架构升级：主 Agent 调度 + SubAgent 执行 + 浏览器抓取 + 智能缓存
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondaily-news-reportExecute the skills CLI command in your project's root directory to begin installation:
Fetches daily-news-report from rookie-ricardo/erduo-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate daily-news-report. Access via /daily-news-report in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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架构升级：主 Agent 调度 + SubAgent 执行 + 浏览器抓取 + 智能缓存
┌─────────────────────────────────────────────────────────────────────┐
│ 主 Agent (Orchestrator) │
│ 职责：调度、监控、评估、决策、汇总 │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ 1. 初始化 │ → │ 2. 调度 │ → │ 3. 监控 │ → │ 4. 评估 │ │
│ │ 读取配置 │ │ 分发任务 │ │ 收集结果 │ │ 筛选排序 │ │
│ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │
│ │ │ │ │ │
│ ▼ ▼ ▼ ▼ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ 5. 决策 │ ← │ 够20条？ │ │ 6. 生成 │ → │ 7. 更新 │ │
│ │ 继续/停止 │ │ Y/N │ │ 日报文件 │ │ 缓存统计 │ │
│ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │
│ │
└──────────────────────────────────────────────────────────────────────┘
↓ 调度 ↑ 返回结果
┌─────────────────────────────────────────────────────────────────────┐
│ SubAgent 执行层 │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Worker A │ │ Worker B │ │ Browser │ │
│ │ (WebFetch) │ │ (WebFetch) │ │ (Headless) │ │
│ │ Tier1 Batch │ │ Tier2 Batch │ │ JS渲染页面 │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ ↓ ↓ ↓ │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ 结构化结果返回 │ │
│ │ { status, data: [...], errors: [...], metadata: {...} } │ │
│ └─────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────┘
本 Skill 使用以下配置文件：
| 文件 | 用途 |
|---|---|
sources.json |
信息源配置、优先级、抓取方法 |
cache.json |
缓存数据、历史统计、去重指纹 |
步骤:
1. 确定日期（用户参数或当前日期）
2. 读取 sources.json 获取源配置
3. 读取 cache.json 获取历史数据
4. 创建输出目录 NewsReport/
5. 检查今日是否已有部分报告（追加模式）
策略：并行调度，分批执行，早停机制
第1波 (并行):
- Worker A: Tier1 Batch A (HN, HuggingFace Papers)
- Worker B: Tier1 Batch B (OneUsefulThing, Paul Graham)
等待结果 → 评估数量
如果 < 15 条高质量:
第2波 (并行):
- Worker C: Tier2 Batch A (James Clear, FS Blog)
- Worker D: Tier2 Batch B (HackerNoon, Scott Young)
如果仍 < 20 条:
第3波 (浏览器):
- Browser Worker: ProductHunt, Latent Space (需要JS渲染)
每个 SubAgent 接收的任务格式：
task: fetch_and_extract
sources:
- id: hn
url: https://news.ycombinator.com
extract: top_10
- id: hf_papers
url: https://huggingface.co/papers
extract: top_voted
output_schema:
items:
- source_id: string # 来源标识
title: string # 标题
summary: string # 2-4句摘要
key_points: string[] # 最多3个要点
url: string # 原文链接
keywords: string[] # 关键词
quality_score: 1-5 # 质量评分
constraints:
filter: "前沿技术/高深技术/提效技术/实用资讯"
exclude: "泛科普/营销软文/过度学术化/招聘帖"
max_items_per_source: 10
skip_on_error: true
return_format: JSON
主 Agent 职责：
监控:
- 检查 SubAgent 返回状态 (success/partial/failed)
- 统计收集到的条目数量
- 记录每个源的成功率
反馈循环:
- 如果某 SubAgent 失败，决定是否重试或跳过
- 如果某源持续失败，标记为禁用
- 动态调整后续批次的源选择
决策:
- 条目数 >= 25 且高质量 >= 20 → 停止抓取
- 条目数 < 15 → 继续下一批
- 所有批次完成但 < 20 → 用现有内容生成（宁缺毋滥）
去重:
- 基于 URL 完全匹配
- 基于标题相似度 (>80% 视为重复)
- 检查 cache.json 避免与历史重复
评分校准:
- 统一各 SubAgent 的评分标准
- 根据来源可信度调整权重
- 手动标注的高质量源加分
排序:
- 按 quality_score 降序
- 同分按来源优先级排序
- 截取 Top 20
对于需要 JS 渲染的页面，使用无头浏览器：
流程:
1. 调用 mcp__chrome-devtools__new_page 打开页面
2. 调用 mcp__chrome-devtools__wait_for 等待内容加载
3. 调用 mcp__chrome-devtools__take_snapshot 获取页面结构
4. 解析 snapshot 提取所需内容
5. 调用 mcp__chrome-devtools__close_page 关闭页面
适用场景:
- ProductHunt (403 on WebFetch)
- Latent Space (Substack JS 渲染)
- 其他 SPA 应用
输出:
- 目录: NewsReport/
- 文件名: YYYY-MM-DD-news-report.md
- 格式: 标准 Markdown
内容结构:
- 标题 + 日期
- 统计摘要（源数量、收录数量）
- 20条高质量内容（按模板）
- 生成信息（版本、时间戳）
更新 cache.json:
- last_run: 记录本次运行信息
- source_stats: 更新各源统计数据
- url_cache: 添加已处理的 URL
- content_hashes: 添加内容指纹
- article_history: 记录收录文章
由于自定义 agent 需要 session 重启才能发现，可以使用 general-purpose 并注入 worker prompt：
Task 调用:
subagent_type: general-purpose
model: haiku
prompt: |
你是一个无状态的执行单元。只做被分配的任务，返回结构化 JSON。
任务：抓取以下 URL 并提取内容
URLs:
- https://news.ycombinator.com (提取 Top 10)
- https://huggingface.co/papers (提取高投票论文)
输出格式：
{
"status": "success" | "partial" | "failed",
"data": [
{
"source_id": "hn",
"title": "...",
"summary": "...",
"key_points": ["...", "...", "..."],
"url": "...",
"keywords": ["...", "..."],
"quality_score": 4
}
],
"errors": [],
"metadata": { "processed": 2, "failed": 0 }
}
筛选标准：
- 保留：前沿技术/高深技术/提效技术/实用资讯
- 排除：泛科普/营销软文/过度学术化/招聘帖
直接返回 JSON，不要解释。
Task 调用:
subagent_type: worker
prompt: |
task: fetch_and_extract
input:
urls:
- https://news.ycombinator.com
- https://huggingface.co/papers
output_schema:
- source_id: string
- title: string
- summary: string
- key_points: string[]
- url: string
- keywords: string[]
- quality_score: 1-5
constraints:
filter: 前沿技术/高深技术/提效技术/实用资讯
exclude: 泛科普/营销软文/过度学术化
# Daily News Report（YYYY-MM-DD）
> 本日筛选自 N 个信息源，共收录 20 条高质量内容
> 生成耗时: X 分钟 | 版本: v3.0
>
> **Warning**: Sub-agent 'worker' not detected. Running in generic mode (Serial Execution). Performance might be degraded.
> **警告**：未检测到 Sub-agent 'worker'。正在以通用模式（串行执行）运行。性能可能会受影响。
---
## 1. 标题
- **摘要**：2-4 行概述
- **要点**：
1. 要点一
2. 要点二
3. 要点三
- **来源**：[链接](URL)
- **关键词**：`keyword1` `keyword2` `keyword3`
- **评分**：⭐⭐⭐⭐⭐ (5/5)
---
## 2. 标题
...
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
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AI/MLsame categoryReviews
4.4★★★★★66 reviews- MMin Taylor★★★★★Dec 28, 2024
daily-news-report fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- XXiao Malhotra★★★★★Dec 28, 2024
daily-news-report has been reliable in day-to-day use. Documentation quality is above average for community skills.
- DDaniel Zhang★★★★★Dec 24, 2024
daily-news-report has been reliable in day-to-day use. Documentation quality is above average for community skills.
- OOlivia Li★★★★★Dec 24, 2024
Useful defaults in daily-news-report — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- IIsabella Dixit★★★★★Dec 16, 2024
I recommend daily-news-report for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- IIsabella Chawla★★★★★Dec 16, 2024
We added daily-news-report from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- GGanesh Mohane★★★★★Dec 12, 2024
Useful defaults in daily-news-report — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- NNoah Li★★★★★Nov 19, 2024
Registry listing for daily-news-report matched our evaluation — installs cleanly and behaves as described in the markdown.
- BBenjamin Perez★★★★★Nov 7, 2024
Keeps context tight: daily-news-report is the kind of skill you can hand to a new teammate without a long onboarding doc.
- BBenjamin Mensah★★★★★Nov 7, 2024
daily-news-report reduced setup friction for our internal harness; good balance of opinion and flexibility.
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