Turn Your OpenClaw Into a Content Machine (Send This to Your Agent)

This content pipeline was the first thing I built with Marc (my agent).

One podcast episode turns into 6 content pieces. All automated.

Here are the 7 steps to follow:

Step 0: Find Guests

Mona Lisa (my guest research agent) crawls X, Reddit, Hacker News and Product Hunt for founders who match my audience. She filters by recent traction: funding rounds, product launches, revenue milestones.

Then she drafts personalized DMs for each one. No templates. No bulk. Each message references something specific about them.

When I wake up, the outreach is ready. I just review and send.

Step 1: Record the episode

This is the ONLY thing I do. I sit down, have a conversation with a founder, and hit stop.

That's it. That's my entire job in this pipeline.

Step 2: Auto-Detect + Transcript

Jimmy (my YouTube agent) monitors my podcast RSS feed every Tuesday and Friday at 1AM.

The second a new episode drops, he detects it, downloads the audio, and runs OpenAI Whisper for a full word-level transcription with timestamps.

Step 3: Clip Selection + Copy

Claude (my copy editor) scans the full transcript at 2AM. He identifies the 3 best clip-worthy moments. Not random quotes. Moments with a clear hook, tension, or insight that makes you stop scrolling.

For each moment, he writes an X post in my voice. He fact-checks every claim against the transcript. He scores each post on a 1-10 copywriting rubric. Anything below 8 gets rewritten.

He also generates Subtitle Notes for each clip: proper nouns, brand names, technical terms. So the subtitles don't say "Proton emails" when the guest said "ProtonMail."

Everything gets pushed to Notion with the exact timestamps.

Step 4: Video Editing

Adrien (video editor agent) picks up Claude's timestamps at 3:20AM and extracts the clips from the full episode using ffmpeg.

Then he runs the full editing pipeline:

• Whisper transcription for word-level timing
• Smart cuts: removes filler words ("um", "uh", "you know") and silences longer than 0.5s
• Subtitle generation: max 4 words per line, sentence boundaries respected, proper nouns verified against Claude's notes
• Subtitle burn-in: clean white text, no background box
• Final encode: h264 video, AAC audio, 16:9, under 140 seconds

All of this runs through a custom bash script (process_clip.sh) + Python scripts for smart cuts and SRT generation. No manual editing. No Premiere. No CapCut.

Bob (QA agent) then reviews every clip at 4AM. He checks subtitle accuracy word by word against the Whisper output, verifies the clip doesn't trail off, confirms h264 encoding. Score below 10/10 = sent back to Adrien.

Step 5: Scheduling

Dan (X growth agent) picks up the approved clips at 5AM. He uploads each video to Typefully using their 3-step media upload API, creates the draft with the post copy, and schedules them:

• 4PM Bali time (morning Europe)
• 9PM Bali time (morning US East)
• 4AM Bali time (evening US)

3 posts. 3 clips. Every single day. I'm asleep for all of it.

Step 6: Newsletter

Tyler takes the same transcript and pulls out the top insights, frameworks, and actionable takeaways. He structures it into a newsletter draft.

Claude reviews it before anything gets sent. Checks tone, accuracy, structure.

It goes out on Beehiiv to my list.

Step 7: Analytics Feedback Loop

Loop (analytics agent) scrapes engagement data daily. Which clips got impressions. Which posts flopped. Which topics resonated.

Everything lands in my HQ dashboard. So when I sit down to record again, I already know what my audience wants more of.

The stack

• OpenClaw (agent orchestration + crons)
• Whisper (transcription)
• ffmpeg (video extraction + encoding)
• Python scripts (smart cuts + subtitle generation)
• Typefully API (X scheduling with native video)
• Notion API (content database + tracking)
• Beehiiv (newsletter)