podcli

Local podcast clipper with face tracking and burned captions, driven from a CLI, a web studio, or 26 MCP tools.

AdaptableOperable ProductCLI Hybrid PythonTypeScript MCP server License: AGPL-3.0

One command transcribes an episode with whisper.cpp, scores moments, crops to the speaker, and renders captioned vertical clips on your machine. The same engine is exposed as a stdio MCP server so Claude Code or Codex can run the whole flow through to YouTube publishing.

Use it when

I host a weekly podcast and want captioned vertical clips without uploading episodes to a cloud clipper
I want Claude Code to clip, title, and publish shorts from a transcript as one conversation

Gotchas

AGPL-3.0; a commercial license is offered by email
58 stars and a young repo: thin evidence of users beyond the Builder
First run downloads Python, Node, ffmpeg, whisper.cpp, and models into a managed folder
Intel Mac support is in progress

Last activity Sep 2, 2026. Fit: adaptable 2, operable 3.

Editions

Free · Free open source

Installcurl -fsSL https://podcli.com/install.sh | sh
Clonegit clone https://github.com/nmbrthirteen/podcli

Commercial license · Free closed source

No distribution recorded.

For agents: JSON · Markdown

Adaptable: what is yours to change

Make it yours

Config only15 things to edit

Copy .env.example to .env: PODCLI_HOME for knowledge, presets, and assets; PODCLI_OUTPUT for renders; PODCLI_QUALITY; HF_TOKEN for diarization; FFMPEG_PATH overrides. Presets and config profiles are documented in docs/configuration.md. The PodStack slash commands in .claude/commands/ are editable prompts for the content workflow.

Env vars: PODCLI_HOME PODCLI_OUTPUT PODCLI_QUALITY PODCLI_PORT PODCLI_LOG_LEVEL PODCLI_PYTHON PODCLI_TRANSITION_AUTOFIX_PASSES HF_TOKEN FFMPEG_PATH FFPROBE_PATH
Config files: .env docs/configuration.md (config profiles) presets
Prompt files: .claude/commands/ CLAUDE.md
Accounts you need: HuggingFace (diarization token)YouTube (publishing)AssemblyAI (optional)

Source

github.com/nmbrthirteen/podcli
AGPL-3.0 license
Last commit Sep 2, 2026
58 stars

Operable: what an agent can drive

Interface

NativeMCP serverpublished by the builderlocal

How an agent can drive an Operable Listing, in five levels: native (built for the agent as the primary interface), official (the Builder ships an agent interface), wrapped (a third party wraps the software's API), config (an agent can only drive it by editing files), gui (screen control only). An interface is official only when it is published by the same organization that ships the software. GUI-level Listings are allowed and sorted last.

Docs: podcli.com/docs/mcp-server
Tools: transcribe_podcast suggest_clips create_clip batch_create_clips manage_reel manage_presets manage_thumbnail_config manage_integrations export_to_davinci_resolve knowledge_base

Reach

Partial coverage. 9 of 10 jobs reachable through the stated interface.

Job	Through the interface	Evidence
Transcribe an episode with diarization	Reachable	MCP tools transcribe_podcast, transcribe_start, import_transcript
Pick the moments worth clipping	Reachable	MCP tools suggest_clips, analyze_energy; AI scoring against the knowledge base
Render clips with face tracking and captions	Reachable	MCP tools create_clip, batch_create_clips, modify_clip
Choose caption style and aspect ratio	Reachable	MCP tools manage_presets, update_settings; four caption styles
Build highlight reels	Reachable	MCP tool manage_reel
Generate thumbnails	Reachable	MCP tool manage_thumbnail_config
Publish to YouTube	Reachable	README: 26 MCP tools let an agent transcribe, score, render, and publish; manage_integrations
Read performance analytics	Undetermined	README lists analytics in the studio; no MCP tool named for it in the docs list
Export to DaVinci Resolve	Reachable	MCP tool export_to_davinci_resolve (FCPXML)
Keep titles and captions in the show's voice	Reachable	MCP tool knowledge_base; clip_history stops resuggesting published moments

Code stack

Languages: PythonTypeScript
Frameworks: whisper.cppffmpegRemotionpyannote
Package manager: npm
Database: none
Hosting target: macOS (Apple Silicon)LinuxWindows
Built: Unknown (recorded, never ranked)

Model stack

Providers: local whisper.cppAssemblyAI (optional)Claude or Codex CLI (optional scoring)HuggingFace pyannote (diarization)
Models: Whisper
Keys you need: HF_TOKEN
Prompts live in: .claude/commands/ CLAUDE.md

Evidence and confidence

Field	Where it was seen	Confidence
formFactor	README: podcli process episode.mp4; installer fetches a self-contained binary	95%
executionModel	README: transcription and rendering local; optional Claude or Codex calls for AI clip scoring	85%
operability	README: 'Agent-native. 26 MCP tools'; podcli mcp install; tool list from podcli.com/llms.txt	90%
effort	.env.example with named PODCLI_* variables; docs/configuration.md referenced	85%
editions	README: free and open source under AGPL-3.0; commercial license by email	90%
active	GitHub pushedAt 2026-09-02	–
reach		80%

Open questions

Real-software gate is met on stars alone (58); look for issues, forum threads, or the X demo's reach before verifying
Whether analytics is reachable through manage_integrations

Sources

podcli.com sourcing:video-streaming:github
github.com/nmbrthirteen/podcli sourcing:video-streaming:github
podcli.com/llms.txt analysis