custom-podcast-discovery（自定义播客发现）

Podcast Discovery & Generation

Automated end-to-end podcast production pipeline. Discovers trending topics from configurable sources, researches them deeply, generates fact-checked scripts with citations, and produces audio via ElevenLabs TTS.

Triggers

Use this skill when user asks to:

• "Generate a podcast"
• "Make a podcast episode"
• "Discover podcast topics"
• "Create an audio episode about X"
• "Find topics for podcast"
• "Research and script a podcast"
• "Produce a podcast episode"

Quick Start

1. Configure

cd ~/.openclaw/skills/podcast
cp config.example.yaml config.yaml
# Edit config.yaml: add sources, interests, voice, storage

2. Discover Topics

python3 scripts/discover.py --config config.yaml --limit 10

3. Run Pipeline

python3 scripts/pipeline.py --config config.yaml --topic "Your Topic" --mode manual

Configuration

Minimal config.yaml:

sources:
  - type: rss
    url: https://aeon.co/feed.rss
    name: Aeon
  - type: hackernews
    min_points: 200

interests:
  - AI/Tech
  - Science

voice:
  voice_id: "<your-voice-id>"

storage:
  type: local
  path: ./output

Storage options:

• type: s3 — Upload to S3 (requires bucket, region)
• type: local — Save to local directory

Pipeline Stages

1. Discovery — Fetch and rank topics from sources
2. Research — Web search framework (OpenClaw worker populates)
3. Script — Generate script with LLM, enforce [Source: URL] citations
4. Verify — Cross-check claims against research sources
5. Audio — Strip citations, call ElevenLabs TTS
6. Upload — Save to S3 or local storage

Each stage can run standalone or as full pipeline.

Usage Examples

Discover only:

python3 scripts/discover.py --config config.yaml --limit 5 --output topics.json

Full pipeline (auto mode):

python3 scripts/pipeline.py --config config.yaml --mode auto

Specific topic:

python3 scripts/pipeline.py --config config.yaml --topic "AI Reasoning" --mode manual

Resume from stage:

python3 scripts/pipeline.py --config config.yaml --resume-from audio

Source Types

Built-in:

• rss — Generic RSS/Atom feed (any URL)
• hackernews — HN API with point/comment filters
• nature — Nature journal (sections: news, research, biotech, medicine)

Add custom RSS:

sources:
  - type: rss
    url: https://yourfeed.com/rss
    name: Your Source
    category: Your Category

Output Files

output/
├── discovery-YYYY-MM-DD.json      # Ranked topics
├── research-YYYY-MM-DD-slug.json  # Research data
├── script-YYYY-MM-DD-slug.txt     # Script with citations
├── verification-YYYY-MM-DD.json   # Fact-check report
├── tts-ready-YYYY-MM-DD-slug.txt  # Clean text for TTS
├── episode-YYYY-MM-DD-slug.mp3    # Final audio
└── pipeline-state-YYYY-MM-DD.json # Pipeline state

Integration with OpenClaw

For discovery: Run directly (no tools needed)

For full pipeline: Spawn OpenClaw worker with:

• web_search() — Research stage
• LLM access — Script generation (Claude Sonnet recommended)
• elevenlabs_text_to_speech — Audio generation

Worker pattern:

cd ~/.openclaw/skills/podcast
# Source environment if available
[ -f ~/.openclaw/env-init.sh ] && source ~/.openclaw/env-init.sh
python3 scripts/pipeline.py --config config.yaml --mode auto

Citation Enforcement

Every factual claim in scripts MUST have [Source: URL] citation:

✅ Correct:

The market grew to $10.2 billion in 2025 [Source: https://example.com/report].

❌ Incorrect:

The market grew significantly.

The verify script cross-references citations against research sources and blocks audio generation if unverified claims are found.

Cron Integration

Daily discovery (8 AM):

schedule: "0 8 * * *"
payload: |
  cd ~/.openclaw/skills/podcast
  python3 scripts/discover.py --config config.yaml --limit 10 \
    --output data/discovery-$(date +%Y-%m-%d).json

Weekly full pipeline:

schedule: "0 9 * * 1"
payload: |
  cd ~/.openclaw/skills/podcast
  [ -f ~/.openclaw/env-init.sh ] && source ~/.openclaw/env-init.sh
  python3 scripts/pipeline.py --config config.yaml --mode auto

Key Features

✅ Zero vendor lock-in — Use any RSS feed, any storage ✅ No external dependencies — Pure Python stdlib (except ElevenLabs for TTS) ✅ Citation enforcement — Every claim must have source ✅ Fact verification — Cross-check against research ✅ Pluggable sources — Easy to add new topic sources ✅ Resume support — Restart from any stage ✅ Manual or auto — Review each stage or run end-to-end

Troubleshooting

No topics found:

• Check RSS URLs are valid
• Verify interests match source content
• Lower min_points for Hacker News

Verification fails:

• Ensure research.json has sources
• Check script has [Source: URL] after claims
• URLs must match research sources

S3 upload fails:

• Verify AWS credentials
• Check bucket exists and region matches
• Ensure bucket policy allows public read

Files

• SKILL.md — This file
• README.md — Detailed documentation
• config.example.yaml — Configuration template
• scripts/ — Pipeline scripts
• sources/ — Source implementations
• templates/ — Prompt templates

License

MIT — Open source, community-maintained OpenClaw skill