podcli
Reduces manual podcast editing labor and accelerates content distribution to social platforms by automating clip generation, face tracking, and caption proce…
Open-source AI podcast clipper. Generate vertical clips with face tracking and burned-in captions. CLI, MCP server, web app.
- Generate short vertical video clips from long podcast episodes for social media posting.
- Automatically track speakers' faces and add captions to podcast video content.
- Batch process multiple podcast recordings into shareable short-form clips efficiently.
Reduces manual podcast editing labor and accelerates content distribution to social platforms by automating clip generation, face tracking, and caption processing—lowering time-to-publish for media teams.
Content operations teams and media companies repurposing long-form podcast episodes into short-form social video assets.
https://github.com/nmbrthirteen/podcli
By nmbrthirteen
How to Get It
claude plugins install nmbrthirteen/podcli
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Help me generate short vertical video clips from long podcast episodes for social media posting
Trust Signals Auto-scanned
Community Pulse Emerging
Discussed on Hacker News
- Show HN: Open-source MCP server for AI podcast clipping — Hacker News · 1 pts
1 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 77 GitHub stars; contributors unknown; last commit 0d ago; license AGPL-3.0.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
- License (AGPL-3.0) may restrict commercial use. Check with your legal team.
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Evaluation
Scored from trust signals (evidence-eval-v1): 77 GitHub stars; contributors unknown; last commit 0d ago; license AGPL-3.0.