🍽️ 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:
CODEBLOCK0
- 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
APIKEY or OPENAI
APIKEY is set) or falls back to keyword matching.
- 4. Generate the report. Run the main entry point:
CODEBLOCK2
- 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:
CODEBLOCK3
Quick Reference
| Command | Description |
|---|
| INLINECODE0 | Full diet audit for an HN user |
| INLINECODE1 |
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 - INLINECODE7 — Hacker News story fetcher
- INLINECODE8 — OPML/RSS feed parser
- INLINECODE9 — Batch content classifier (LLM + fallback)
- INLINECODE10 — Shared utilities and formatting
Examples
Example 1: HN Audit
User: "Audit my HN reading diet — my username is tosh"
Agent runs:
CODEBLOCK4
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:
CODEBLOCK5
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:
CODEBLOCK6
🍽️ 信息饮食分析
审计你的信息摄入,生成一份精美的报告,展示你实际消费的内容。
触发条件
当用户提及以下任一内容时激活:
- - 信息饮食分析
- 信息摄入审计
- 审计我的信息源
- 我在读什么
- 分析我的HN
- 阅读习惯
- 内容饮食
- 信息源报告
操作说明
审计模式(默认)
- 1. 确定数据来源。 询问用户选择以下之一:
-
Hacker News用户名(例如:tosh)
-
OPML文件路径,包含RSS订阅源
- 2. 获取内容。 运行相应的获取脚本:
bash
# 对于HN:
bash $SKILL_DIR/scripts/hn-fetch.sh 用户名 100
# 对于OPML:
bash $SKILL_DIR/scripts/opml-parse.sh /path/to/feeds.opml
- 3. 分类项目。 将获取的项目通过分类器处理:
bash
cat items.jsonl | bash $SKILL_DIR/scripts/classify.sh > classified.jsonl
分类器使用LLM(如果设置了ANTHROPICAPIKEY或OPENAIAPIKEY)或回退到关键词匹配。
- 4. 生成报告。 运行主入口点:
bash
bash $SKILL_DIR/scripts/feed-diet.sh audit --hn 用户名 --limit 100
- 5. 向用户展示报告。 输出为Markdown格式——直接渲染。
摘要模式(每周精选阅读)
当用户希望根据目标获取过滤后的阅读列表时:
bash
bash $SKILL_DIR/scripts/feed-diet.sh digest --hn 用户名 --goal 系统编程,分布式系统 --days 7
快速参考
| 命令 | 描述 |
|---|
| feed-diet audit --hn 用户 | 对HN用户进行完整饮食审计 |
| feed-diet audit --opml 文件 |
对RSS订阅源进行完整饮食审计 |
| feed-diet digest --hn 用户 --goal X | 按目标过滤的每周摘要 |
给助手的提示
- - 保持对话感。 展示报告后,提供观察意见,例如看起来你新闻类内容偏多——想让我推荐一些更深度的技术信息源吗?
- 如果用户对过滤阅读内容感兴趣,建议使用摘要模式。
- 报告是核心。 不要总结——完整呈现。它设计为值得截图分享的格式。
- 如果分类结果看起来不准确,提示设置LLM API密钥可以提高准确性。
Discord v2 交付模式(OpenClaw v2026.2.14+)
当对话在Discord频道中进行时:
- - 发送简洁的初步摘要(顶部类别、多样性评分、前2条推荐),然后询问用户是否需要完整报告。
- 首次回复控制在约1200字符以内,避免在第一条消息中包含宽类别表格。
- 如果Discord组件可用,包含快速操作:
- 显示完整饮食报告
- 生成每周摘要
- 显示推荐
- - 如果组件不可用,以编号列表形式提供相同的后续选项。
- 分享长报告时,优先使用短消息块(每条消息<=15行)。
参考
- - scripts/feed-diet.sh — 主入口点
- scripts/hn-fetch.sh — Hacker News故事获取器
- scripts/opml-parse.sh — OPML/RSS订阅源解析器
- scripts/classify.sh — 批量内容分类器(LLM + 回退方案)
- scripts/common.sh — 共享工具和格式化功能
示例
示例1:HN审计
用户: 审计我的HN阅读饮食——我的用户名是tosh
助手运行:
bash
bash $SKILL_DIR/scripts/feed-diet.sh audit --hn tosh --limit 50
输出: 完整的Markdown报告,包含类别细分表格、主要类别及示例项目、意外发现和推荐。
示例2:每周摘要
用户: 给我一份与我在编译器和编程语言方面工作相关的摘要
助手运行:
bash
bash $SKILL_DIR/scripts/feed-diet.sh digest --hn tosh --goal 编译器,编程语言,解析器 --days 7
输出: 10-20个项目的精选阅读列表,按与用户目标的相关性排序。
示例3:RSS订阅源审计
用户: 这是我的OPML文件,告诉我我的信息源饮食情况
助手运行:
bash
bash $SKILL_DIR/scripts/feed-diet.sh audit --opml /path/to/feeds.opml