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youtube-video-api-skill

This skill helps users automatically extract channel-level and video detail data from a specific YouTube channel via BrowserAct API. Agent should proactively apply this skill when users express needs like extracting channel video data, getting latest or popular videos from a YouTube channel, tracking competitor channel content, extracting video metrics such as views likes comments, retrieving subscriber count and channel info, monitoring posting cadence of a YouTube channel, gathering video data for content strategy analysis, getting earliest videos of a YouTube creator, analyzing engagement signals across a full channel, and downloading structured YouTube video details without manual scraping.

DeepseekModel 官方收录技能 质量 优秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=browser-act-skills-solutions-video-platforms-youtube-video-api-skill-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name youtube-video-api-skill description This skill helps users automatically extract channel-level and video detail data from a specific YouTube channel via BrowserAct API. Agent should proactively apply this skill when users express needs like extracting channel video data, getting latest or popular videos from a YouTube channel, tracking competitor channel content, extracting video metrics such as views likes comments, retrieving subscriber count and channel info, monitoring posting cadence of a YouTube channel, gathering video data for content strategy analysis, getting earliest videos of a YouTube creator, analyzing engagement signals across a full channel, and downloading structured YouTube video details without manual scraping. metadata {"openclaw":{"emoji":"🌐","requires":{"bins":"[Truncated]","env":"[Truncated]"}}} YouTube Video API Skill 📖 Introduction This skill provides users with a one-stop YouTube video data extraction service using BrowserAct's YouTube Video API template. It can directly extract structured channel-level data plus video detail data from a specific YouTube channel through a single API request. Just input the YouTube channel URL and video type (Latest, Popular, or Earliest), and you can get clean, ready-to-use video metrics. ✨ Features No hallucinations, ensuring stable and accurate data extraction : Pre-set workflows avoid generative AI hallucinations. No CAPTCHA issues : No need to handle reCAPTCHA or other verification challenges. No IP access restrictions and geo-blocking : No need to deal with regional IP restrictions. More agile execution speed : Compared to pure AI-driven browser automation solutions, task execution is faster. Extremely high cost-effectiveness : Significantly reduces data acquisition costs compared to AI solutions that consume a large number of Tokens. 🔑 API Key Guidance Flow Before running, you must check the BROWSERACT_API_KEY environment variable. If it is not set, do not take any other actions first. You should request and wait for the user to provide it collaboratively. The Agent must inform the user at this time : "Since you have not configured the BrowserAct API Key yet, please go to the BrowserAct Console first to get your Key." 🛠️ Input Parameters When calling the script, the Agent should flexibly configure the following parameters based on user needs: YouTube_channel_url Type : string Description : Target YouTube channel URL used to load the channel video list. Example : https://www.youtube.com/@BrowserAct Video_type Type : string Description : Which ordering mode to use when traversing the channel video list. Optional Values : Latest Popular Earliest Default : Popular 🚀 Invocation Method The Agent should implement "one command gets results" by executing the following independent script: # Invocation example python -u ./scripts/youtube_video_api.py "YouTube_channel_url" "Video_type" ⏳ Running Status Monitoring Since this task involves automated browser operations, it may take a long time (several minutes). The script will continuously output status logs with timestamps (e.g., [14:30:05] Task Status: running ) while running. Agent Instructions : While waiting for the script to return results, please keep an eye on the terminal output. As long as the terminal is still outputting new status logs, it means the task is running normally. Do not misjudge it as a deadlock or unresponsiveness. If the status remains unchanged for a long time or the script stops outputting and no result is returned, the retry mechanism can be considered. 📊 Data Output Description After successful execution, the script will parse and print the results directly from the API response. The results include: Channel fields channel_title : Channel name displayed on the channel page channel_url : Channel URL subscribers : Subscriber count shown on the channel page Video fields video_title : Video title shown on the video page video_url : Video URL publish_date : Published date or time shown on YouTube view_count : View count shown on YouTube video_duration : Video duration comment_count : Total number of comments (if available) like_count : Like count (if available) ⚠️ Error Handling & Retry During script execution, if an error occurs (such as network fluctuation or task failure), the Agent should follow the logic below: Check the output content : If the output contains "Invalid authorization" , it means the API Key is invalid or expired. At this time, do not retry , but guide the user to recheck and provide the correct API Key. If the output does not contain "Invalid authorization" but the task execution fails (for example, the output starts with Error: or the return result is empty), the Agent should automatically try to execute the script once more . Retry limits : Automatic retry is limited to once . If the second attempt still fails, stop retrying and report the specific error information to the user. 🌟 Typical Use Cases Competitor Tracking : Track performance trends and posting cadence of a competitor's channel. Creator Research : Analyze engagement signals and popular videos of content creators. Content Ops Reporting : Monitor channel videos and performance metrics for reporting. Growth Analytics : Understand what video types (Latest/Popular) drive growth. Database Automation : Send channel videos directly into CRM or databases without manual export. Market Research : Aggregate video metrics across different channels in a specific industry. Trend Spotting : Identify the most popular videos on specific tech or gaming channels. Audience Engagement Analysis : Correlate subscriber counts with video views and likes. Content Strategy : Review a channel's earliest videos to understand their origin and growth path. Automated Social Monitoring : Keep tabs on new content released by key industry leaders.
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下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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