ai-marketing-skills/podcast-ops/README.md

AI Podcast Ops

One podcast episode in, 15-20 content pieces out. Scored, deduplicated, and scheduled.

Most podcast teams publish an episode and maybe pull one audiogram. This pipeline treats every episode as a content mine — extracting narrative arcs, quotable moments, controversial takes, data points, and stories, then generating platform-native content for every channel with viral scoring and deduplication.

What's Inside

🎙️ Podcast-to-Everything Pipeline (podcast_pipeline.py)

End-to-end pipeline that ingests podcast episodes (via RSS feed or raw transcript) and produces a full cross-platform content calendar.

Ingest modes:

• RSS feed → auto-download + Whisper transcription
• Raw transcript file (text, SRT, VTT)
• Batch mode: process last N episodes from a feed

Content generated per episode:

• 3-5 short-form video clip suggestions (with timestamps + hooks)
• 2-3 Twitter/X thread outlines
• 1 LinkedIn article draft
• 1 newsletter section
• 3-5 quote cards (text overlays for social)
• 1 blog post outline with SEO keywords
• 1 YouTube Shorts/TikTok script

Intelligence layer:

• Editorial Brain: LLM-powered extraction of 7 content atom types
• Viral scoring: Novelty × Controversy × Utility (0-100)
• Dedup engine: semantic similarity check against last N days of output
• Calendar generator: auto-schedules by platform best practices

📋 SKILL.md

Claude Code skill file. Drop into your project and ask: "Turn this podcast episode into a content calendar" — it handles the rest.

Quick Start

# 1. Install dependencies
pip install -r requirements.txt

# 2. Set up environment
cp .env.example .env
# Edit .env with your API keys (OPENAI_API_KEY, ANTHROPIC_API_KEY)

# 3. Process latest episode from your podcast RSS
python podcast_pipeline.py --rss "https://feeds.example.com/podcast.xml"

# 4. Or process a local transcript
python podcast_pipeline.py --transcript episode-42.txt

# 5. Batch process last 5 episodes
python podcast_pipeline.py --batch "https://feeds.example.com/podcast.xml" --episodes 5

# 6. Generate weekly content calendar
python podcast_pipeline.py --calendar

# 7. Only keep high-scoring content
python podcast_pipeline.py --rss "https://feeds.example.com/podcast.xml" --min-score 80

Configuration

Environment Variables

Variable	Required	Description
OPENAI_API_KEY	Yes	OpenAI API key (Whisper transcription)
ANTHROPIC_API_KEY	Yes	Anthropic API key (content generation)
OPENAI_LLM_KEY	Optional	Separate OpenAI key for GPT-based generation

CLI Options

Flag	Description	Default
--rss <url>	Process latest episode from RSS feed	—
--transcript <file>	Process a local transcript file	—
--batch <url>	Batch process from RSS feed	—
--episodes <n>	Number of episodes for batch mode	5
--calendar	Generate weekly calendar from outputs	—
--dedup-days <n>	Days of history for dedup check	30
--min-score <n>	Minimum viral score to include	0
--output-dir <path>	Output directory	./output

Output Structure

output/
├── episodes/
│   ├── 2024-01-15-episode-title/
│   │   ├── transcript.txt         # Clean transcript
│   │   ├── atoms.json             # Extracted content atoms
│   │   ├── content_pieces.json    # All generated content
│   │   └── calendar.json          # Scheduled calendar
│   └── ...
├── calendar/
│   └── week-2024-W03.json        # Aggregated weekly calendar
├── content_history.json           # Dedup tracking (hashes + embeddings)
└── pipeline_log.json              # Run history and performance stats

How It Works

RSS Feed / Transcript
        │
        ▼
┌─────────────────┐
│  1. INGEST       │  Download audio → Whisper → clean transcript
│                  │  OR read transcript file directly
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  2. EXTRACT      │  Editorial Brain: find narrative arcs, quotes,
│                  │  controversial takes, data points, stories,
│                  │  frameworks, predictions
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  3. GENERATE     │  For each atom → platform-native content:
│                  │  clips, threads, articles, newsletter,
│                  │  quote cards, blog outlines, short scripts
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  4. SCORE        │  Viral potential: novelty × controversy × utility
│                  │  Filter below threshold
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  5. DEDUP        │  Semantic similarity vs last N days
│                  │  Remove overlaps, flag near-dupes
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  6. SCHEDULE     │  Calendar generation with platform-specific
│                  │  timing rules and content mix optimization
└─────────────────┘

Viral Scoring

Every generated piece is scored on three dimensions:

Dimension	Weight	What It Measures
Novelty	40%	Is this new or surprising?
Controversy	30%	Will people argue about this?
Utility	30%	Can someone use this immediately?

Thresholds: 80+ = priority publish, 60-79 = solid fill, 40-59 = gap filler, <40 = cut

Integration with Other Skills

• Content Ops / Expert Panel — Run generated content through the expert panel for quality gating before publish
• SEO Ops — Feed blog outlines to the SEO pipeline for keyword validation
• Outbound Engine — Use podcast insights as personalization hooks in outbound sequences
• Growth Engine — A/B test different content formats from the same episode atoms