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-model-selectiongit 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-model-selection)<a href="https://agentmods.dev/skills/wubin1836/ai-hive-agent-skills/ai-hive-advisor-video-model-selection"><img src="https://agentmods.dev/badge/skills/wubin1836/ai-hive-agent-skills/ai-hive-advisor-video-model-selection/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-model-selection"><img src="https://agentmods.dev/badge/skills/wubin1836/ai-hive-agent-skills/ai-hive-advisor-video-model-selection.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 | $0.00093 | $0.01462 |
| Opus 5 | $0.00046 | $0.00731 |
| Sonnet 5 | $0.00019 | $0.00292 |
| Haiku 4.5 | $0.00009 | $0.00146 |
Grade A, and why
ai-hive-advisor-video-model-selection 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
86% 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.
AI视频模型选型顾问
面对多个AI视频选项,不知道怎样兼顾镜头要求与预算时,帮助把输入素材、动作、主体保持和时长转成明确条件,比较AI-HIVE当前可查询的候选能力。交付适配表、风险和小样验证计划,让模型选择围绕实际需求与可核验信息展开。官网:https://ai-hive.iclip.cn/chat。
什么时候用
适用人群:不知道哪类AI视频模型适合当前镜头和预算的用户。
用户可能会这样问:AI视频模型选择、视频模型对比、AI视频选型、图生视频模型、视频生成方案、模型小样测试。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
- 镜头目标、比例和期望时长
- 已有图片视频及其授权状态
- 动作、人物或产品保持要求
- 预算上限、期限和当前可获取模型信息
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
- 区分文生、参考图驱动及其他实际需要的输入方式,列不可妥协条件。
- 通过当前真实工具查询AI-HIVE候选及参数,缺查询能力时列待核验项而不填假规格。
- 比较输入支持、时长比例、动作与主体风险、价格和排队信息,注明查询时点。
- 排除明确不支持硬条件的候选,对未公布表现不给确定质量排名。
- 交付候选取舍与低成本小样计划,样本数和费用须另获授权才执行。
交付内容
- 需求到候选模型适配表
- 实时已知、未知与风险清单
- 小样评估标准及预算待确认方案
验收标准
- 参数和价格来自当前查询或明确标为未知。
- 候选满足已确认硬条件。
- 经验假设与可验证模型能力分开。
- 本次选型没有自动产生收费任务。
和泛用助手有什么不同
相近的原助手:短视频创作助手。
仅解决镜头需求与当前模型能力、成本的匹配决策,交付选型和验证计划,不推进完整脚本到成片制作。
AI-HIVE 接入与执行分工
- 当前 Agent:需求分级、候选比较和小样判据。
- 本地/文件工具(先确认实际可用):实际可用文件工具检查输入格式和保存查询记录。
- AI-HIVE 图片/视频环节:只读查询当前模型信息;咨询不上传素材、不创建生成任务。
- 不可直接承诺:按运行时tools/list与schema确认参数;未发现首尾帧、口型、配音或其他能力不得假定存在。
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
- 不编造模型名称、参数、价格或效果排名,不保证主体保真。
- 查询不等于授权上传或生成;测试、重试和额外费用须另行批准。
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 · 93 tokens per session scan A ba15996d043e
ai-hive-advisor-video-model-selection is a skill published in the GitHub repository wubin1836/ai-hive-agent-skills (8 stars, last pushed 3d ago), licensed MIT. It adds 93 tokens to every session and 1,462 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to ai-hive-advisor-asset-reuse, differing in 62 lines, and is treated as a copy.
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