Podcast-to-Everything content pipeline. Takes a podcast RSS feed or raw transcript and generates a full cross-platform content calendar: short-form video clips, Twitter/X threads, LinkedIn articles, newsletter sections, quote cards, blog outlines with SEO keywords, and YouTube Shorts/TikTok scripts. Scores each piece by viral potential (novelty × controversy × utility) and deduplicates against recent output. Use when asked to: "repurpose this podcast", "turn this episode into content", "podcast content calendar", "extract clips from this episode", "podcast to social", "content from RSS feed", "batch process episodes", or any request to turn podcast/audio content into a multi-platform content plan.
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Podcast-to-Everything content pipeline. Takes a podcast RSS feed or raw transcript and generates a full cross-platform content calendar: short-form video clips, Twitter/X threads, LinkedIn articles, newsletter sections, quote cards, blog outlines with SEO keywords, and YouTube Shorts/TikTok scripts. Scores each piece by viral potential (novelty × controversy × utility) and deduplicates against recent output. Use when asked to: "repurpose this podcast", "turn this episode into content", "podcast content calendar", "extract clips from this episode", "podcast to social", "content from RSS feed", "batch process episodes", or any request to turn podcast/audio content into a multi-platform content plan.
version
1.0.1
display_name
podcast-ops
display_name_en
Podcast Ops
description_zh
播客内容拆解流水线，一期节目生成 20+ 跨平台内容
description_en
Podcast content repurposing pipeline, one episode to 20+ cross-platform pieces
visibility
public
Preamble (runs on skill start)
# Version check (silent if up to date)
python3 telemetry/version_check.py 2>/dev/null || true# Telemetry opt-in (first run only, then remembers your choice)
python3 telemetry/telemetry_init.py 2>/dev/null || true
Privacy: This skill logs usage locally to ~/.ai-marketing-skills/analytics/. Remote telemetry is opt-in only. No code, file paths, or repo content is ever collected. See telemetry/README.md.
Podcast-to-Everything Pipeline
Turns podcast episodes into a full content calendar across every platform.
One episode in, 15-20 content pieces out — scored, deduplicated, and scheduled.
Step 1: Ingest — Get the Transcript
Determine the input source and obtain a clean transcript.
Option A: RSS Feed (--rss <url>)
Fetch the RSS feed XML
Extract the latest episode's audio URL (or use --episodes N for batch)
Download the audio file
Transcribe via OpenAI Whisper API (with timestamps)
Store transcript with episode metadata (title, date, description, duration)
Option B: Raw Transcript (--transcript <file>)
Read the transcript file (plain text, SRT, or VTT)
Parse timestamps if present
Extract episode metadata from filename or prompt user
Remove filler words (um, uh, like, you know) for written content
Preserve original with timestamps for video clip suggestions
Split into logical segments by topic shift
Step 2: Editorial Brain — Deep Analysis
Feed the full transcript to the LLM with this extraction framework:
Extract these content atoms:
Narrative Arcs — Complete story segments with setup → tension → resolution.
Tag with start/end timestamps.
Quotable Moments — Punchy, shareable statements. One-liners that stand alone.
Must pass the "would someone screenshot this?" test.
Controversial Takes — Opinions that go against conventional wisdom.
The stuff that makes people reply "hard disagree" or "finally someone said it."
Data Points — Specific numbers, percentages, dollar amounts, timeframes.
Concrete proof points that add credibility.
Stories — Personal anecdotes, case studies, client examples.
Must have a character, a problem, and an outcome.
Frameworks — Step-by-step processes, mental models, decision matrices.
Anything structured that people would save or bookmark.
Predictions — Forward-looking claims about trends, markets, technology.
Hot takes about where things are going.
Output format per atom:
- Type: [narrative_arc | quote | controversial_take | data_point | story | framework | prediction]
- Content: [extracted text]
- Timestamp: [start - end, if available]
- Context: [what was being discussed]
- Viral Score: [0-100, see Step 4]
- Suggested platforms: [where this atom works best]
Step 3: Content Generation — One Episode, Many Pieces
For each episode, generate ALL of these from the extracted atoms:
3a. Short-Form Video Clips (3-5 per episode)
- Hook: [First 3 seconds — pattern interrupt or bold claim]
- Clip segment: [Timestamp range from transcript]
- Caption overlay: [Text for the screen]
- Platform: [YouTube Shorts / TikTok / Instagram Reels]
- Why it works: [What makes this clippable]
Prioritize: controversial takes > stories with payoffs > surprising data points
3b. Twitter/X Threads (2-3 per episode)
- Thread hook (tweet 1): [Curiosity gap or bold opener]
- Thread body (5-10 tweets): [Each tweet is one complete thought]
- Thread closer: [CTA — follow, reply, retweet trigger]
- Source atoms: [Which content atoms feed this thread]
Rules: No tweet over 280 chars. Each tweet must stand alone. Use data points as proof.
3c. LinkedIn Article Draft (1 per episode)
- Headline: [Specific, benefit-driven]
- Hook paragraph: [Before the "see more" fold — must earn the click]
- Body: [3-5 sections with headers, 800-1200 words]
- CTA: [Engagement driver — question, not link]
- Hashtags: [3-5 relevant, not spammy]
Voice: Professional but not corporate. First-person. Story-driven.
3d. Newsletter Section (1 per episode)
- Section headline: [Scannable, specific]
- TL;DR: [One sentence, the core insight]
- Body: [3-5 bullet points, each with a takeaway]
- Pull quote: [The most shareable line from the episode]
- Link: [Back to full episode]
3e. Quote Cards (3-5 per episode)
- Quote text: [Max 20 words — must work as text overlay]
- Attribution: [Speaker name]
- Background suggestion: [Color/mood that matches the tone]
- Platform sizing: [1080x1080 for IG, 1200x675 for Twitter, 1080x1920 for Stories]
3f. Blog Post Outline (1 per episode)
- Title: [SEO-optimized, includes primary keyword]
- Primary keyword: [Search volume + difficulty estimate]
- Secondary keywords: [3-5 related terms]
- Meta description: [155 chars max]
- H2 sections: [5-7, each maps to a content atom]
- Internal linking opportunities: [Topics that connect to existing content]
- Estimated word count: [1500-2500]
3g. YouTube Shorts / TikTok Script (1 per episode)
- HOOK (0-3s): [Pattern interrupt — question, bold claim, or visual]
- SETUP (3-15s): [Context — why should they care]
- PAYOFF (15-45s): [The insight, data, or story resolution]
- CTA (45-60s): [Follow, comment prompt, or part 2 tease]
- On-screen text: [Key phrases to overlay]
- B-roll suggestions: [Visual ideas if not talking-head]
Step 4: Content Scoring — Viral Potential
Score every generated piece on three dimensions (each 0-100):
