler words well. Costs roughly $0.006 per minute of audio, so a 60-minute episode runs about $0.36. No speaker diarization built in, but you can add it with a post-processing step.
Deepgram API: Better for multi-speaker identification out of the box. Speaker diarization is included in the response, which saves you a processing step. Slightly lower accuracy on mumbled speech compared to Whisper, but the speaker labels make the tradeoff worthwhile for interview shows. Pricing is comparable at $0.0043 per minute.
Deepgram is the better choice for interview-format shows because the speaker labels save 30 minutes of manual cleanup per episode. For solo shows, Whisper is the better choice.
The Transcription Skill
Create skills/podcast-transcribe.md in your workspace:
## Podcast Transcription Skill
When a new audio file appears in workspace/podcast/inbox/:
1. Identify the audio file (mp3, wav, m4a)
2. Call the Deepgram API with these parameters:
- model: nova-3
- diarize: true
- punctuate: true
- paragraphs: true
- utterances: true (for timestamp accuracy)
3. Parse the API response into a structured transcript:
- Format: [HH:MM:SS] Speaker N: "text"
- Group by speaker turns, not individual sentences
- Preserve filler words for authenticity scoring later
4. Save to workspace/podcast/transcripts/[episode-slug].md
5. Move the audio file to workspace/podcast/processed/
6. Log: episode slug, duration, speaker count, word count
Triggering Transcription Automatically
Wire transcription to a file-watch cron that checks for new audio every 15 minutes:
openclaw cron add --name "podcast-transcribe" \
--cron "*/15 * * * *" \
--session isolated \
--message "Check workspace/podcast/inbox/ for new audio files. If found, run the podcast-transcribe skill. If no new files, do nothing."
The --session isolated flag matters here. Transcription involves passing large API responses through the context window, and you do not want that bleeding into your main session history.
Stage 2: Show Notes Generation
Show notes are where most podcasters either phone it in or spend two hours agonizing over the phrasing. OpenClaw hits the sweet spot: detailed enough to be useful for listeners who skim, structured enough for SEO, and written fast enough that you can publish the same day you record.
What Good Show Notes Include
Based on common patterns across successful podcast show notes, the format that works best is:
- Episode summary (3-4 sentences, not a paragraph-long wall of text)
- Key topics discussed with timestamps (so listeners can skip ahead)
- Guest bio (2-3 sentences, pulled from the guest research skill)
- Resources mentioned (links to tools, articles, books referenced in the conversation)
- Notable quotes (2-3 pull quotes that capture the episode’s best moments)
Skip the generic “In this episode, we discuss…” opener. Listeners who read show notes already know what episode they are looking at. Lead with the most interesting thing that was said.
The Show Notes Skill
## Podcast Show Notes Skill
When given a transcript path:
1. Read the full transcript
2. Generate show notes with these sections:
### Episode Summary
- 3-4 sentences capturing the core argument or story, not a neutral overview
- Lead with the most interesting insight or claim from the conversation
- Include the guest's name and relevant credential (one, not their full CV)
### Topics and Timestamps
- Extract 6-10 major topic shifts from the transcript
- Match each to the nearest timestamp from the transcript
- Format: [HH:MM:SS] Topic description (1 line, specific)
### Resources Mentioned
- Scan transcript for tool names, book titles, article references, company names
- For each, find the URL via web search
- Format as a linked list
### Pull Quotes
- Extract 3 quotes that are specific, surprising, or opinionated
- Avoid generic wisdom ("It's all about the customer")
- Include speaker attribution and timestamp
- Each quote should work as a standalone social media post
3. Save to workspace/podcast/show-notes/[episode-slug].md
4. Update episode status in workspace/podcast/calendar.md to 'notes-ready'
A Detail That Matters
The show notes skill should reference the guest research file (from Stage 5) when writing the guest bio. If guest research ran before recording, OpenClaw already has a file at workspace/podcast/guests/[guest-name].md with background information. The show notes skill should check for that file and use it rather than inventing a bio from the transcript alone.
Without this step, show notes might describe a guest as a “startup founder” when they are actually the CTO of a 500-person company. If the transcript does not mention an exact title but the guest research file has it, the skill should always check the research file first.
Stage 3: Chapter Markers and Timestamps
Chapter markers make the difference between a podcast people listen to sequentially and one they use as a reference. For technical and interview shows especially, listeners come back to specific segments. Good chapter markers turn an episode into a searchable resource.
Generating Chapter Markers from Transcripts
## Podcast Chapter Markers Skill
When given a transcript path:
1. Read the transcript and identify major topic transitions
2. For each transition, record:
- Timestamp (HH:MM:SS)
- Chapter title (5-8 words, specific not vague)
- One-sentence description of what is discussed
3. Rules for chapter identification:
- Minimum 3 minutes between chapters (avoid micro-chapters)
- Maximum 15 chapters per hour of content
- First chapter starts at 00:00:00 (intro/cold open)
- Include a chapter for any guest introduction
- Mark Q&A or audience question segments separately
4. Output two formats:
- Markdown table for show notes
- Podlove Simple Chapters XML for podcast apps
5. Save both to workspace/podcast/chapters/[episode-slug]/
Podlove format example:
<psc:chapters version="1.2">
<psc:chapter start="00:00:00" title="Cold Open" />
<psc:chapter start="00:02:15" title="Guest Introduction: Jane Smith" />
<psc:chapter start="00:05:30" title="Why Traditional CI/CD Pipelines Break at Scale" />
</psc:chapters>
The Podlove Simple Chapters format is supported by Apple Podcasts, Overcast, Pocket Casts, and most modern podcast apps. Embedding chapter markers in your episode file means listeners see clickable segments without visiting your website. If your hosting platform supports chapter metadata (Transistor, Buzzsprout, and Podbean all do), OpenClaw can include these in the RSS feed update.
Timestamp Accuracy
One caveat: timestamps from the transcript are only as accurate as the transcription service. Deepgram’s utterance-level timestamps are accurate to within 1-2 seconds for most content. Whisper’s word-level timestamps drift more, sometimes by 5-10 seconds in longer episodes. If you use Whisper, budget an extra 5 minutes during review to spot-check chapter start times against the actual audio.
Stage 4: Social Media Clips and Quotes
A single podcast episode should produce a week’s worth of social content. Most podcasters extract one audiogram and call it done. OpenClaw can pull 8-12 social assets from every episode without you touching an editing tool.
The Social Extraction Skill
## Podcast Social Media Skill
When given a transcript path and show notes path:
1. Read both files
2. Generate social assets:
### Quote Cards (5-8 per episode)
- Extract quotable moments: specific claims, surprising stats, contrarian takes
- Format each as a standalone statement (no "As I said on the podcast..." framing)
- Include speaker name and episode title
- Flag the top 3 as "high-impact" for priority posting
### Twitter/X Threads (2 per episode)
- Thread 1: Key insights from the conversation (5-7 tweets)
- Thread 2: Behind-the-scenes or contrarian angle (4-5 tweets)
- First tweet of each thread must hook without mentioning the podcast
- Last tweet links to the episode
### LinkedIn Post
- 150-200 words, professional angle
- Lead with a specific insight, not "Had a great conversation with..."
- Tag the guest if their LinkedIn handle is in the guest research file
### Audiogram Suggestions
- Identify 3-5 segments (30-60 seconds each) with high quote density
- For each, provide: start timestamp, end timestamp, suggested caption
- Prioritize segments where the speaker makes a clear, standalone point
3. Save all assets to workspace/podcast/social/[episode-slug]/
4. Create a posting schedule:
- Day 0 (publish day): Thread 1 on X, LinkedIn post
- Day 1: Top quote card
- Day 2: Thread 2 on X
- Days 3-6: Remaining quote cards, one per day
Why Audiogram Suggestions Instead of Audiograms
OpenClaw identifies the best segments and provides timestamps, but it does not generate the actual audiograms (the video files with waveforms and captions). That step requires audio processing tools like Headliner, Descript, or FFmpeg scripts. OpenClaw’s role is curation: telling you which 45 seconds of a 60-minute episode will perform best on social, so you are not scrubbing through the entire recording looking for clip-worthy moments.
If you want to fully automate audiogram creation, you can chain OpenClaw’s timestamp output into a Descript API call or an FFmpeg script that extracts the audio segment, overlays captions, and renders the video. This is not a standard OpenClaw skill because the video rendering step is compute-heavy and easier to handle with dedicated tooling.
Stage 5: Guest Research Prep
Guest research is the skill that pays for itself on the first episode. Before every interview, OpenClaw builds a guest dossier that covers their background, recent work, talking points, and questions you have not seen on every other podcast they have appeared on.
The Guest Research Skill
## Podcast Guest Research Skill
When given a guest name and optional context (company, role, topic):
1. Web search for the guest:
- Recent interviews and podcast appearances (last 12 months)
- Published articles, blog posts, or talks
- Company announcements or product launches
- Social media presence (Twitter/X, LinkedIn posts)
2. Build a guest dossier with:
### Background (3-4 sentences)
- Current role and company
- Relevant previous experience
- Why they are interesting for this topic
### Recent Activity
- 3-5 bullet points of things they have said or done in the last 6 months
- Focus on positions they have taken, not resume items
### Questions Other Podcasters Asked
- List 5-8 questions from recent interviews
- Purpose: so you can AVOID repeating these
### Questions Nobody Has Asked Yet
- Based on their recent work and public positions, suggest 5-7 original questions
- Focus on tensions, contradictions, or unexplored angles
- Example: if they advocate for X publicly but their company recently did Y, ask about that
### Talking Points to Explore
- 3-4 topics where their expertise intersects with your show's audience
3. Save to workspace/podcast/guests/[guest-name-slugified].md
4. Log the research date so it can be refreshed if the interview gets rescheduled
Scheduling Guest Research
Run guest research 48 hours before the scheduled recording, not the night before. This gives you time to read the dossier and add your own questions:
openclaw cron add --name "guest-research" \
--cron "0 6 * * *" \
--session isolated \
--message "Check workspace/podcast/calendar.md for any recording scheduled within the next 48 hours. If the guest does not have a research file, run the podcast-guest-research skill. If the research file exists but is older than 14 days, refresh it."
The 14-day refresh window handles rescheduled recordings. If a guest got pushed back two weeks, their dossier gets updated with anything new they have published or said in the interim.
Stage 6: Episode Scheduling and RSS Feed Management
Publishing a podcast episode is not just uploading audio. It is updating the RSS feed with the right metadata, scheduling the publish time for your audience’s timezone, and making sure the show notes, chapter markers, and social assets all go live in coordination.
Managing the Episode Calendar
Create workspace/podcast/calendar.md to track episode status:
## Podcast Calendar
| Episode | Guest | Record Date | Status | Publish Date |
|---------|-------|-------------|--------|-------------|
| EP042: Scaling ML Infrastructure | Jane Smith | 2026-03-28 | published | 2026-04-01 |
| EP043: Why RAG Is Overrated | Mike Chen | 2026-04-04 | notes-ready | 2026-04-08 |
| EP044: Open Source LLM Economics | Sarah Kim | 2026-04-11 | guest-researched | 2026-04-15 |
| EP045: TBD | — | 2026-04-18 | planning | 2026-04-22 |
## Publishing Rules
- Episodes publish every Tuesday at 5 AM Eastern
- Show notes go live on the website 1 hour before audio
- Social posts begin at 10 AM Eastern on publish day
- Never publish two episodes in the same week unless it's a bonus episode
RSS Feed Updates
Most podcast hosts (Transistor, Buzzsprout, Podbean, Libsyn) manage the RSS feed for you when you upload through their dashboard or API. OpenClaw’s role is to prepare and push the metadata:
## Podcast RSS Publishing Skill
When given an approved episode with all assets ready:
1. Verify all required files exist:
- Audio file in workspace/podcast/processed/
- Show notes in workspace/podcast/show-notes/
- Chapter markers in workspace/podcast/chapters/
- Social assets in workspace/podcast/social/
2. Upload to podcast host via API:
- Episode title, description (from show notes summary)
- Chapter markers (Podlove XML if supported)
- Tags and categories
- Publish date/time from calendar
- Episode number and season number
3. Update website:
- Create or update the episode page with full show notes
- Embed the player widget
- Add transcript (collapsible section)
- Link to resources mentioned
4. Queue social posts:
- Schedule according to the posting timeline from the social skill
- Verify all posts are queued in Buffer, Typefully, or your scheduling tool
5. Update calendar status to 'published'
6. Send Telegram confirmation with the episode URL and social schedule
If your podcast host does not have an API (some smaller hosts do not), OpenClaw can prepare all the metadata in a structured format and send you a Telegram checklist for manual upload. The human step takes 5 minutes instead of 30 because everything is already formatted and ready to paste.
Wiring It All Together with Cron and Heartbeat
The individual skills are useful on their own, but the real leverage comes from chaining them. Here is how the cron schedule and heartbeat work together for a weekly podcast:
Cron Schedule
# Transcription: check for new audio every 15 minutes
openclaw cron add --name "podcast-transcribe" \
--cron "*/15 * * * *" \
--session isolated \
--message "Check inbox for new audio. Transcribe if found."
# Post-processing: run 30 minutes after transcription window
openclaw cron add --name "podcast-post-process" \
--cron "0 * * * *" \
--session isolated \
--message "Check for transcripts without show notes. Run show-notes, chapter-markers, and social-media skills in sequence."
# Guest research: daily at 6 AM, checks for upcoming recordings
openclaw cron add --name "guest-research" \
--cron "0 6 * * *" \
--session isolated \
--message "Research guests for recordings in the next 48 hours."
# Publishing: Tuesday at 4:30 AM Eastern
openclaw cron add --name "podcast-publish" \
--cron "30 4 * * 2" \
--session isolated \
--message "Publish the next approved episode via podcast-rss-publish skill."
Heartbeat Configuration
Add to your heartbeat.md:
## Podcast Pipeline Status
If today is Monday and the time is between 09:00 and 09:30:
1. Check workspace/podcast/calendar.md for tomorrow's scheduled episode
2. Verify all assets are ready: show notes, chapters, social posts
3. If anything is missing, flag it
4. Send me a pre-publish checklist via Telegram:
- Episode title and guest
- Show notes preview (first 3 sentences)
- Number of social posts queued
- Chapter count
- Any missing assets that need attention
- "Approve for Tuesday publish?"
If a new transcript was processed today:
1. Send me a summary: episode title, duration, word count
2. Estimated time until show notes are ready
3. Link to the transcript for early review if I want it
The Monday pre-publish check is the critical human touchpoint. Everything else can run autonomously, but this is where you catch problems: a chapter marker that landed on the wrong topic, a show notes summary that misrepresents what the guest said, or a social quote taken out of context. Monday review, Tuesday publish. Clean cadence.
What This Costs to Run
For a weekly podcast producing one 60-minute episode:
| Pipeline Stage | Model/Service | Cost Per Episode |
|---|---|---|
| Transcription (Deepgram) | Nova-3 | ~$0.26 |
| Show notes generation | Claude Opus 4.6 | ~$1.20-1.80 |
| Chapter markers | Claude Sonnet 4.6 | ~$0.30-0.50 |
| Social media assets | Claude Sonnet 4.6 | ~$0.40-0.60 |
| Guest research | Gemini 3.1 Pro (web search) | ~$0.50-0.80 |
| RSS/publishing automation | Claude Haiku | ~$0.05 |
| Heartbeat (weekly checks) | Claude Haiku | ~$0.10 |
| Total per episode | $2.80-4.05 |
At four episodes per month, you are looking at $11-16 in direct API costs. Add the heartbeat running throughout the week and the total lands around $15-25/month.
Compare that to hiring a podcast producer ($200-500/episode) or a VA for post-production ($15-25/hour for 10+ hours of work per episode). Even at the low end of outsourcing, OpenClaw pays for an entire year of automation in what you would spend on a single episode of human post-production.
The caveat: you still need to record the podcast, review the outputs, and make editorial decisions about which quotes to promote. OpenClaw eliminates the labor, not the judgment.
Frequently Asked Questions
Can OpenClaw edit my podcast audio, like removing ums or long pauses?
No. OpenClaw works with text and APIs, not audio waveforms. For audio editing, use Descript (which has its own AI cleanup features) or Adobe Podcast. OpenClaw’s role starts after the audio is finalized: it handles the transcript, show notes, and publishing metadata. You could chain OpenClaw with Descript’s API to trigger audio cleanup before transcription, but that is a custom integration.
How accurate are the chapter markers?
Within 1-3 seconds when using Deepgram for transcription. The timestamps come from the transcription service’s utterance-level timing, not from OpenClaw guessing. Whisper timestamps drift more, sometimes 5-10 seconds in longer episodes. Budget 5 minutes during review to spot-check 3-4 chapter start times against the audio. Deepgram’s markers are generally accurate enough to publish without correction about 85% of the time.
What if my podcast has three or more speakers?
Deepgram handles multi-speaker diarization well up to about five distinct speakers. Beyond that, accuracy drops. The show notes and social skills work fine with any number of speakers as long as the transcript has speaker labels. The main challenge is the guest research skill, which is designed for one guest at a time. For panel episodes, run the research skill once per guest and have OpenClaw merge the dossiers.
Do I need a specific podcast hosting platform for RSS automation?
Any host with an API works: Transistor, Buzzsprout, Podbean, and Captivate all have well-documented APIs. If your host does not have an API (some free-tier hosts strip this out), OpenClaw can generate a pre-formatted upload package and send it to you via Telegram. You paste the metadata manually, which takes about 5 minutes.
How do I handle episodes that get rescheduled?
The guest research skill automatically refreshes dossiers older than 14 days when a recording date changes. Update the publish date in workspace/podcast/calendar.md and the cron pipeline adjusts. The Monday pre-publish heartbeat checks the calendar dynamically, so it will flag the correct episode for the upcoming Tuesday regardless of what shifted.
Can OpenClaw generate video clips from podcast audio?
OpenClaw can identify the best segments with timestamps and captions, but it does not render video. The audiogram suggestion output from the social skill is designed to feed into Headliner, Descript, or a custom FFmpeg script that handles the actual video creation. Think of OpenClaw as the curator (which 45 seconds to clip) and the video tool as the renderer.
How long does the full pipeline take from recording to publish-ready?
About 25-35 minutes of processing time and 20 minutes of human review. Transcription takes 5-8 minutes for a 60-minute episode. Show notes, chapters, and social assets generate in parallel and finish within 10-15 minutes. The bottleneck is your review, not the automation.
Key Takeaways
- The OpenClaw podcast pipeline is six skills chained with cron jobs: transcription, show notes, chapter markers, social extraction, guest research, and RSS publishing. Each skill runs independently, which means you can adopt them one at a time.
- Deepgram with speaker diarization is the better transcription choice for interview shows. Whisper is better for solo content. The speaker labels save significant manual cleanup time downstream.
- Guest research running 48 hours before recording is the highest-leverage skill in the pipeline. Original questions that other interviewers have not asked produce better conversations and more quotable moments.
- Budget 20 minutes of human review per episode, focused on the Monday pre-publish checklist. The automation handles labor; you handle judgment calls about tone, accuracy, and which social quotes represent your guest fairly.
- Total cost is $15-25/month for a weekly show, compared to $800-2,000/month for equivalent human post-production labor.
Related Resources
- How to Set Up OpenClaw the Right Way: 10 Steps — prerequisite installation and model configuration
- OpenClaw Cron Jobs: Schedule Automated Tasks — deep dive on cron syntax, isolated sessions, and chaining
- OpenClaw Heartbeat Scheduling: Configure Proactive Agent Behavior — interval tuning and Telegram integration
- How to Use OpenClaw for Content Creation: Automated Writing Workflows — similar pipeline architecture applied to blog content
- How to Use OpenClaw for Social Media Scheduling — detailed guide on Buffer and Typefully integrations
- How to Connect Buffer to OpenClaw — setting up the social posting integration used in Stage 4
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