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Fs Street
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hjw21century
· GitHub ↗
· v0.1.0
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Install in OpenClaw
/install fs-street
Description
Fetches articles from Farnam Street RSS. Use when asking about decision-making, mental models, learning, or wisdom from Farnam Street blog.
README (SKILL.md)
Farnam Street
Fetches articles from Farnam Street blog, covering topics like mental models, decision-making, leadership, and learning.
Quick Start
# Basic queries
昨天的文章
今天的FS文章
2024-06-13的文章
# Search
有哪些可用的日期
Query Types
| Type | Examples | Description |
|---|---|---|
| Relative date | 昨天的文章 今天的文章 前天 |
Yesterday, today, day before |
| Absolute date | 2024-06-13的文章 |
YYYY-MM-DD format |
| Date range | 有哪些日期 可用的日期 |
Show available dates |
| Topic search | 关于决策的文章 思维模型 |
Search by keyword |
Workflow
- [ ] Step 1: Parse date from user request
- [ ] Step 2: Fetch RSS data
- [ ] Check content availability
- [ ] Format and display results
Step 1: Parse Date
| User Input | Target Date | Calculation |
|---|---|---|
昨天 |
Yesterday | today - 1 day |
前天 |
Day before | today - 2 days |
今天 |
Today | Current date |
2024-06-13 |
2024-06-13 | Direct parse |
Format: Always use YYYY-MM-DD
Step 2: Fetch RSS
python skills/fs-street/scripts/fetch_blog.py --date YYYY-MM-DD
Available commands:
# Get specific date
python skills/fs-street/scripts/fetch_blog.py --date 2024-06-13
# Get date range
python skills/fs-street/scripts/fetch_blog.py --date-range
# Relative dates
python skills/fs-street/scripts/fetch_blog.py --relative yesterday
Requirements: pip install feedparser requests
Step 3: Check Content
When NOT Found
Sorry, no article available for 2024-06-14
Available date range: 2023-04-19 ~ 2024-06-13
Suggestions:
- View 2024-06-13 article
- View 2024-06-12 article
Members Only Content
Some articles are marked [FS Members] - these are premium content and may only show a teaser.
Step 4: Format Results
Example Output:
# Farnam Street · 2024年6月13日
> Experts vs. Imitators: How to tell the difference between real expertise and imitation
## Content
If you want the highest quality information, you have to speak to the best people. The problem is many people claim to be experts, who really aren't.
**Key Insights**:
- Imitators can't answer questions at a deeper level
- Experts can tell you all the ways they've failed
- Imitators don't know the limits of their expertise
---
Source: Farnam Street
URL: https://fs.blog/experts-vs-imitators/
Configuration
| Variable | Description | Default |
|---|---|---|
| RSS_URL | RSS feed URL | https://fs.blog/feed/ |
No API keys required.
Troubleshooting
| Issue | Solution |
|---|---|
| RSS fetch fails | Check network connectivity |
| Invalid date | Use YYYY-MM-DD format |
| No content | Check available date range |
| Members only | Some articles are premium content |
CLI Reference
# Get specific date
python skills/fs-street/scripts/fetch_blog.py --date 2024-06-13
# Get date range
python skills/fs-street/scripts/fetch_blog.py --date-range
# Relative dates
python skills/fs-street/scripts/fetch_blog.py --relative yesterday
Usage Guidance
This skill appears coherent and limited to fetching Farnam Street RSS entries. Before installing: (1) note you may need to pip install feedparser and requests if you run the script locally; (2) adjust the script path in the docs if you invoke it exactly as shown (use scripts/fetch_blog.py or update the path to where the agent installs skills); (3) be aware the skill fetches content from fs.blog over the network and may show only teasers for members-only posts; (4) no secrets or broad permissions are requested. If you require stricter controls, run the included script in an isolated environment or inspect it locally before enabling autonomous use.
Capability Analysis
Type: OpenClaw Skill
Name: fs-street
Version: 0.1.0
The OpenClaw skill bundle 'fs-street' is classified as benign. The `SKILL.md` file provides clear, non-malicious instructions for fetching Farnam Street articles and does not contain any prompt injection attempts to subvert the agent's behavior. The `scripts/fetch_blog.py` Python script uses `argparse` for robust command-line argument parsing, hardcodes the RSS feed URL (`https://fs.blog/feed/`), and outputs structured JSON, preventing shell injection or arbitrary code execution within the script itself. There is no evidence of data exfiltration, persistence mechanisms, or other malicious activities.
Capability Assessment
Purpose & Capability
Name and description match the included code and instructions. The only external network access is to the Farnam Street RSS feed (https://fs.blog/feed/), which is exactly what the skill claims to do.
Instruction Scope
SKILL.md instructs running a bundled Python script and includes only feed-related operations and date parsing. One minor inconsistency: the docs show the script path as 'python skills/fs-street/scripts/fetch_blog.py' while the repository file is at 'scripts/fetch_blog.py' — that path mismatch may cause confusion when invoking the CLI exactly as shown.
Install Mechanism
There is no install spec in the registry (instruction-only), and the script depends on public Python packages (feedparser, requests) which the SKILL.md correctly documents (pip install feedparser requests). No downloads from untrusted URLs or archive extraction are present.
Credentials
The skill requires no environment variables, credentials, or config paths. It performs a single, expected network fetch to the public RSS URL; no secrets are requested or used.
Persistence & Privilege
The skill does not request persistent 'always' inclusion and does not modify other skills or system settings. It runs on-demand and prints structured JSON output; autonomous invocation is enabled by platform default but the skill itself has no elevated privileges.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install fs-street - After installation, invoke the skill by name or use
/fs-street - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
- Initial release of the fs-street skill.
- Fetches articles from the Farnam Street blog via RSS.
- Supports queries by absolute and relative dates, date ranges, and topic keywords.
- Provides clear guidance for basic queries and CLI usage.
- Handles premium member-only articles with clear messaging and teasers.
- Includes troubleshooting tips and configuration details.
Metadata
Frequently Asked Questions
What is Fs Street?
Fetches articles from Farnam Street RSS. Use when asking about decision-making, mental models, learning, or wisdom from Farnam Street blog. It is an AI Agent Skill for Claude Code / OpenClaw, with 682 downloads so far.
How do I install Fs Street?
Run "/install fs-street" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Fs Street free?
Yes, Fs Street is completely free (open-source). You can download, install and use it at no cost.
Which platforms does Fs Street support?
Fs Street is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Fs Street?
It is built and maintained by hjw21century (@hjw21century); the current version is v0.1.0.
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