fs-street

What This Skill Does

The fs-street skill acts as an intelligent interface for the Farnam Street blog, a premier resource for mental models, decision-making, leadership, and continuous learning. By integrating this skill into your OpenClaw agent, you can programmatically fetch, parse, and analyze articles from the blog. The skill handles complex queries by mapping relative natural language requests—such as 'yesterday' or 'the day before yesterday'—to specific date-based RSS fetches. It provides a structured workflow that includes content verification and formatted output, ensuring that insights from the blog are presented in a clean, readable, and actionable Markdown format.

Installation

To integrate this skill into your environment, use the OpenClaw command-line interface. Run the following command in your terminal:

clawhub install openclaw/skills/skills/hjw21century/fs-street

Ensure you have Python installed on your system along with the necessary dependencies: feedparser and requests. You can install these via pip install feedparser requests to ensure the fetch scripts operate correctly. No external API keys or complex configuration files are required, making it a plug-and-play solution for knowledge management.

Use Cases

This skill is perfect for professionals, students, and lifelong learners who want to keep up with Farnam Street’s wisdom without manually browsing the web. Common use cases include: summarizing complex decision-making strategies before a meeting, gathering mental models for a specific project, or building a recurring archive of leadership insights. It is especially useful for agents acting as a personal research assistant that filters out the noise and delivers high-signal content directly to your chat interface.

Example Prompts

1. "Could you fetch the Farnam Street article from yesterday and summarize the main points on decision-making?"
2. "Search the Farnam Street archives for any articles related to mental models."
3. "What was the post from 2024-06-13 about? Please format the key insights clearly."

Tips & Limitations

Please be aware that some premium content on the Farnam Street blog is marked as '[FS Members]'. The skill will fetch the available teaser text for these articles, but full access is subject to their platform's membership rules. Always use the YYYY-MM-DD date format when requesting specific days to ensure the Python script parses the request accurately. If you receive a 'no article available' response, use the '可用的日期' (available dates) command to see the range of data currently accessible.