Ft Reader OpenClaw Plugin & Skill | ClawHub
Looking to integrate Ft Reader into your AI workflows? This free OpenClaw plugin from ClawHub helps you automate browser & automation tasks instantly, without having to write custom tools from scratch.
What this skill does
Use this skill to perform deep, structured, and bilingual analysis of top articles from Financial Times (ft.com).
Install
openclaw skills install @zhouziyue233/ft-readernpx clawhub@latest install ft-readerFull SKILL.md
Open originalSKILL.md content below is scrollable.
Financial Times Deep Reader (ft-reader)
Use this skill to perform deep, structured, and bilingual analysis of top articles from Financial Times (ft.com). This skill automates login, article selection, and high-quality summarization suitable for academic and professional use.
Capabilities
- Automated Access: Logs into FT.com using stored credentials via Browser tool.
- Strategic Selection: Identifies "Most Read" based on user preference.
- Bilingual Synthesis: Provides high-fidelity English-Chinese summaries with a focus on core arguments.
- Academic Rigor: Extracts specific data, quotes, and important charts in the article.
Configuration & Credentials
- Browser Profile: Use
openclawprofile to maintain session persistence. - Credentials:
- User:
xxxxxx - Pass:
xxxxxx
- User:
Workflow (Mandatory Steps)
Phase 1: Authentication & Navigation
- Open
https://www.ft.com/login. - Enter email and password.
- Navigate to the homepage or a specific section requested by the user.
Phase 2: Content Extraction
-
Use
evaluateto identify the top N articles from the homepage (targeting.o-teaser__headingor most-read sections). -
For each target article:
-
Navigate to the article URL.
-
Use
evaluatewith the following JavaScript to extract clean content:() => { const title = document.querySelector('h1')?.innerText; const standfirst = document.querySelector('div[class*="standfirst"]')?.innerText; const paragraphs = Array.from(document.querySelectorAll('div[class*="article-body"] p, article p')) .map(p => p.innerText.trim()) .filter(text => text.length > 0); return { title, summary: standfirst, content: paragraphs.join('\n\n') }; }
-
Phase 3: Analysis & Reporting
For each article, generate a report (around 600 words) using the following structure:
- Title (Bilingual)
- Core Opinion (Bilingual)
- Arguments (Bilingual)
- Conclusion (Bilingual)
Constraints
- Style: Professional, academic, and fluff-free (follow SOUL.md).
- Language: Always provide both English and Chinese translations for technical terms and core ideas.
- Independent Reading: Treat each article as a standalone piece unless cross-analysis is requested.
- Token Management: If many articles are requested, split the delivery into multiple turns to avoid truncation.
Usage Examples
- "Lulu, use ft-reader to analyze the top 3 Most Read articles from today."
- "Perform a deep dive into the top story on FT regarding AI productivity using the ft-reader skill."