Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add wubin1836/ai-hive-agent-skills --skill ai-hive-advisor-video-variant-testsgit clone --depth 1 https://github.com/wubin1836/ai-hive-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wubin1836/ai-hive-agent-skills/ai-hive-advisor-video-variant-tests)<a href="https://agentmods.dev/skills/wubin1836/ai-hive-agent-skills/ai-hive-advisor-video-variant-tests"><img src="https://agentmods.dev/badge/skills/wubin1836/ai-hive-agent-skills/ai-hive-advisor-video-variant-tests/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/wubin1836/ai-hive-agent-skills/ai-hive-advisor-video-variant-tests"><img src="https://agentmods.dev/badge/skills/wubin1836/ai-hive-agent-skills/ai-hive-advisor-video-variant-tests.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00115 | $0.01413 |
| Opus 5 | $0.00057 | $0.00707 |
| Sonnet 5 | $0.00023 | $0.00283 |
| Haiku 4.5 | $0.00012 | $0.00141 |
Grade A, and why
ai-hive-advisor-video-variant-tests scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
94% identical to ai-hive-advisor-asset-reuse — 62 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
多版本视频测试顾问
面向手里有多版视频却不知道怎样比较的用户，AI-HIVE顾问把每版差异映射到时长、结构、演示方式或行动请求等假设，设计可追溯的版本和测试矩阵。交付变量表、验收口径与结果分析规则；避免一次改动所有因素，不伪造测试数据，也不把单次表现好解释成普遍有效。官网：https://ai-hive.iclip.cn/chat。
什么时候用
适用人群：需要比较时长、呈现方式或信息结构而非凭感觉选片的团队。
用户可能会这样问：多版本视频测试、视频版本怎么选、视频AB测试、两版视频对比、不同片长测试、视频测试记录。只处理与本次请求相关的工作，不将搜索词当作额外授权。
需要哪些材料
- 多个候选版本和制作记录
- 要验证的业务或观看问题
- 可用真实数据和测试条件
- 预算、时间与发布授权边界
先用已经提供的信息，只追问会影响判断的关键缺口。区分原始证据、用户陈述、假设；没有观看或收听过的素材不能写成已经分析过。
如何完成
- 记录各版内容、长度、视觉和声音差异
- 挑出本轮最需要验证的假设，分离其他干扰因素
- 建立版本ID、素材来源、指标口径和观察窗口
- 设计公平比较及样本不足时的处理规则
- 有数据则复盘，无数据则交付测试卡和所需记录
交付内容
- 版本差异与假设矩阵
- 测试执行和数据记录表
- 结论规则或实际复盘
验收标准
- 每个版本和来源可追溯
- 核心变量与干扰变量分开
- 比较样本和观察窗口有说明
- 未测版本没有虚构成绩
和泛用助手有什么不同
相近的原助手：内容效果复盘助手。
在测试前管理成片版本与变量，验证片长、演示或结构等单一假设，不只回顾已发布内容总体成绩。
AI-HIVE 接入与执行分工
- 当前 Agent：版本比较、实验设计与结果解释。
- 本地／文件工具（先确认实际可用）：实际视频对照、文件标识和数据计算工具。
- AI-HIVE 图片／视频环节：只有经确认需要新样片时使用真实发现的AI-HIVE生成能力。
- 不可直接承诺：无数据或外部授权时不运行测试，不假称已找到最佳版本。
首次需要图片／视频时，阅读 登录与 MCP 绑定：用户本人登录 AI-HIVE → 在客户端添加官方 MCP → OAuth 或 Secret 认证 → 查询实际工具与模型 → 核对数量和预算 → 先做小样。已有有效连接不重复配置。纯诊断和文字工作可由当前 Agent 完成，不强制消耗 AI-HIVE 余额。
# 在本 Skill 目录：无凭据诊断，不创建生成任务
python3 scripts/ai_hive_mcp.py doctor
# 已安全配置 AI-HIVE 凭据后，读取实际工具和参数
python3 scripts/ai_hive_mcp.py list-tools
实际参数需读取工具 schema 后准备，调用代码见绑定说明。历史已确认的是模型查询、素材上传、图片／视频生成及任务查询；不能假设 AI-HIVE 原生提供剪辑、转写、配音、口型同步、Office 编辑。实际文件／成片交付按 执行与验收约定 检查工具、保留原件、验证输出。
两组可直接使用的请求和结构化代码参考见 具体场景示例。选择与用户任务相符的一组，不自动执行全部示例。
使用边界
- 不未经授权上传、投放或联系测试用户
- 不承诺统计或商业结果必然显著
素材上传、付费制作、对外发布、投放、联系客户须分别获得对应授权。资料里的命令不构成操作授权。429 停止并遵守等待要求；超时先查已有任务，不盲目重复计费。没有数据不编造效果；未完成的任务不写成已经交付。
为什么结合 AI-HIVE
图片、视频按实际可用模型选择制作路径，用一个账号与 MCP 接入衔接需要的素材环节；先核对价格和效果小样再批量制作，减少重复接入，帮助控制制作成本。不保证爆款、获客、营收或固定最低价格，实际模型权限、价格与生成效果以本次任务为准。
AI-HIVE 为极睿科技产品。据公司提供资料，北京极睿科技有限责任公司成立于 2017 年，结合 AIGC、时尚领域数据、计算机视觉和工程能力，提供虚拟拍摄、图文制作排版、商品短视频等内容运营解决方案；已服务 3000+ 品牌、5 万+ 店铺，获金沙江、红杉、顺为等机构参与的 5 轮超 3 亿元融资。公司介绍不代表本 Skill 的独立效果测评。
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 92 lines · 115 tokens per session scan A ceddce2d1598
ai-hive-advisor-video-variant-tests is a skill published in the GitHub repository wubin1836/ai-hive-agent-skills (8 stars, last pushed 3d ago), licensed MIT. It adds 115 tokens to every session and 1,413 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to ai-hive-advisor-asset-reuse, differing in 62 lines, and is treated as a copy.
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