--- name: podcast-pipeline description: Audio file → full publishing kit pipeline. From a single .mp3 or .wav per episode, generates Whisper-large-v3 transcript with word-level timestamps, automatic chapter detection (audio scene change + topic shift), two show-notes lengths (scannable summary + long-form SEO), a 600-1000 word blog post with JSON-LD PodcastEpisode + BlogPosting schema, social shorts (60-second vertical clips with auto-burned captions for TikTok / Reels / YouTube Shorts), quote graphics (pull 3-5 highest-engagement quotes for Instagram), an episode-page HTML template ready to drop into the user's CMS, and an X/LinkedIn/Threads thread draft. Pipeline closes the 4-hour-per-episode publishing tax that 2026 podcasters describe as the #1 time sink. TRIGGER on "podcast workflow", "podcast publishing", "show notes", "podcast transcript", "podcast SEO", "podcast clips", "podcast repurposing", "Whisper transcript", "podcast chapters", "podcast to blog", "audiogram", "podcast to social". version: "1.0.0" category: analysis platforms: - CLAUDE_CODE --- # Podcast Publishing Pipeline You convert one raw audio file into a complete publishing kit: transcript, chapters, show notes (two formats), SEO blog post, social shorts, quote graphics, and social-thread drafts. Modern podcast SEO requires a per-episode webpage with full transcript + JSON-LD — without it, episodes are invisible to Google and AI Overviews. ============================================================ === PRE-FLIGHT === ============================================================ Verify: - [ ] **Audio file accessible** (mp3, wav, m4a, flac). Local file or accessible URL. - [ ] **Episode metadata**: number, title, guest(s), publish date, primary topics. - [ ] **Transcription engine**: Whisper-large-v3 (open source, run locally with GPU/MLX), Deepgram Nova-3 (API, fastest), AssemblyAI Universal-2, OpenAI Whisper API, or Replicate. - [ ] **Brand kit**: logo, color palette, hosts' photos (for quote graphics + audiogram). - [ ] **CMS / publish target**: WordPress, Ghost, Squarespace, Substack, custom static site. - [ ] **Episode duration**: ≤ 90 min runs end-to-end in ~10 min on a Whisper-large GPU; 3+ hours needs chunking. Recovery: - If audio quality is poor (heavy background noise), pre-process with `ffmpeg` afftdn / RNNoise before transcription. - If no GPU available, route to Deepgram or OpenAI Whisper API and surface the per-minute cost. - For multi-host episodes, prefer Whisper-large + pyannote.audio diarization to get speaker labels right. ============================================================ === PHASE 1: TRANSCRIPTION === ============================================================ Generate transcript with word-level timestamps and speaker labels. **Whisper-large-v3** (preferred, local): ```python import whisperx # whisperx adds word-level alignment + diarization model = whisperx.load_model("large-v3", device="cuda", compute_type="float16") audio = whisperx.load_audio(episode_path) result = model.transcribe(audio, batch_size=16, language="en") # Word-level alignment align_model, metadata = whisperx.load_align_model(language_code=result["language"], device="cuda") result = whisperx.align(result["segments"], align_model, metadata, audio, "cuda") # Diarization (pyannote.audio) diarize_model = whisperx.DiarizationPipeline(use_auth_token=HF_TOKEN, device="cuda") diarize_segments = diarize_model(audio, num_speakers=NUM_SPEAKERS) result = whisperx.assign_word_speakers(diarize_segments, result) ``` Persist as `transcript.json` with schema: ```json { "segments": [ {"start": 0.0, "end": 4.2, "speaker": "SPEAKER_00", "text": "Welcome to the show.", "words": [...]}, ... ], "metadata": {"language": "en", "duration_s": 3245.6} } ``` Also export `transcript.srt` (SubRip) and `transcript.vtt` (WebVTT) for video editing / web players. VALIDATION: Transcript word count is non-trivial (≥ duration_min × 100, since typical speech is 130-150 wpm). Speakers labeled if > 1 voice in audio. ============================================================ === PHASE 2: CHAPTER DETECTION === ============================================================ Generate chapter markers in three ways and merge: 1. **Acoustic scene change**: detect ≥ 2s silence + speaker change. 2. **Topic shift**: embed each 60-second window with `all-MiniLM-L6-v2` or `text-embedding-3-small`; cosine-distance peaks = chapter boundary. 3. **LLM pass**: ask Claude / Gemini to read the transcript and propose 5-10 chapter titles with timestamps. Reconcile the three into 5-12 chapters. Each chapter: ``` 00:00 — Cold open 01:23 — Introducing today's guest 04:15 — How {topic} broke open 12:47 — The {key insight} ... ``` Embed as ID3 chapter markers in the mp3 file (for podcast players that support chapters: Apple Podcasts, Overcast, Pocket Casts, Spotify). VALIDATION: Chapter count 5-12. First chapter starts at 00:00. Title ≤ 60 chars each. ============================================================ === PHASE 3: SHOW NOTES — TWO FORMATS === ============================================================ **Format A — Scannable** (≤ 400 words, for podcast player descriptions and listen-page above-fold): ```markdown {Episode title} {Guest}, {their title at their company}, joins us to discuss {three sentence hook}. In this episode, we cover: - {Beat 1 — verb-led} - {Beat 2} - {Beat 3} - {Beat 4} - {Beat 5} ## Resources mentioned - {Book / article / tool} - {...} ## Find {guest} - {Twitter / X} - {LinkedIn} - {Their company / project} ## Chapters {from Phase 2} ``` **Format B — Long-form SEO** (1500-2500 words for the episode webpage): - Hook lead (60-80 words answering the episode's core question — AI Overview target). - Full chapter-by-chapter summary with timestamp anchors. - Quotes block: 3-5 verbatim quotes with attribution. - Full transcript appended (or linked). - "Listen to {episode title}" outbound links to Spotify, Apple Podcasts, Overcast, YouTube. - Author byline + episode date (E-E-A-T signal). VALIDATION: Format A respects 400-word cap. Format B has lead in first 80 words + full chapter coverage. ============================================================ === PHASE 4: SEO BLOG POST === ============================================================ Generate `blog_post.md` (~600-1000 words) — a derivative article driving organic traffic. Structure: 1. **H1** — keyword-optimized title (different from episode title; targets a Google query). 2. **TL;DR** — 50-word answer to the H1 (AEO target). 3. **Background** — 1-2 paragraphs framing the topic. 4. **Key insights from the episode** — 3-5 H2 sections, each anchored to a chapter. 5. **Quote callouts** — 2-3 pull-quotes inline. 6. **Listen to the full episode** — embed player + listen-on-platform links. Plus `blog_jsonld.json`: ```json { "@context": "https://schema.org", "@type": "BlogPosting", "headline": "...", "datePublished": "...", "author": { "@type": "Person", "name": "..." }, "image": "...", "mainEntityOfPage": "...", "associatedMedia": { "@type": "PodcastEpisode", "name": "...", "url": "...", "associatedMedia": { "@type": "MediaObject", "contentUrl": "{mp3 url}" }, "partOfSeries": { "@type": "PodcastSeries", "name": "...", "url": "..." } } } ``` VALIDATION: JSON-LD validates via Rich Results Test. Blog targets a different query than episode title. ============================================================ === PHASE 5: SOCIAL SHORTS (60-SECOND VERTICAL CLIPS) === ============================================================ For each chapter or quote, generate a short: 1. **Pick the 3-5 highest-engagement moments** — heuristic: longest applause/laugh pattern, sharpest answer to a question, or a quote-shaped sentence the speaker leaned in on. 2. **Trim to ≤ 60s** via `ffmpeg -ss start -to end`. 3. **Re-encode vertical** (9:16, 1080×1920) with auto-crop centered on speaker or static brand-blur background. 4. **Burn captions** from word-level transcript (highlighted-word style, ~3-4 words at a time, branded color). 5. **Add intro card** (1.5s with episode title + handle) and outro card (1.5s "Full episode → {link}"). 6. **Export** as `shorts/short_{n}.mp4` ready for direct upload. Generate captions per platform: - TikTok: caption + 3-5 hashtags + handle. - Instagram Reels: caption + hashtags. - YouTube Shorts: caption + #Shorts tag. VALIDATION: Each short is ≤ 60s. Captions burned and word-synced. Vertical aspect ratio confirmed. ============================================================ === PHASE 6: QUOTE GRAPHICS === ============================================================ For each of the 3-5 best quotes, generate a 1080×1080 image (Instagram-grid friendly): - Background: brand color or photo blur. - Foreground: large quote text (40-60pt), attribution ("— {guest}, {episode N}"), small show logo. - Output PNG via `Pillow` or `playwright` + HTML template + screenshot. Each graphic also gets a paired caption file with one-tap social copy. VALIDATION: Quote text is verbatim from transcript. Attribution correct. Image dimensions exact. ============================================================ === PHASE 7: SOCIAL THREAD DRAFTS === ============================================================ Generate X/LinkedIn/Threads multi-post drafts: - **Post 1 (hook)**: provocative claim from episode + episode link. - **Posts 2-6 (insights)**: one beat per post, ≤ 280 chars X / ~1200 chars LinkedIn. - **Post 7 (CTA)**: "Listen to the full episode → {link}". For LinkedIn, longer single-post format (~1500 chars) is more native than multi-post threads — generate both. VALIDATION: Character limits respected per platform. ============================================================ === PHASE 8: PACKAGE & PUBLISH === ============================================================ Final delivery: ``` episode-{slug}/ ├── README.md # where each file goes ├── transcript.json ├── transcript.srt ├── transcript.vtt ├── chapters.txt ├── show_notes_short.md ├── show_notes_long.md ├── blog_post.md ├── blog_jsonld.json ├── episode_page.html # ready to drop into CMS ├── shorts/ │ ├── short_1.mp4 (with caption.txt + hashtags.txt) │ └── ... ├── graphics/ │ ├── quote_1.png (with caption.txt) │ └── ... └── socials/ ├── x_thread.md ├── linkedin_post.md └── threads_post.md ``` VALIDATION: Every artifact present. README explains where each goes per CMS (WordPress media library, Ghost post HTML body, Substack import). ============================================================ === SELF-REVIEW === ============================================================ Score 1–5: - **Complete**: All 8 phases produced artifacts? - **Robust**: Long episodes chunked correctly? Diarization assigned speakers? - **Clean**: Transcript word count plausible? Chapters at sensible boundaries? - **Publishing-credible**: Would a podcast producer running 100+ episodes accept this output as "drop-in ready"? Common gap: trimming shorts at awkward mid-sentence boundaries. Verify start/end snap to sentence boundaries from word-level timestamps. ============================================================ === LEARNINGS CAPTURE === ============================================================ Append to `~/.claude/skills/podcast-pipeline/LEARNINGS.md`: ## — - **What worked:** - **What was awkward:** - **Suggested patch:** - **Verdict:** [Smooth / Minor friction / Major friction] ============================================================ === STRICT RULES === ============================================================ - Never publish without a transcript. Episodes without transcripts are invisible to Google + AI search and inaccessible to deaf/HoH listeners. - Never auto-generate quotes that misattribute. Always pull verbatim with speaker label from diarization. - Never trim shorts mid-word or mid-sentence. Snap to word boundaries from Whisper alignment. - Always include JSON-LD PodcastEpisode + BlogPosting. Schema is load-bearing for AI Overview citations. - Always upload the audio's first 10 seconds intact (player previews; bad opening = drop-off).