audio-tldr

Any video, audio, or podcast → key takeaways. Transcribed locally, cached forever.

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An agent skill — open SKILL.md standard, works in Claude Code and Codex — that turns long-form media into 3–7 key takeaways + a summary. Transcription runs locally with whisper and is cached by content hash — while a cache entry exists, the same source is not transcribed again (unless you --force). Ask for a different angle later and it re-digests from cache in seconds.

First-run transcription time depends on your hardware, model, and backend — after that, the cache answers.

Why?

Watching a 90-minute talk to extract 5 useful points is a bad trade. Sending audio to a cloud API costs money and leaks content. And summarizing the same episode twice — because the first summary had the wrong focus — means paying the transcription cost all over again.

Without audio-tldr                    With audio-tldr
──────────────────                    ───────────────
watch the whole video                 paste the URL
take notes by hand                    get takeaways + summary
"summarize it differently…"           re-digest from cache, instant
re-upload, re-transcribe, re-pay      transcribe once, reuse from cache

Features

• ✓ YouTube, podcasts, and any yt-dlp-supported URL — or local audio/video files
• ✓ Local media pipeline: download, transcription, cache all run on your machine — audio is never uploaded (see Privacy)
• ✓ Content-hash cache: re-summarizing (any angle) reuses the transcript while the entry exists
• ✓ Whisper backend auto-detection: mlx-whisper / faster-whisper / whisper.cpp / openai-whisper
• ✓ Language auto-detection; optional Simplified→Traditional Chinese conversion (OpenCC)
• ✓ Cache management built in: list, clear one, clear all, opt-in retention
• ✓ Timeline for long content (> 20 min)
• ✓ Digests saved to an output folder as Markdown or HTML — transcripts stay in the cache
• ✓ Conversational digest prompt: no request stated? The agent asks in plain text, listing the template menu
• ✓ Digest templates: meeting minutes, key summary, analysis report — or save your own reusable format
• ✓ Opt-in fully-local digest: digest_model: ollama:<model> runs Phase 2 through your own Ollama server — transcript text never leaves the machine
• ✓ Translation at the digest layer: digests in any language, or a faithful full-transcript translation
• ✓ Optional preferences file for standing habits — zero setup required
• ✓ Interpreter auto-selection: backend installed in another Python (e.g. Homebrew) is found and used automatically; --doctor diagnoses the environment
• ✓ Apple Podcasts fallback built in: when yt-dlp's extractor fails, episodes resolve via the iTunes lookup API — cache identity stays on your original link; a show link (no episode id) automatically uses the latest episode
• ✓ Opt-in frame extraction for video sources: scene-detection slide capture, or stills at exact timestamps — video fetched at ≤720p and deleted after extraction; frames share the transcript's cache entry
• ✓ Subtitle export: --format srt / --format vtt write a standard subtitle file with segment timestamps alongside the transcript, on any of the four backends
• ✓ Transcript correction: before each digest, a model proposes sound-alike fixes,