# How to Build a Claude Research Agent That Reads the Internet Every Morning and Briefs You in 5 Mins
**作者**: CyrilXBT
**日期**: 2026-05-25T03:16:38.000Z
**来源**: [https://x.com/cyrilXBT/status/2058749052712276332](https://x.com/cyrilXBT/status/2058749052712276332)
---
Most people start their day the same way.
They open Twitter and spend 20 minutes scrolling through noise looking for the three things that actually matter. They open their email and get pulled into reactive mode before they have done a single thing they intended to do. They check their RSS reader and feel overwhelmed by 200 unread items they will never get through. They open a news aggregator and read three articles about things that have no bearing on their work or their life.
Forty-five minutes later they are behind, stressed, and no better informed than if they had slept fifteen minutes longer.
The Claude research agent solves this problem permanently.
Every morning before you open anything, a Claude agent has already read every source that matters to your work, filtered everything irrelevant, synthesized the developments worth knowing, and deposited a structured 5-minute brief in your Obsidian vault.
You wake up. You read the brief. In five minutes you know everything you need to know. You start working.
This article is the complete build guide from zero to a running agent that briefs you every morning automatically.
## What the Research Agent Actually Does
Before the technical build, understand exactly what you are building and why each component matters.
The research agent performs four functions every morning automatically.
Source monitoring. It reads every information source you configure. Industry news. Competitor websites. Academic papers in your field. Specific newsletters. YouTube channels that publish research. Podcast transcripts. GitHub repositories you follow. Reddit communities in your niche. Any source where important information for your work might appear.
Signal filtering. It does not summarize everything it reads. It identifies what is actually significant based on criteria you define. A new product launch from a competitor is significant. A blog post rehashing the same information published last week is not. The filtering layer is what makes the brief genuinely useful rather than just a compressed version of everything.
Synthesis. It does not list the significant items as bullet points. It synthesizes them into a structured narrative that tells you what happened, why it matters, how it connects to things you already know, and what you should probably do about it.
Delivery. It deposits the brief in a specific location in your Obsidian vault every morning at a configured time so it is waiting when you open your laptop rather than requiring you to trigger it manually.
These four functions replace forty-five minutes of daily information gathering with five minutes of reading.
## The Technical Architecture
The research agent has five components. Each one has a specific role. Remove any one of them and the system produces worse results.
Claude is the intelligence layer. It reads raw information from sources, applies your criteria to filter what matters, and synthesizes the filtered information into a structured brief.
The Filesystem MCP connects Claude to your Obsidian vault. It gives Claude direct read and write access to your vault so it can read your CLAUDE.md for context and deposit the brief in the correct folder automatically.
Brave Search MCP gives Claude access to real-time web search. Without this Claude can only reason about information up to its training cutoff. With it Claude searches the live web and finds the most current information on any topic you specify.
N8N schedules the entire workflow. It fires the research agent at your configured time every morning, passes the correct context to Claude, receives the output, and saves it to your vault.
CLAUDE.md is the context layer that makes the brief relevant to your specific situation rather than generically informative. It tells Claude what you do, what you care about, what you already know, and what kind of information is actually actionable for you.
## Setting Up the Foundation
Before you build the workflow you need three things in place.
Claude Desktop with MCP connections.
Install Claude Desktop from claude.ai/download.
Configure your claude_desktop_config.json with the Filesystem and Brave Search MCP servers:
{
"mcpServers": {
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/path/to/your/obsidian/vault"
]
},
"brave-search": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-brave-search"],
"env": {
"BRAVE_API_KEY": "your-brave-api-key"
}
}
}
}
Get your Brave Search API key at brave.com/search/api. The free tier allows 2,000 queries per month which is more than sufficient for a daily research agent.
Restart Claude Desktop after saving the config. Verify the connections are live by asking Claude to search for something and checking that it returns real-time results.
Your Obsidian vault structure.
Create a folder called BRIEFINGS inside your vault if you do not already have one. This is where every morning brief will be deposited.
vault/
BRIEFINGS/
[YYYY-MM-DD]-morning-brief.md ← auto-generated daily
CLAUDE.md
[rest of your vault]
N8N self-hosted.
Self-hosted N8N on a $5 DigitalOcean droplet gives you unlimited workflow runs with no per-execution pricing.
If you do not have N8N set up follow this sequence: create a DigitalOcean account, spin up the smallest Ubuntu droplet, SSH in, install N8N via npm, and configure it to run as a service.
The full N8N setup takes about 30 minutes the first time. Every workflow you build after that uses the same infrastructure.
## Writing the CLAUDE.md Research Context
The CLAUDE.md is what makes the brief relevant to you specifically.
A generic research agent produces generic briefs. A research agent configured with your specific context produces briefs where every item is directly relevant to your work and your decisions.
Add a Research Context section to your existing CLAUDE.md or create a dedicated research CLAUDE.md in your vault:
# Research Agent Context
## Who I Am
[Your name, your role, what you do]
## My Primary Focus Areas
[List the specific topics, industries, or domains
where you need to stay informed]
## What Constitutes Significant News for Me
[Be specific. Not "AI news" but "Claude Code updates,
new MCP servers, multi-agent frameworks, AI agent
security developments"]
## My Competitive Landscape
[Specific companies, people, products you monitor]
## What I Already Know Well
[Topics where you have deep expertise and therefore
only need significant new developments, not
introductory coverage]
## What I Am Currently Working On
[Active projects where relevant news would be
directly actionable — UPDATE WEEKLY]
## Sources I Trust
[Specific publications, newsletters, researchers,
YouTube channels, subreddits worth prioritizing]
## What I Specifically Do NOT Want
[Topics that show up in your niche but waste your
time — generic AI hype pieces, rehashed content,
announcements from companies you do not care about]
## Brief Format Preference
[How you want the output structured — see template below]
The What I Specifically Do NOT Want section is the most important one most people skip.
Without it Claude includes everything tangentially related to your topics. With it Claude filters aggressively and the brief contains only what you would actually act on.
## The Research Agent Prompt
This is the core prompt that runs every morning. It goes into your N8N workflow as the message sent to the Claude API.
You are my personal research agent. Your job is to produce
my morning intelligence brief.
Read my research context from CLAUDE.md in my vault.
Then execute the following research sequence:
STEP 1: PRIMARY TOPIC SEARCH
For each focus area in my research context, search for
significant developments from the last 24 hours.
Use these search queries as a starting point but adapt
based on what you find:
[CLAUDE will generate appropriate queries based on
your CLAUDE.md focus areas]
STEP 2: SIGNAL FILTERING
From everything you find, apply these filters:
INCLUDE:
- New product launches or significant updates from
companies or tools I follow
- Research findings that change how we understand
something I care about
- Strategic moves by competitors I monitor
- Regulatory or policy changes affecting my domain
- New tools, frameworks, or techniques worth knowing
- Significant market movements in my focus areas
EXCLUDE:
- Anything I noted in my "What I Do NOT Want" section
- Rehashed content summarizing things published
more than 48 hours ago
- Opinion pieces without new information
- Announcements from companies I do not follow
- General AI hype without specific actionable detail
If nothing significant happened in a category:
say nothing happened rather than filling space.
STEP 3: COMPETITIVE INTELLIGENCE
Search specifically for any news about the companies
and people listed in my competitive landscape.
Highlight anything that represents a strategic shift,
new product, or significant announcement.
STEP 4: SYNTHESIS
Produce the brief in this exact format:
---
# Morning Brief — [DATE]
Generated: [TIME]
## THE ONE THING
[The single most important development today.
One paragraph. Why it matters for my specific situation.]
## WHAT HAPPENED
[3-7 significant items, each with:]
- **[Source/Company/Topic]**: What happened and
why it matters for my work. One to three sentences max.
## COMPETITIVE WATCH
[Any significant moves from my competitive landscape.
If nothing notable, say "Nothing significant today."]
## WHAT TO DO ABOUT IT
[1-3 specific actions worth considering based on
today's developments. Only include if genuinely
actionable. Skip this section if nothing warrants action.]
## READING LIST
[2-3 links to the most important full articles
for deeper reading on today's top items]
---
IMPORTANT FORMATTING RULES:
- The entire brief should take 5 minutes to read,
not 15.
- Every item must connect directly to something
in my research context.
- No filler. No hedging. If something matters say
why it matters specifically.
- If it was a slow news day say so directly rather
than inflating thin content.
Save the brief to: BRIEFINGS/[YYYY-MM-DD]-morning-brief.md
## Building the N8N Workflow
The N8N workflow has five nodes. Each one performs a specific function in the sequence.
Node 1: Schedule Trigger
Set the trigger to fire at your preferred morning time. 6AM works well for most people because it means the brief is ready before you open your laptop.
Configure the cron expression for your timezone:
0 6 * * 1-5 ← 6AM Monday through Friday
0 6 * * * ← 6AM every day including weekends
Node 2: Read CLAUDE.md
This node reads your research CLAUDE.md from your vault using a Read File operation pointed at the correct path.
Output: The full text of your CLAUDE.md research context.
Node 3: Prepare API Request
This node constructs the API call to Claude. It combines the research prompt template with the CLAUDE.md content read in Node 2.
const claudeMd = $node["Read CLAUDE.md"].json.content;
const today = new Date().toISOString().split('T')[0];
const time = new Date().toLocaleTimeString();
const systemPrompt = `You are a personal research agent.
Today's date is ${today}. Current time is ${time}.
You have access to real-time web search through
the Brave Search MCP. Use it extensively.
Research context from the user's CLAUDE.md:
${claudeMd}`;
return {
model: "claude-opus-4-5",
max_tokens: 4096,
system: systemPrompt,
messages: [{
role: "user",
content: "Generate my morning research brief following the format and instructions in my research context."
}]
};
Node 4: Claude API Call
HTTP Request node calling the Anthropic API:
URL: https://api.anthropic.com/v1/messages
Method: POST
Headers:
x-api-key: [YOUR ANTHROPIC API KEY]
anthropic-version: 2023-06-01
content-type: application/json
Body: [Output from Node 3]
Node 5: Save to Vault
Write File node that saves the Claude output to your BRIEFINGS folder:
File path: /path/to/vault/BRIEFINGS/[date]-morning-brief.md
Content: [Extracted text from Claude API response]
Extract the text content from the Claude API response:
const response = $node["Claude API Call"].json;
const content = response.content[0].text;
const date = new Date().toISOString().split('T')[0];
return {
filename: `${date}-morning-brief.md`,
content: content
};
Optional Node 6: Telegram Notification
Add a Telegram bot notification that fires when the brief is ready:
Message: "Morning brief ready: BRIEFINGS/[DATE]-morning-brief.md"
This means your phone receives a notification when the brief is deposited. You can read it on your phone before you get out of bed.
## Configuring Your Source List
The Brave Search MCP handles open web search automatically. But some of your most valuable sources are not well-indexed by standard search.
For sources that require specific monitoring add them to your CLAUDE.md research context with explicit instructions:
## Specific Sources to Monitor
### Daily Checks
- news.ycombinator.com — check front page for AI and developer tools
- reddit.com/r/MachineLearning — significant paper releases only
- reddit.com/r/ClaudeAI — new Claude features and community builds
### Weekly Checks
- arxiv.org/list/cs.AI — significant papers published this week
- github.com/trending — trending repositories in my tech stack
### Alert-Level Monitoring
[Companies or people where any news is significant]
- Anthropic blog: anthropic.com/news
- [Competitor 1] blog: [URL]
- [Competitor 2] press releases: [URL]
### Sources to Explicitly Ignore
[Publications or websites that consistently produce
low-signal content in your domain]
## The Feedback Loop That Improves the Brief Over Time
The brief gets better every week if you spend two minutes giving Claude feedback after reading it.
At the bottom of each brief add a section for your notes:
## My Notes on This Brief
[Your annotation — what was useful, what was noise,
what was missing]
Every Sunday run this prompt in Claude:
Read all morning briefs from the past week in my
BRIEFINGS folder and read all my annotations in
the "My Notes on This Brief" sections.
Based on my annotations, update my research
CLAUDE.md with:
1. Sources that consistently produced useful
signal → add to priority list
2. Topics that consistently produced noise →
add to the Do Not Want section
3. New search queries that would have surfaced
the information I noted as missing
4. Any patterns in what I found useful that
should be captured as explicit preferences
Show me the proposed changes to CLAUDE.md before
making them.
This feedback loop means the brief in month three is dramatically better calibrated to your specific information needs than the brief in week one.
The agent learns what you care about from your own annotations without you having to manually tune the configuration.
## Advanced Configurations
Once the basic morning brief is running reliably you can extend the system in several directions.
Topic Deep Dives
When a significant development appears in the morning brief add it to a queue for deeper research:
DEEP-DIVE: [topic from today's brief]
The queue processor picks this up and runs a more comprehensive research session producing a detailed analysis note rather than a brief summary.
Competitive Intelligence Alerts
Configure a separate lightweight workflow that checks for competitor news every four hours rather than once per day. When it finds something significant it sends an immediate Telegram notification rather than waiting for the morning brief.
Weekly Synthesis
Every Sunday morning instead of the standard daily brief Claude reads all seven daily briefs from the week and produces a weekly synthesis:
The biggest theme of the week and what it signals for your domain long-term.
The most important single development and what you should do about it.
What you expected to happen that did not and what that means.
The weekly synthesis often reveals patterns invisible in any individual daily brief.
Research on Demand
Drop any topic in your QUEUE folder prefixed with RESEARCH:
RESEARCH-quantum-computing-applications-in-finance.md
The queue processor runs a deep research session on the topic and deposits a comprehensive research brief in GENERATED rather than waiting for the morning cycle.
## What Changes After 30 Days
The morning brief delivers immediate value from day one.
The compounding effect becomes visible at month two.
After 30 days the brief is calibrated to your specific annotations. Topics that consistently produced noise have been removed. Sources that consistently produced signal have been prioritized. The five-minute reading time contains more genuinely actionable information than it did in week one.
After 60 days the weekly synthesis starts revealing patterns you could not see from inside any individual brief. Trends that were invisible in daily snapshots become visible across eight weeks of data.
After 90 days you have a research operation that knows your domain as well as a dedicated analyst who has been briefing you for three months.
The information advantage this creates compounds every week.
Your competitors are still spending forty-five minutes every morning scrolling through noise.
You spend five minutes reading signal.
That difference is not just time saved.
It is the quality of decisions made from better information consistently over time.
Build the workflow this weekend.
The first brief runs Monday morning.
Follow @cyrilXBT for the exact N8N workflow templates, CLAUDE.md research context structures, and Brave Search query patterns that make this entire system run.
## 相关链接
- [CyrilXBT](https://x.com/cyrilXBT)
- [@cyrilXBT](https://x.com/cyrilXBT)
- [272K](https://x.com/cyrilXBT/status/2058749052712276332/analytics)
- [claude.ai/download](https://claude.ai/download)
- [@modelcontextprotocol/server-filesystem](https://x.com/@modelcontextprotocol/server-filesystem)
- [@modelcontextprotocol/server-brave-search](https://x.com/@modelcontextprotocol/server-brave-search)
- [brave.com/search/api](https://brave.com/search/api)
- [$node](https://x.com/search?q=%24node&src=cashtag_click)
- [https://api.anthropic.com/v1/messages](https://api.anthropic.com/v1/messages)
- [$node](https://x.com/search?q=%24node&src=cashtag_click)
- [news.ycombinator.com](https://news.ycombinator.com/)
- [arxiv.org/list/cs.AI](https://arxiv.org/list/cs.AI)
- [github.com/trending](https://github.com/trending)
- [anthropic.com/news](https://anthropic.com/news)
- [@cyrilXBT](https://x.com/@cyrilXBT)
- [Upgrade to Premium](https://x.com/i/premium_sign_up)
- [11:16 AM · May 25, 2026](https://x.com/cyrilXBT/status/2058749052712276332)
- [272.3K Views](https://x.com/cyrilXBT/status/2058749052712276332/analytics)
- [View quotes](https://x.com/cyrilXBT/status/2058749052712276332/quotes)
---
*导出时间: 2026/5/26 21:56:27*
---
## 中文翻译
# 如何打造一个每天早上阅读互联网、并用 5 分钟为你做简报的 Claude 研究代理
**作者**： CyrilXBT
**日期**： 2026-05-25T03:16:38.000Z
**来源**： [https://x.com/cyrilXBT/status/2058749052712276332](https://x.com/cyrilXBT/status/2058749052712276332)
---
大多数人以同样的方式开始新的一天。
他们打开 Twitter，花 20 分钟在噪音中滑动，寻找真正重要的三件事。他们打开邮箱，在还没做任何一件原本计划要做的事情之前，就进入了被动应付模式。他们查看 RSS 阅读器，被 200 条永远不会读完的未读内容压得喘不过气。他们打开新闻聚合器，阅读三篇与自己的工作和生活毫无关系的文章。
四十五分钟后，他们落后了、疲惫了，但得到的信息并不比多睡十五分钟来得多。
Claude 研究代理可以一劳永逸地解决这个问题。
每天早上，在你打开任何东西之前，一个 Claude 代理已经读完了与你的工作相关的每一个重要信息源，过滤掉所有无关内容，综合整理出值得了解的进展，并将一份结构化的 5 分钟简报存入你的 Obsidian 库。
你醒来。你阅读简报。五分钟内，你了解了所有需要知道的事情。你开始工作。
本文是一份从零开始的完整构建指南，带你打造一个每天早上自动为你做简报的运行中的代理。
## 研究代理到底做什么
在开始技术构建之前，先明确你要构建的东西是什么，以及每个组件为什么重要。
研究代理每天早上自动执行四项功能。
信息源监控。它会读取你配置的每一个信息源。行业新闻。竞争对手网站。你所在领域的学术论文。特定的新闻通讯。发布研究内容的 YouTube 频道。播客文字稿。你关注的 GitHub 仓库。你所在细分领域的 Reddit 社区。任何可能出现对你工作重要的信息的来源。
信号过滤。它不会把它读到的所有内容都总结一遍。它会根据你定义的标准，识别出真正重要的内容。竞争对手发布新产品是重要的。一篇重新包装上周发布过的相同信息的博客文章则不是。正是这个过滤层让简报真正有用，而不只是把所有信息压缩一遍。
综合。它不会把重要条目简单地列成要点。它会将它们综合成一篇结构化的叙述，告诉你发生了什么、为什么重要、它与你已经知道的事情有什么关联，以及你可能应该采取什么行动。
交付。它每天早上在设定的时间将简报存入你 Obsidian 库中的特定位置，这样当你打开笔记本电脑时简报已经在那里等你，而不需要你手动触发。
这四项功能将每天 45 分钟的信息收集工作替换为 5 分钟的阅读。
## 技术架构
研究代理有五个组件。每个组件都有特定的角色。去掉任何一个，系统产生的结果都会变差。
Claude 是智能层。它读取来自各信息源的原始信息，按照你的标准过滤重要内容，并将过滤后的信息综合成一份结构化简报。
Filesystem MCP 将 Claude 连接到你的 Obsidian 库。它赋予 Claude 对你的库的直接读写权限，使其能够读取你的 CLAUDE.md 获取上下文，并自动将简报存入正确的文件夹。
Brave Search MCP 赋予 Claude 实时网络搜索能力。没有它，Claude 只能基于其训练截止日期之前的信息进行推理。有了它，Claude 可以搜索实时网络，找到你指定的任何主题的最新信息。
N8N 调度整个工作流。它每天早上在你设定的时间触发研究代理，将正确的上下文传递给 Claude，接收输出，并保存到你的库中。
CLAUDE.md 是上下文层，它让简报与你的具体情况相关，而不是泛泛而谈的信息。它告诉 Claude 你做什么工作、你关心什么、你已经知道什么，以及哪类信息对你来说是真正可行动的。
## 搭建基础
在构建工作流之前，你需要准备好三样东西。
带 MCP 连接的 Claude Desktop。
从 claude.ai/download 安装 Claude Desktop。
用 Filesystem 和 Brave Search MCP 服务器配置你的 claude_desktop_config.json：
{
"mcpServers": {
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/path/to/your/obsidian/vault"
]
},
"brave-search": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-brave-search"],
"env": {
"BRAVE_API_KEY": "your-brave-api-key"
}
}
}
}
在 brave.com/search/api 获取你的 Brave Search API 密钥。免费套餐每月允许 2,000 次查询，对于每天运行一次的研究代理来说绰绰有余。
保存配置后重启 Claude Desktop。让 Claude 搜索一些内容并确认它返回实时结果，以验证连接是否已生效。
你的 Obsidian 库结构。
如果你的库中还没有，创建一个名为 BRIEFINGS 的文件夹。每天早上的简报都将存放在这里。
vault/
BRIEFINGS/
[YYYY-MM-DD]-morning-brief.md ← 每天自动生成
CLAUDE.md
[库的其他部分]
自托管 N8N。
在一台 5 美元的 DigitalOcean 云主机上自托管 N8N，可以无限次运行工作流，且无需按执行次数付费。
如果你还没有设置 N8N，请按以下步骤操作：创建 DigitalOcean 账户，启动最小的 Ubuntu 云主机，SSH 登录，通过 npm 安装 N8N，并将其配置为服务运行。
完整的 N8N 设置第一次大约需要 30 分钟。之后你构建的每个工作流都使用相同的基础设施。
## 编写 CLAUDE.md 研究上下文
CLAUDE.md 是让简报与你的具体情况相关的关键。
一个通用的研究智能体只能产出通用的简报。而一个配置了你的具体背景的研究智能体，产出的简报中每一项内容都与你的工作和决策直接相关。
在你现有的 CLAUDE.md 中添加一个“研究背景”部分，或者在知识库中创建一个专门用于研究的 CLAUDE.md：
# 研究智能体背景
## 我是谁
[你的姓名、职位、从事的工作]
## 我的主要关注领域
[列出你需要持续了解的具体话题、行业或领域]
## 对我而言什么样的新闻才算重大
[要具体。不是"AI 新闻"，而是"Claude Code 更新、
新的 MCP 服务器、多智能体框架、AI 智能体
安全方面的新进展"]
## 我关注的竞争格局
[你在关注的具体公司、人物、产品]
## 我已经非常熟悉的领域
[你有深厚专长的话题，因此
只需要重大的新进展，
不需要入门性的内容]
## 我目前正在进行的工作
[相关的新闻能够直接转化为行动的活跃项目 —— 每周更新]
## 我信任的信息来源
[值得优先关注的特定出版物、新闻通讯、研究者、
YouTube 频道、subreddit]
## 我明确不想要的内容
[虽然出现在你的领域内但浪费你时间的话题 —— 泛泛的 AI 炒作文章、翻来覆去炒冷饭的内容、你并不关心的公司发布的公告]
## 简报格式偏好
[你希望输出如何组织 —— 参见下面的模板]
“我明确不想要的内容”这一节是最重要的，但大多数人都会跳过。
没有这一节，Claude 会把所有与你话题沾边的内容都包括进来。有了这一节，Claude 会积极过滤，简报中只包含你真正会采取行动的内容。
## 研究智能体提示词
这是每天早上运行的核心提示词。它作为发送给 Claude API 的消息放入你的 N8N 工作流中。
你是我的私人研究智能体。你的任务是生成
我的晨间情报简报。
从我的知识库中的 CLAUDE.md 读取我的研究背景。
然后执行以下研究流程：
第 1 步：主要话题搜索
对于我的研究背景中的每个关注领域，搜索
过去 24 小时内的重大进展。
以这些搜索查询为起点，但要根据你的
发现进行调整：
[CLAUDE 将根据你 CLAUDE.md 中的关注领域
生成合适的查询]
第 2 步：信号过滤
对你找到的所有内容，应用以下过滤器：
包括：
- 我关注的那些公司或工具的新产品发布或重大更新
- 能改变我们对我关心事物理解的研究发现
- 我关注的竞争对手的战略动向
- 影响我所在领域的监管或政策变化
- 值得了解的新工具、框架或技术
- 我的关注领域中的重要市场动态
排除：
- 我在"我不想要的内容"一节中注明的任何内容
- 对 48 小时之前发布内容进行转述的重复内容
- 没有新信息的观点文章
- 我不关注的公司的公告
- 没有具体可行动细节的泛泛 AI 炒作
如果某个类别没有重大事件发生：
直接说明"没有发生什么"，而不是用内容填充篇幅。
第 3 步：竞争情报
专门搜索关于我竞争格局中列出的
公司和人物的任何新闻。
突出任何代表战略转向、
新产品或重大公告的内容。
第 4 步：综合
以下述确切格式生成简报：
---
# 晨间简报 — [日期]
生成时间：[时间]
## 今日头等大事
[今天最重要的一项进展。
一个段落。它为什么对我的具体情况重要。]
## 发生了什么
[3-7 条重大事项，每条包含：]
- **[来源/公司/话题]**：发生了什么，
以及为什么对我的工作重要。最多一到三句话。
## 竞争动态观察
[我的竞争格局中的任何重大动向。
如果没有值得注意的，写"今天没有重大动态。"]
## 应对行动
[基于今天的进展值得考虑的 1-3 条具体行动。
只包含真正可行动的内容。如果没有值得行动的事项，
则跳过此节。]
## 阅读清单
[2-3 条链接，指向关于今日重点事项的最重要的
完整文章，供深入阅读]
---
重要的格式规则：
- 整份简报的阅读时间应为 5 分钟，而不是 15 分钟。
- 每一条内容都必须与我的研究背景中的某项内容直接相关。
- 不要凑数。不要含糊其辞。如果某件事重要，就要具体说明为什么重要。
- 如果今天是新闻淡日，直接说明，而不是用稀薄的内容充数。
将简报保存到：BRIEFINGS/[YYYY-MM-DD]-morning-brief.md
## 构建 N8N 工作流
N8N 工作流有五个节点。每个节点在流程中执行特定的功能。
节点 1：定时触发器
将触发器设置为你偏好的早晨时间。早上 6 点对大多数人来说很合适，因为这意味着在你打开笔记本电脑之前简报已经准备好了。
根据你的时区配置 cron 表达式：
0 6 * * 1-5 ← 周一至周五早上 6 点
0 6 * * * ← 每天早上 6 点，包括周末
节点 2：读取 CLAUDE.md
此节点使用指向正确路径的 Read File 操作从你的知识库中读取研究用 CLAUDE.md。
输出：你的 CLAUDE.md 研究背景的完整文本。
节点 3：准备 API 请求
此节点构建对 Claude 的 API 调用。它将研究提示词模板与节点 2 中读取的 CLAUDE.md 内容组合起来。
const claudeMd = $node["Read CLAUDE.md"].json.content;
const today = new Date().toISOString().split('T')[0];
const time = new Date().toLocaleTimeString();
const systemPrompt = `You are a personal research agent.
Today's date is ${today}. Current time is ${time}.
You have access to real-time web search through
the Brave Search MCP. Use it extensively.
Research context from the user's CLAUDE.md:
${claudeMd}`;
return {
model: "claude-opus-4-5",
max_tokens: 4096,
system: systemPrompt,
messages: [{
role: "user",
content: "按照我的研究上下文中的格式和说明生成我的晨间研究简报。"
}]
};
节点 4：Claude API 调用
调用 Anthropic API 的 HTTP Request 节点：
URL：https://api.anthropic.com/v1/messages
方法：POST
请求头：
x-api-key: [你的 ANTHROPIC API 密钥]
anthropic-version: 2023-06-01
content-type: application/json
请求体：[节点 3 的输出]
节点 5：保存到 Vault
将 Claude 输出保存到你的 BRIEFINGS 文件夹的 Write File 节点：
文件路径：/path/to/vault/BRIEFINGS/[日期]-morning-brief.md
内容：[从 Claude API 响应中提取的文本]
从 Claude API 响应中提取文本内容：
const response = $node["Claude API Call"].json;
const content = response.content[0].text;
const date = new Date().toISOString().split('T')[0];
return {
filename: `${date}-morning-brief.md`,
content: content
};
可选节点 6：Telegram 通知
添加一个 Telegram 机器人通知，在简报生成完毕时触发：
消息："晨间简报已生成：BRIEFINGS/[日期]-morning-brief.md"
这意味着当简报生成完毕时，你的手机会收到一条通知。你可以在起床之前就在手机上阅读它。
## 配置你的信息源列表
Brave Search MCP 会自动处理公开网络搜索。但你最有价值的一些信息源并不能被标准搜索引擎很好地索引。
对于需要专门监测的信息源，将它们添加到你的 CLAUDE.md 研究上下文中，并附上明确的说明：
## 需要监测的特定信息源
### 每日检查
- news.ycombinator.com —— 查看首页的 AI 和开发者工具内容
- reddit.com/r/MachineLearning —— 仅限重要论文发布
- reddit.com/r/ClaudeAI —— 新的 Claude 功能和社区作品
### 每周检查
- arxiv.org/list/cs.AI —— 本周发表的重要论文
- github.com/trending —— 我的技术栈中的热门仓库
### 警报级监测
[任何新闻都重要的公司或个人]
- Anthropic 博客：anthropic.com/news
- [竞争对手 1] 博客：[URL]
- [竞争对手 2] 新闻稿：[URL]
### 明确忽略的信息源
[在你的领域中持续产出低信号内容的
出版物或网站]
## 让简报随时间不断改进的反馈循环
如果你在阅读简报后花两分钟给 Claude 反馈，简报每周都会变得更好。
在每份简报的底部添加一个供你记录笔记的板块：
## 我对本简报的笔记
[你的批注——哪些有用，哪些是噪音，
哪些内容缺失了]
每个周日，在 Claude 中运行以下提示：
阅读我 BRIEFINGS 文件夹中过去一周的所有晨间简报，
并阅读"我对本简报的笔记"板块中的所有批注。
根据我的批注，更新我的研究 CLAUDE.md：
1. 持续产出有用信号的信息源 → 加入优先列表
2. 持续产生噪音的话题 → 加入 Do Not Want（不想要）部分
3. 能够发现我标注为缺失信息的新搜索查询
4. 我发现的有用内容中的任何模式，若应被记录为明确偏好
在做任何更改之前，先向我展示对 CLAUDE.md 的
拟议更改。
这个反馈循环意味着，第三个月的简报在针对你特定信息需求的校准上，会远远好于第一周的简报。
代理会从你自己的批注中学习你关心什么，而无需你手动调整配置。
## 高级配置
一旦基础的晨间简报稳定运行，你可以将系统向多个方向扩展。
话题深度研究
当晨间简报中出现重要进展时，将其加入一个队列以便进行更深入的研究：
DEEP-DIVE: [今天简报中的话题]
队列处理器会处理这项任务并运行一次更全面的研究会话，产出一份详细的分析笔记，而不是简短的摘要。
竞争情报警报
配置一个单独的轻量级工作流，每四小时而不是每天一次检查竞争对手新闻。当它发现重要内容时，会立即发送 Telegram 通知，而不是等待晨间简报。
每周综合
每个周日早晨，Claude 不生成标准的每日简报，而是阅读该周的全部七份每日简报并生成一份每周综合报告：
本周最大的主题及其对你的领域的长期意义。
最重要的单一进展以及你应该为此采取什么行动。
哪些你预期会发生但没有发生的事情，以及这意味着什么。
每周综合报告往往能揭示在任何单独一份每日简报中都看不见的模式。
按需研究
在你的 QUEUE 文件夹中放入任何以 RESEARCH 为前缀的话题：
RESEARCH-quantum-computing-applications-in-finance.md
队列处理器会对该话题运行一次深度研究会话，并在 GENERATED 中生成一份全面的研究简报，而不是等待晨间流程。
## 30 天后会发生什么变化
晨间简报从第一天起就能带来直接价值。
复利效应在第二个月开始显现。
30 天后，简报已根据你的具体批注完成校准。持续产生噪音的话题已被移除。持续产出信号的来源已被优先处理。这五分钟的阅读时间里，包含的真实可操作信息比第一周更多。
60 天后，每周综合报告开始揭示你从任何单独一份简报中都看不到的模式。在日常快照中不可见的趋势，在八周的数据中变得清晰可见。
90 天后，你将拥有一个如同一位已经为你简报了三个月的专职分析师一样了解你所在领域的研究体系。
由此产生的信息优势每周都在复利增长。
你的竞争对手仍然每天早上花四十五分钟在噪音中滚动浏览。
你只需花五分钟阅读信号。
这个差别不仅仅是节省了时间。
这是由更好的信息、在长期内持续做出的决策的质量。
本周末就把工作流搭建起来。
第一份简报将在周一早上运行。
关注 @cyrilXBT 获取让整套系统运转所需的确切 N8N 工作流模板、CLAUDE.md 研究上下文结构以及 Brave Search 查询模式。
## 相关链接
- [CyrilXBT](https://x.com/cyrilXBT)
- [@cyrilXBT](https://x.com/cyrilXBT)
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- [claude.ai/download](https://claude.ai/download)
- [@modelcontextprotocol/server-filesystem](https://x.com/@modelcontextprotocol/server-filesystem)
- [@modelcontextprotocol/server-brave-search](https://x.com/@modelcontextprotocol/server-brave-search)
- [brave.com/search/api](https://brave.com/search/api)
- [$node](https://x.com/search?q=%24node&src=cashtag_click)
- [https://api.anthropic.com/v1/messages](https://api.anthropic.com/v1/messages)
- [$node](https://x.com/search?q=%24node&src=cashtag_click)
- [news.ycombinator.com](https://news.ycombinator.com/)
- [arxiv.org/list/cs.AI](https://arxiv.org/list/cs.AI)
- [github.com/trending](https://github.com/trending)
- [anthropic.com/news](https://anthropic.com/news)
- [@cyrilXBT](https://x.com/@cyrilXBT)
- [升级到 Premium](https://x.com/i/premium_sign_up)
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*导出时间: 2026/5/26 21:56:27*
