Podcast Pipeline

What it does

When your agent uses it

• Asked to: repurpose this podcast
• Turn this episode into content
• Podcast content calendar
• Extract clips from this episode

Example prompts

• “repurpose this podcast”
• “turn this episode into content”
• “podcast content calendar”
• “/podcast-pipeline”

Requirements

• Python 3
• A credential in OPENAI_API_KEY
• A credential in ANTHROPIC_API_KEY

Workflow steps

7 steps, taken from the step headings in SKILL.md.

1. 1Ingest — Get the Transcript
2. 2Editorial Brain — Deep Analysis
3. 3Content Generation — One Episode, Many Pieces
4. 4Content Scoring — Viral Potential
5. 5Dedup Engine
6. 6Calendar Generation (--calendar)
7. 7Output

What it can do on your machine

Read from SKILL.md and the folder at commit 8088e1a. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships script files (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • python
  • python3

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • OPENAI_API_KEY
  • ANTHROPIC_API_KEY
  • OPENAI_LLM_KEY

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Podcast Pipeline loads about 2.6k tokens when it runs. Until then it costs about 181 tokens; SKILL.md has 745 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~181

When it runs · the whole SKILL.md, loaded when a task matches

~2.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ericosiu/ai-marketing-skills at commit 8088e1a, republished under its MIT licence (© ericosiu). 745 words, ~2,649 tokens.

name

podcast-pipeline

description

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 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

- 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]

- 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]

- 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]

- 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]

- 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]

- 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]

- 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]

- 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]

{
  "week_of": "2024-01-15",
  "episode_source": "Episode Title - Guest Name",
  "content_pieces": [
    {
      "date": "2024-01-15",
      "time": "09:00 ET",
      "platform": "twitter",
      "type": "thread",
      "content": "...",
      "viral_score": 85,
      "status": "draft"
    }
  ],
  "total_pieces": 18,
  "avg_viral_score": 72,
  "coverage": {
    "twitter": 6,
    "linkedin": 3,
    "youtube_shorts": 3,
    "newsletter": 1,
    "blog": 1,
    "quote_cards": 4
  }
}

output/
├── episodes/
│   ├── YYYY-MM-DD-episode-slug/
│   │   ├── transcript.txt
│   │   ├── atoms.json          # Extracted content atoms
│   │   ├── content_pieces.json # All generated content
│   │   └── calendar.json       # Scheduled calendar
│   └── ...
├── calendar/
│   └── week-YYYY-WNN.json     # Aggregated weekly calendar
├── content_history.json        # Dedup tracking
└── pipeline_log.json           # Run history and stats

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

# Process a local transcript
python podcast_pipeline.py --transcript episode-42.txt

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

# Generate weekly calendar from existing outputs
python podcast_pipeline.py --calendar

# Process with custom dedup window
python podcast_pipeline.py --rss "https://feeds.example.com/podcast.xml" --dedup-days 60

# Process and only keep 80+ viral score content
python podcast_pipeline.py --rss "https://feeds.example.com/podcast.xml" --min-score 80

© ericosiu, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files in podcast-ops of ericosiu/ai-marketing-skills.

• SKILL.md
• .env.example
• README.md
• podcast_pipeline.py
• requirements.txt

Open the folder on GitHubat commit 8088e1a

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in ericosiu/ai-marketing-skills, which our catalogue first saw on October 7, 2026.

• infometa/workbuddyskillsskills/podcast-ops