---
name: fs-street-4fc1ffb50e5c
description: Fetches articles from Farnam Street RSS. Use when asking about decision-making, mental models, learning, or wisdom from Farnam Street blog.
---

# 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

```bash
python skills/fs-street/scripts/fetch_blog.py --date YYYY-MM-DD
```

**Available commands**:

```bash
# 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

```markdown
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**:

```markdown
# 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

```bash
# 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
```


---
Source attribution: modbender
Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/fs-street/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 skill-library-mcp contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: modbender
Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/fs-street/SKILL.md
Attribution basis: Source repository record

Fields of work: Marketing
Roles: Content Marketer, Growth Marketer, Marketing Analyst
Specialties: API
Tools: Feedparser
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: a74e4224f3ae4fad973dc552f53364d2323ff9f0c08840fa9ce7660ac4f7d595