Best use case
visual-benchmarker is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
(元技能) 视觉对标视频搜索器,通过指导AI调用其他工具,为项目确认视觉风格。
Teams using visual-benchmarker should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/visual-benchmarker/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How visual-benchmarker Compares
| Feature / Agent | visual-benchmarker | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
(元技能) 视觉对标视频搜索器,通过指导AI调用其他工具,为项目确认视觉风格。
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 技能名称:视觉对标视频搜索器 (visual-benchmarker)
### 核心目标
这是一个**元技能 (Meta-Skill)**,它本身不包含代码,而是作为一个行动手册,指导AI通过组合自身能力和外部工具,为项目(如短视频拍摄)确认视觉风格和剪辑风格,从而避免返工。
---
## 🧠 工作流程(三步走)
### 第一步:AI 提取关键词
AI 需要根据用户上传的资料(如文本文件),提取核心品类与搜索关键词。**这一步完全依赖 AI 的推理能力,无需代码**,只需执行以下提示词:
```markdown
**提示词模板:**
你是一位资深的短视频内容策略师。请仔细阅读以下客户资料,从中提取可用于抖音搜索的“核心品类”和“关键词”。
**客户资料:**
{此处粘贴用户上传的作战地图/产品资料/启动会录音文本}
**要求:**
1. 首先确定核心品类(例如:女装、母婴、知识付费等)。
2. 然后围绕该品类,提取 3-5 个精准搜索关键词(例如:极简通勤穿搭、亲子互动、职场口才等)。
3. 以 JSON 数组形式输出,例如:["女装", "通勤穿搭", "轻熟风", "一衣多穿"]。
```
### 第二步:调用 `douyin-video-search` 技能获取视频
AI 将第一步生成的关键词,作为参数,**调用已安装的 `douyin-video-search` 技能**来获取视频列表。
**调用示例 (可移植):**
```
# AI应使用工具调用,而不是执行固定路径的脚本
# 以下为概念性示例,实际调用取决于AI对工具的封装
tools.douyin_video_search(keyword="科技公司 办公空间", sort="1", publish="7")
```
*AI应优先选择按点赞量排序 (`--sort "1"`) 的结果,以筛选高热度视频,并可使用分页参数 (`--cursor`) 获取更多结果。*
### 第三步:AI 筛选与输出案例
AI 对第二步获取的视频列表进行筛选,保留点赞量高、视觉风格独特、与项目定位匹配的视频,最终输出 **3 个以上的对标视频案例**,并附上简要分析。
**输出格式示例:**
```
## 对标视频案例推荐
### 案例 1:极简通勤穿搭|一衣多穿公式
- **视频链接**:https://www.douyin.com/video/1234567890
- **亮点分析**:视频开头3秒直接展示穿搭前后对比,节奏紧凑;画面干净高级,符合“极简风”定位。
- **可借鉴元素**:开头的痛点提问方式、快速切换的搭配镜头。
### 案例 2:小户型收纳|3招让空间大一倍
- **视频链接**:https://www.douyin.com/video/2345678901
- **亮点分析**:采用第一视角拍摄,代入感强;字幕设计清晰,信息密度高。
- **可借鉴元素**:第一视角运镜、关键信息字幕包装。
```
---
## 🛠️ 使用说明
### 输入
用户需提供一份包含战略信息的文本文件(作战地图、产品资料等)。
### 输出
一个包含至少 3 个对标视频的 Markdown 格式报告。
### 依赖技能
- **`skill:douyin-video-search`**: 必须已安装并在 `config.json` 中配置好 `tikhub_api_token`。
---
## ⚠️ 注意事项
- **动态调用**: AI 在执行第二步时,**必须**通过工具系统动态调用 `douyin-video-search` 技能,**绝不能**依赖或执行任何写死的本地文件路径。
- **关键词质量**: 第一步的关键词提取质量直接影响最终结果,AI 可与用户交互,对关键词进行确认或调整。Related Skills
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