podcli
podcli is an AI-powered podcast clipping server that turns long-form video/audio into short-form, upload-ready clips. Here's what you can do:
Transcription & Input
Transcribe audio/video using Whisper AI with speaker detection (sync or async background jobs with progress polling)
Import external transcripts with word-level timestamps or parse raw speaker-labeled text
Set a working video file without transcribing
Clip Suggestion & Management
Submit AI-powered viral clip suggestions (with scoring and reasoning) to the Web UI for review
Analyze audio energy levels to find high-energy moments
Modify clip timing, title, or caption style; delete or toggle clips for batch export
Rendering & Export
Render single or batch clips as 9:16 vertical shorts (or 16:9, 1:1) with burned-in captions (4 styles), face/speaker tracking, and normalized audio
Batch export supports async rendering with progress polling
Export clips as DaVinci Resolve FCPXML projects with ProRes alpha caption overlays
List all rendered output files
Knowledge Base & Branding
Read/write/delete
.mdknowledge files to teach the AI your show's brand, voice, and styleManage reusable assets (logos, outros) by name
Save/load named rendering presets (caption style, crop, logo, outro)
Manage thumbnail template configuration (show/export/import/reset)
History & Duplicates
View past clips and check for duplicates before creating new ones
Session & Workflow
Read current session state (video, transcript, suggestions, settings) with next-step guidance
Update global rendering settings
System & Integrations
Manage portable config profiles (export/import bundles, migrate legacy paths)
Set/unset environment variables (HuggingFace token, AI CLI paths)
Enable/disable integrations (e.g., DaVinci Resolve exporter)
Check availability of Claude Code / Codex for AI-powered features
Provides tools for generating upload-ready YouTube Shorts, including titles, descriptions, and thumbnails, as well as a publish optimization checklist for YouTube.
Integrates with YouTube Studio to retrieve performance analytics and enable retrospective analysis of published episodes via slash commands.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@podcliprocess episode.mp4 and generate clips"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
podcli process episode.mp4That one command transcribes the episode, picks the moments worth clipping, crops to whoever is speaking, and burns the captions in. Transcription and rendering run on your machine. The only network calls are the optional Claude or Codex requests when you use AI clip scoring.
Install
No prerequisites. The installer fetches a self-contained binary, and the first run provisions Python, Node, FFmpeg, whisper.cpp, and the models it needs into a managed folder.
macOS and Linux
curl -fsSL https://podcli.com/install.sh | shWindows (PowerShell)
irm https://podcli.com/install.ps1 | iexRuns on macOS (Apple Silicon), Linux (x64 and arm64), and Windows (x64). Intel Mac support is in progress.
Related MCP server: CrabCut
Quick start
podcli # interactive menu, opens the web studio
podcli process episode.mp4 # transcribe, pick moments, render clipsClips land in podcli-clips/ in the directory you ran it from, so each show keeps its own renders. Everything else (knowledge, presets, assets, clip history, cache) lives in one managed folder that follows you between directories. Set PODCLI_OUTPUT to render somewhere fixed instead.
What you get
Clips
9:16, 16:9, or 1:1, with captions sized for each canvas
Face tracking that follows the speaker, split-screen layouts included
Multi-segment cuts that drop filler, long pauses, and tangents
Four caption styles: branded, hormozi, karaoke, subtle
Logos, intros, outros, and background music from a reusable asset library
Loudness-normalized audio and hardware encoding on VideoToolbox, NVENC, and VAAPI, with a CPU fallback
Finding the moments
Whisper transcription with speaker diarization, or bring your own transcript as
.txt,.srt, or.vttAssemblyAI as an alternative engine, and yt-dlp to pull an episode straight from a URL
AI scoring against your knowledge base, checked against your episode database so it stops resuggesting moments you already published
Audio energy and laughter detection to build highlight reels
The studio at localhost:3847
Library, episode workspace, per-clip detail, highlights, thumbnails, content, analytics, assets, knowledge, config, integrations, and MCP setup
⌘Kcommand palette across pages, clips, and assetsTitles, descriptions, tags, and hashtags, with any section regenerated on your own guidance
Thumbnail studio for 16:9 and 9:16, with frame and text options
Transcript corrections that carry through to every render
Shipping it
26 MCP tools, so an agent can transcribe, score, render, and publish through conversation
YouTube publishing plus performance analytics to see which clips landed
DaVinci Resolve export as FCPXML when you want to finish by hand
Presets, clip history with duplicate detection, and a transcript cache
Why podcli
If you are weighing podcli against the cloud clippers, this is the difference:
Runs locally. Transcription and rendering happen on your machine by default, so episodes stay there. Only the optional cloud engine (AssemblyAI) and publishing to YouTube send anything out.
Free and open source under AGPL-3.0. Exports are unlimited, full quality, and watermark-free.
Agent-native. 26 MCP tools let Claude Code or Codex drive the whole flow, transcription through publishing.
A knowledge base keeps titles, captions, and descriptions in your show's voice, and stops the engine from resuggesting moments you already published.
DaVinci Resolve handoff. Export any clip as FCPXML when you want to finish the edit yourself.
Use it from your agent
podcli is an MCP server, so an agent can transcribe, suggest clips, and render them through conversation.
podcli mcp install # registers it with Claude CodeClaude Desktop and Codex setup is in the MCP docs.
Content workflow
PodStack ships with podcli as a set of Claude Code slash commands. They take a transcript to a publish-ready package: scored moments, titles, descriptions, thumbnail briefs, a brand review, and a publish checklist.
/produce-shortsThe commands live in .claude/commands/. CLAUDE.md describes each one.
Docs
Guide | What's in it |
Install, first episode, the whole flow | |
Web UI: library, episodes, content, highlights | |
Commands, flags, presets, assets | |
Agent setup and available tools | |
Styles, aspect ratios, cropping | |
Environment variables, config profiles, transcript format |
Docs are open source at nmbrthirteen/podcli-docs.
Contributing
See CONTRIBUTING.md for the dev setup and conventions, and RELEASE.md for how releases are cut.
Credits
Content workflow powered by PodStack, inspired by gstack by Garry Tan.
License
AGPL-3.0. See LICENSE.
Need podcli without AGPL terms? A commercial license is available. Email siradze@nikusha.me with a one-line description of your use case.
Maintenance
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