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 openclaw profile to maintain session persistence.
• Credentials:
• User: xxxxxx
• Pass: xxxxxx

Workflow (Mandatory Steps)

Phase 1: Authentication & Navigation

1. Open https://www.ft.com/login.
2. Enter email and password.
3. Navigate to the homepage or a specific section requested by the user.

Phase 2: Content Extraction

1. Use evaluate to identify the top N articles from the homepage (targeting .o-teaser__heading or most-read sections).

2. For each target article:

3. Navigate to the article URL.

4. Use evaluate with the following JavaScript to extract clean content: javascript () => { 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('nn') }; }

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."