hn-podcast-archive（hn 播客档案）

HN Podcast Archive

Set up or maintain a repeatable pipeline that:

1. reads an RSS feed,
2. detects new episodes,
3. downloads audio,
4. transcribes with local Whisper,
5. writes a markdown archive per episode,
6. updates index/state files.

Workflow

1. Read references/layout.md to understand the expected archive layout and outputs.
2. Use scripts/hn_podcast_archive.py as the primary implementation.
3. Run python3 scripts/hn_podcast_archive.py --help to inspect options.
4. For first-time setup, ensure required binaries and Python modules exist.
5. For automation, schedule the script on a recurring cadence with a stable output directory.

Required runtime dependencies

The script expects:

• ffmpeg in PATH
• whisper in PATH
• Python 3.10+
• Python package feedparser

If any dependency is missing, surface a clear setup note instead of pretending the pipeline is ready to execute.

Recommended command

python3 skills/hn-podcast-archive/scripts/hn_podcast_archive.py \
  --feed-url "https://example.com/podcast.rss" \
  --output-dir ./data/hn-podcast-archive \
  --whisper-model turbo

Output expectations

For each ingested episode, create:

• downloaded audio under audio/
• transcript under transcripts/
• markdown archive under episodes/

Keep these shared files current:

• index.md
• state.json
• run-log.jsonl

Automation guidance

For automation, prefer a cron/standing-order style trigger that runs every few hours. The script is idempotent at the episode level by tracking processed GUIDs/URLs in state.json.

Safe operating rules

• Never overwrite unrelated archive content.
• Skip already-processed episodes unless explicitly forced.
• Preserve source metadata (title, published date, audio URL, guid).
• If transcription fails after download, keep the audio and record the failure in the log/state.

Customization points

Useful flags:

• --limit N to ingest only recent items during testing
• --force to reprocess already-seen items
• --dry-run to inspect actions without writing outputs
• --whisper-model to trade speed vs accuracy

Packaging/publishing

Package the skill from its folder. Publish with ClawHub only after local validation passes and authentication is available.