🍽️ Feed Diet

Audit your information diet and get a gorgeous report showing what you actually consume.

Trigger

Activate when the user mentions any of:

• "feed diet"
• "information diet"
• "audit my feeds"
• "what am I reading"
• "analyze my HN"
• "reading habits"
• "content diet"
• "feed report"

Instructions

Audit Mode (default)

1. Determine the data source. Ask the user for one of:

- A Hacker News username (e.g., "tosh")

- An OPML file path containing RSS feed subscriptions

1. Fetch the content. Run the appropriate fetch script:

```bash

# For HN:

bash "$SKILL_DIR/scripts/hn-fetch.sh" USERNAME 100

# For OPML:

bash "$SKILL_DIR/scripts/opml-parse.sh" /path/to/feeds.opml

```

1. Classify items. Pipe the fetched items through the classifier:

```bash

cat items.jsonl | bash "$SKILL_DIR/scripts/classify.sh" > classified.jsonl

```

The classifier uses LLM (if ANTHROPIC_API_KEY or OPENAI_API_KEY is set) or falls back to keyword matching.

1. Generate the report. Run the main entry point:

```bash

bash "$SKILL_DIR/scripts/feed-diet.sh" audit --hn USERNAME --limit 100

```

1. Present the report to the user. The output is Markdown — render it directly.

Digest Mode (weekly curated reading)

When the user wants a filtered reading list based on their goals:

bash "$SKILL_DIR/scripts/feed-diet.sh" digest --hn USERNAME --goal "systems programming, distributed systems" --days 7

Quick Reference

| Command | Description |

|---------|-------------|

| feed-diet audit --hn USER | Full diet audit for an HN user |

| feed-diet audit --opml FILE | Full diet audit from RSS feeds |

| feed-diet digest --hn USER --goal "X" | Weekly digest filtered by goals |

Notes for the Agent

• Be conversational. After presenting the report, offer observations like "Looks like you're heavy on news — want me to suggest some deeper technical feeds?"
• Suggest the digest mode if the user seems interested in filtering their reading.
• The report is the star. Don't summarize it — present it in full. It's designed to be screenshot-worthy.
• If classification seems off, mention that setting an LLM API key improves accuracy.

Discord v2 Delivery Mode (OpenClaw v2026.2.14+)

When the conversation is happening in a Discord channel:

• Send a compact first summary (top category, diversity score, top 2 recommendations), then ask if the user wants the full report.
• Keep the first response under ~1200 characters and avoid wide category tables in the first message.
• If Discord components are available, include quick actions:

- Show Full Diet Report

- Generate Weekly Digest

- Show Recommendations

• If components are not available, provide the same follow-ups as a numbered list.
• Prefer short follow-up chunks (<=15 lines per message) when sharing long reports.

References

• scripts/feed-diet.sh — Main entry point
• scripts/hn-fetch.sh — Hacker News story fetcher
• scripts/opml-parse.sh — OPML/RSS feed parser
• scripts/classify.sh — Batch content classifier (LLM + fallback)
• scripts/common.sh — Shared utilities and formatting

Examples

Example 1: HN Audit

User: "Audit my HN reading diet — my username is tosh"

Agent runs:

bash "$SKILL_DIR/scripts/feed-diet.sh" audit --hn tosh --limit 50

Output: A full Markdown report with category breakdown table, top categories with sample items, surprising finds, and recommendations.

Example 2: Weekly Digest

User: "Give me a digest of what's relevant to my work on compilers and programming languages"

Agent runs:

bash "$SKILL_DIR/scripts/feed-diet.sh" digest --hn tosh --goal "compilers, programming languages, parsers" --days 7

Output: A curated reading list of 10-20 items ranked by relevance to the user's goals.

Example 3: RSS Feed Audit

User: "Here's my OPML file, tell me what my feed diet looks like"

Agent runs:

bash "$SKILL_DIR/scripts/feed-diet.sh" audit --opml /path/to/feeds.opml