优质资讯 logo

优质资讯

Community
laborany
优质资讯

RSS 资讯聚合工具。从全球 92 个高质量技术博客采集 AI/科技资讯,支持关键词搜索、主题分类、时间范围过滤、来源筛选,输出 Markdown 和 HTML 双格式报告。

Overview

Publisherlaborany
Repositorylaborany
Skill name优质资讯
Stars
84
Forks
10
Bundled files
7
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by laborany on GitHub. Read the source before you install it.

Installation

Install the 优质资讯 AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/laborany/laborany.git /tmp/laborany
mkdir -p .claude/skills
cp -r /tmp/laborany/skills/rss-news-aggregator .claude/skills/laborany-12
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 优质资讯 in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads 优质资讯 on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so 优质资讯 is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

优质资讯 - RSS 资讯聚合工具

从全球 92 个高质量技术博客采集最新的 AI/科技资讯,支持多维度过滤和智能质量评估。

✨ 功能特性

  • 多源聚合:从 92 个精选 RSS 源(HN Popularity Contest 2025)采集资讯
  • 智能过滤:支持关键词搜索、自然语言查询、主题分类、时间范围、精准来源筛选
  • 质量评估:基于来源权威性、内容质量、时效性自动评分排序(0-100 分)
  • 双格式输出:Markdown(便于编辑)+ HTML(Linear 简约风格 + 卡片布局)
  • 实时更新:直接从 RSS feeds 获取,分钟级时效性

🚀 快速开始

方式 1:获取最近 3 天的所有资讯

bash
python scripts/fetch_rss.py --days 3 --output /tmp/raw.json
python scripts/filter_content.py --input /tmp/raw.json --output /tmp/filtered.json
python scripts/generate_report.py --input /tmp/filtered.json --output-dir docs/news

方式 2:搜索特定主题

bash
# 搜索 "Claude" 相关资讯
python scripts/fetch_rss.py --days 7 --output /tmp/raw.json
python scripts/filter_content.py --input /tmp/raw.json --query "Claude" --output /tmp/filtered.json
python scripts/generate_report.py --input /tmp/filtered.json --output-dir docs/news

方式 3:精准过滤特定来源

bash
# 只获取 Simon Willison 和 Gary Marcus 的文章
python scripts/fetch_rss.py --days 3 --sources "simonwillison.net,garymarcus.substack.com" --output /tmp/raw.json
python scripts/filter_content.py --input /tmp/raw.json --output /tmp/filtered.json
python scripts/generate_report.py --input /tmp/filtered.json --output-dir docs/news

📋 使用流程

步骤 1:获取 RSS 数据

bash
python scripts/fetch_rss.py [选项]

选项

  • --days N:获取最近 N 天的资讯(默认 3 天)
  • --sources "source1,source2":只获取指定来源(可选)
  • --output FILE:输出 JSON 文件路径

步骤 2:过滤和评分

bash
python scripts/filter_content.py [选项]

选项

  • --input FILE:输入 JSON 文件(来自步骤 1)
  • --query "关键词":搜索查询(可选)
  • --sources "source1,source2":过滤特定来源(可选)
  • --min-quality N:最低质量分数 0-100(可选)
  • --output FILE:输出 JSON 文件路径

步骤 3:生成报告

bash
python scripts/generate_report.py [选项]

选项

  • --input FILE:输入 JSON 文件(来自步骤 2)
  • --output-dir DIR:输出目录(默认 docs/news
  • --format md|html|both:输出格式(默认 both)

步骤 4:查看报告

  • Markdowndocs/news/rss-news-YYYY-MM-DD.md
  • HTML:在浏览器中打开 docs/news/rss-news-YYYY-MM-DD.html

📊 数据源分类

来源统计

  • 总计:92 个高质量技术博客
  • 来源:HN Popularity Contest 2025

主要领域

  • AI/ML:Simon Willison, Gary Marcus, Gwern 等
  • 软件开发:Dan Abramov (Overreacted), Mitchell Hashimoto 等
  • 安全:Troy Hunt, Krebs on Security, lcamtuf 等
  • 系统架构:Jeff Geerling, antirez 等
  • 科技评论:Daring Fireball, Pluralistic 等

知名博主

  • Simon Willison (simonwillison.net) - AI/数据库专家
  • Paul Graham (paulgraham.com) - YC 创始人
  • Gary Marcus (garymarcus.substack.com) - AI 研究者
  • Mitchell Hashimoto (mitchellh.com) - HashiCorp 创始人
  • Dan Abramov (overreacted.io) - React 核心开发者

🎯 质量评估机制

评分算法(0-100 分)

来源权威性(40%)

  • 官方博客:1.5x 权重
  • 学术/研究:1.3x 权重
  • 科技媒体:1.2x 权重
  • 个人博客:1.0x 权重

内容质量(30%)

  • 高质量关键词:research, analysis, deep dive, tutorial, guide 等(+2 分/个)
  • 低质量关键词:clickbait, shocking, viral 等(-5 分/个)

时效性(20%)

  • 24 小时内:+10 分
  • 72 小时内:+5 分

相关性(10%)

  • 关键词匹配度(查询时)

综合排序

final_score = quality_score * 0.6 + relevance_score * 100 * 0.4

主题分类

自动将资讯分类到以下主题:

  • 🤖 AI/ML
  • 💻 Software Development
  • 🔒 Security
  • 🏗️ System Architecture
  • 🌐 Web Development
  • ⚙️ DevOps
  • 🗄️ Database
  • 🚀 Startup/Business
  • 📄 Other

🆚 与 topic-collector 的差异

特性topic-collectorrss-news-aggregator
数据源mcp__laborany_web__search 搜索引擎RSS feeds 直接订阅
时效性依赖搜索引擎索引实时 RSS 更新(分钟级)
覆盖面广泛但不稳定精选高质量源(92 个)
过滤能力手动整理自动化多维过滤
质量控制人工筛选算法自动评分
适用场景每日热点采集专题资讯聚合
用户输入固定主题灵活查询(关键词/自然语言)

定位

  • topic-collector:每日 AI 热点快照(广度优先)
  • rss-news-aggregator:专题资讯深度聚合(深度优先)

🛠️ 技术挑战与解决方案

挑战 1:RSS 源失效或格式不一致

解决方案

  • 使用 feedparser 库统一解析 RSS/Atom/JSON Feed
  • 实现容错机制:单个源失败不影响整体
  • 并发获取(ThreadPoolExecutor,max_workers=10)

挑战 2:内容去重

解决方案

  • 基于 URL 去重(优先)
  • 基于标题相似度去重(可选)

挑战 3:HTML 样式独立性

解决方案

  • 所有样式内联到 HTML 文件(无外部 CSS)
  • 使用 CSS 变量实现暗色模式
  • 响应式设计(移动端友好)

📦 依赖说明

bash
pip install feedparser python-dateutil

依赖列表

  • feedparser:RSS/Atom feed 解析
  • python-dateutil:日期时间处理

📁 输出文件

文件类型保存位置命名规则
Markdowndocs/news/rss-news-{YYYY-MM-DD}.md
HTMLdocs/news/rss-news-{YYYY-MM-DD}.html

🔮 未来扩展

  • 订阅管理:用户自定义 RSS 源(Web UI)
  • 智能摘要:使用 LLM 生成更精炼的摘要
  • 趋势分析:识别热门话题和趋势(词云、时间线)
  • 邮件推送:定时发送资讯摘要到邮箱
  • 多语言翻译:自动翻译外文资讯
  • 收藏功能:用户标记感兴趣的资讯

📝 示例输出

Markdown 格式

markdown
# 优质资讯 - 2026-03-11

## 📊 概览
- 总计:45 条资讯
- 来源:28 个
- 生成时间:2026-03-11 10:30:00

## 🤖 AI/ML
### [Claude 4.6 Released with Enhanced Reasoning](https://example.com)
**来源**: simonwillison.net | **时间**: 2 小时前 | **质量**: 95/100

Anthropic announces Claude 4.6 with significant improvements...

HTML 格式

Linear 简约风格,包含:

  • 响应式卡片布局
  • 暗色模式支持
  • 悬停动画效果
  • 质量徽章(颜色编码)
  • 移动端友好

📞 支持

如有问题或建议,请在 laborany 项目中提交 issue。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the 优质资讯 AI skill do?

RSS 资讯聚合工具。从全球 92 个高质量技术博客采集 AI/科技资讯,支持关键词搜索、主题分类、时间范围过滤、来源筛选,输出 Markdown 和 HTML 双格式报告。

Why use 优质资讯 on TypingMind?

Because you install it once and use it with any model. 优质资讯 is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install 优质资讯 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/laborany/laborany/tree/main/skills/rss-news-aggregator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use 优质资讯?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with 优质资讯?

As many as you like. As long as a model supports skills, you can use 优质资讯 with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the 优质资讯 AI skill free?

Yes. It is published on GitHub by laborany under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇