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tkuehnl

feed-diet

by Todd Kuehnl · GitHub ↗ · v0.2.1
cross-platform ✓ Security Clean
625
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Install in OpenClaw
/install feed-diet
Description
Audit your information diet across HN and RSS feeds — beautiful reports with category breakdowns, ASCII charts, and personalized recommendations.
README (SKILL.md)

🍽️ 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
  2. Fetch the content. Run the appropriate fetch script:

    # For HN:
    bash "$SKILL_DIR/scripts/hn-fetch.sh" USERNAME 100
    
    # For OPML:
    bash "$SKILL_DIR/scripts/opml-parse.sh" /path/to/feeds.opml
    
  3. Classify items. Pipe the fetched items through the classifier:

    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.

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

    bash "$SKILL_DIR/scripts/feed-diet.sh" audit --hn USERNAME --limit 100
    
  5. 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 (\x3C=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
Usage Guidance
This skill is internally consistent and implements the declared purpose. Before installing, consider: (1) Required local deps: ensure you have bash (4+), curl, python3, and jq installed (the metadata omitted these). (2) Privacy: if you set ANTHROPIC_API_KEY or OPENAI_API_KEY, the skill will send batches of item titles/URLs to those LLM providers for classification — if you prefer not to transmit data, leave those env vars unset; the script will fall back to its local keyword classifier. (3) The skill will read any OPML file path you supply and will cache fetched items under $XDG_CACHE_HOME or ~/.cache/feed-diet. (4) Review the included scripts yourself if you have sensitive data or strict network/privacy requirements. Overall: coherent and appropriate for its stated purpose.
Capability Analysis
Type: OpenClaw Skill Name: feed-diet Version: 0.2.1 The OpenClaw Feed Diet skill is classified as benign. While the `SKILL.md` instructions show user-provided inputs (HN username, OPML file path, goal) being passed to `bash` scripts, a detailed analysis of `feed-diet.sh`, `hn-fetch.sh`, and `opml-parse.sh` reveals robust sanitization and safe handling. Arguments are correctly quoted when passed between scripts, and variables are used in contexts (e.g., as URL components for trusted APIs, as Python environment variables, or as file paths for XML parsing) that prevent shell injection. Crucially, `opml-parse.sh` includes explicit URL scheme validation (`http://` or `https://` only) to prevent Server-Side Request Forgery (SSRF). The skill uses LLM API keys (Anthropic/OpenAI) if available, which is a necessary capability for its stated purpose of content classification, not a malicious act. Prompt instructions in `SKILL.md` are benign and align with the skill's functionality.
Capability Assessment
Purpose & Capability
Name/description (HN + OPML/RSS auditing) align with the included scripts (hn-fetch.sh, opml-parse.sh, classify.sh, feed-diet.sh). Minor inconsistency: the registry metadata lists no required binaries/env, but the code requires bash, curl, python3 and (for nicer output/JSON handling) jq. Those runtime dependencies are documented in README/SKILL.md but not declared in registry metadata.
Instruction Scope
Instructions are scoped to fetching HN submissions or parsing an OPML file, classifying items, and generating a Markdown report. Important runtime behaviors to note: (1) the agent will read a user-supplied OPML file path, (2) items (titles/URLs) are sent to external LLM endpoints when ANTHROPIC_API_KEY or OPENAI_API_KEY are set, and (3) the tool writes cache files under $XDG_CACHE_HOME or ~/.cache/feed-diet. The OPML parser enforces http/https feed URLs to reduce SSRF risk.
Install Mechanism
No remote install or downloads are performed by the skill (instruction-only with bundled scripts). That is lower risk; the skill will execute local shell scripts and create cache/temp files. There are no third-party packages pulled from networks at install time.
Credentials
The skill requests no required credentials in registry metadata. It optionally uses ANTHROPIC_API_KEY or OPENAI_API_KEY for LLM classification (documented). No unrelated secrets (AWS, GitHub tokens, etc.) are requested. Other env usage: FEED_DIET_BATCH_SIZE and XDG_CACHE_HOME/HOME for cache location.
Persistence & Privilege
always:false (not force-included). The skill creates a per-user cache directory (~/.cache/feed-diet) and temporary files; it does not modify other skills or system-wide configurations. Autonomous invocation is allowed (platform default) but not combined with any broad credential demands.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install feed-diet
  3. After installation, invoke the skill by name or use /feed-diet
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.2.1
Rebrand to Anvil AI. Remove CacheForge marketing copy. Normalize install commands.
v0.2.0
Republish — metadata refresh, force re-index
v0.1.2
Docs: normalize CacheForge footer and CTA.
v0.1.1
Launch: CacheForge wave 2. Discord v2 delivery, security hardened.
Metadata
Slug feed-diet
Version 0.2.1
License
All-time Installs 2
Active Installs 2
Total Versions 4
Frequently Asked Questions

What is feed-diet?

Audit your information diet across HN and RSS feeds — beautiful reports with category breakdowns, ASCII charts, and personalized recommendations. It is an AI Agent Skill for Claude Code / OpenClaw, with 625 downloads so far.

How do I install feed-diet?

Run "/install feed-diet" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is feed-diet free?

Yes, feed-diet is completely free (open-source). You can download, install and use it at no cost.

Which platforms does feed-diet support?

feed-diet is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created feed-diet?

It is built and maintained by Todd Kuehnl (@tkuehnl); the current version is v0.2.1.

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