---
name: sn-search-social-en-53618a001ec2
description: 用于搜索英文社交平台，包括 Reddit 帖子、Twitter/X 推文和 YouTube 视频。
---

# sn-search-social-en - 英文社交平台搜索

## 凭证配置

API key、token 与 cookie 统一建议写在仓库根目录 `.env`（参考 `.env.example`），并由 runtime 或用户在执行前加载为同名环境变量。脚本仍只从环境变量或显式 CLI 参数读取凭证；不要把真实密钥写入 skill payload、报告、日志或提交。

搜索 Reddit、Twitter/X、YouTube 三个英文社交平台。

## 可用脚本

| 脚本 | 平台 | 用途 | API 密钥 |
|------|------|------|---------|
| `reddit_search.py` | Reddit | 帖子和讨论搜索 | 无需 |
| `twitter_search.py` | Twitter/X | 推文搜索 | 需 `TIKHUB_TOKEN` |
| `youtube_search.py` | YouTube | 视频搜索 | 需 `YOUTUBE_API_KEY` |

## 依赖

首次运行或脚本提示缺库时，使用本技能的依赖清单安装到当前 Python 环境：

```bash
python3 -m pip install -r requirements.txt
```

不要在脚本内部自动安装依赖。若安装失败、网络不可用或包不可用，停止使用对应脚本并改用网页搜索，说明缺少依赖。

## 参数说明

### reddit_search.py

```bash
python3 scripts/reddit_search.py <query> [选项]
```

| 参数 | 说明 | 默认值 |
|------|------|--------|
| `query` | 搜索关键词（必填） | — |
| `--limit`, `-n` | 返回结果数量 | 10 |
| `--subreddit`, `-r` | 限定子版块（如 `python`, `machinelearning`） | — |
| `--sort` | 排序方式：`relevance`, `hot`, `top`, `new`, `comments` | relevance |
| `--time`, `-t` | 时间范围：`hour`, `day`, `week`, `month`, `year`, `all` | all |

```bash
python3 scripts/reddit_search.py "machine learning projects" --limit 5
python3 scripts/reddit_search.py "async python" --subreddit python --sort top --time month --limit 5
```

### twitter_search.py

```bash
python3 scripts/twitter_search.py <query> [选项]
```

| 参数 | 说明 | 默认值 |
|------|------|--------|
| `query` | 搜索关键词（必填） | — |
| `--limit`, `-n` | 返回结果数量 | 10 |
| `--token` | TikHub Token（也可通过 `TIKHUB_TOKEN` 环境变量设置，必填） | — |

```bash
python3 scripts/twitter_search.py "AI agents" --limit 10
python3 scripts/twitter_search.py "LLM" --token your_tikhub_token --limit 5
```

### youtube_search.py

```bash
python3 scripts/youtube_search.py <query> [选项]
```

| 参数 | 说明 | 默认值 |
|------|------|--------|
| `query` | 搜索关键词（必填） | — |
| `--limit`, `-n` | 返回结果数量 | 10 |
| `--api-key` | YouTube API 密钥（也可通过 `YOUTUBE_API_KEY` 环境变量设置，必填） | — |
| `--order` | 排序方式：`relevance`, `date`, `viewCount`, `rating` | relevance |

```bash
python3 scripts/youtube_search.py "transformer explained" --limit 5
python3 scripts/youtube_search.py "python tutorial" --order viewCount --limit 10
```

## 输出格式

标准 JSON：`{"success": true, "query": "...", "provider": "reddit|twitter|youtube", "items": [...], "error": null}`


---
Source attribution: OpenSenseNova
Original project: https://github.com/OpenSenseNova/SenseNova-Skills
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

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: OpenSenseNova
Original project: https://github.com/OpenSenseNova/SenseNova-Skills
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Marketing
Roles: Content Marketer, Influencer Marketer, Social Media Manager
Specialties: API
Tools: Python
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 388f198fa5717cc3fa20f8f99d9689207e0bbf720ac39d6b1ca8dd8aadbbe09d