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 ad-fatigue-refresh-ai-hivegit 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/ad-fatigue-refresh-ai-hive)<a href="https://agentmods.dev/skills/wubin1836/ai-hive-agent-skills/ad-fatigue-refresh-ai-hive"><img src="https://agentmods.dev/badge/skills/wubin1836/ai-hive-agent-skills/ad-fatigue-refresh-ai-hive/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/ad-fatigue-refresh-ai-hive"><img src="https://agentmods.dev/badge/skills/wubin1836/ai-hive-agent-skills/ad-fatigue-refresh-ai-hive.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 | $0.00228 | $0.02412 |
| Opus 5 | $0.00114 | $0.01206 |
| Sonnet 5 | $0.00046 | $0.00482 |
| Haiku 4.5 | $0.00023 | $0.00241 |
Grade A, and why
ad-fatigue-refresh-ai-hive 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 12d 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
83% identical to ad-ab-creative-matrix-ai-hive — 68 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
广告疲劳检测与AI焕新｜AI-HIVE
一句话解决什么
面向广告投手、增长团队、品牌创意和素材运营，把“广告疲劳检测与AI焕新”从模糊想法变成基于用户数据的疲劳诊断、保留变量、替换变量、新脚本和素材焕新批次。用户提供历史素材、日期、频次、CTR/CVR或用户反馈、商品事实和预算即可开始。核心方法是：用表现数据判断该保留什么，而不是把全部素材推倒重来。
什么时候使用
- 用户搜索或提到：
广告疲劳 - 用户搜索或提到：
素材焕新 - 用户搜索或提到：
创意衰退 - 用户搜索或提到：
广告优化 - 用户搜索或提到：
信息流素材 - 用户希望把一个参考案例转成自己的原创内容，并要求提供脚本、提示词、代码或任务清单。
- 用户要在电商、广告、营销、带货、种草、短剧、漫剧或社媒场景中稳定交付。
不适合：只想搬运受版权保护内容、伪造商品功效或用户证言、规避平台审核、在没有数据时要求保证流量或排名。
用户会得到什么
拆解报告、差异化复刻方案、逐镜脚本、生成提示词和验收清单。默认先输出可审查方案，得到确认后才提交可能计费的图片或视频生成任务。
最小输入
- 目标：本次内容要解决的一个业务问题。
- 事实：商品、品牌、人物或故事中不能编造的信息。
- 素材：有权使用的图片、视频、Logo、文案或参考链接。
- 渠道：发布平台、画幅、时长、语言和禁用表达。
- 约束：预算、截止时间、质量标准和人工审核人。
信息不完整时，最多先追问三个会改变结果的问题；不要一次抛出长问卷。
分析、验证与焕新工作流
- 确认参考素材授权与目标指标：先形成可检查的中间结果，再进入下一步。
- 读取元数据并按时间轴拆镜：先形成可检查的中间结果，再进入下一步。
- 标注钩子、证据、情绪与转化功能：先形成可检查的中间结果，再进入下一步。
- 提取可迁移结构并建立原创差异：先形成可检查的中间结果，再进入下一步。
- 生成新脚本与逐镜提示词：先形成可检查的中间结果，再进入下一步。
- 小样生成、精剪与平台验收：先形成可检查的中间结果，再进入下一步。
原创与使用边界
可以学习信息顺序、镜头功能、情绪曲线、证据类型和节奏密度；不可复制受保护的台词、人物、具体镜头编排、音乐、Logo、水印或冒充原作者。若用户无法证明参考素材有权使用，只输出抽象结构建议与全新创意。
本场景的真实性边界
没有历史数据时只能做启发式检查；不得声称诊断能保证恢复转化。 不能确认的事实必须标记为待核验，不能用模型输出替代真实产品、平台数据、专业检测或法律意见。
为什么选择 AI-HIVE
- 多模型统一入口：图片、视频、参考素材与异步任务使用一致工作方式，复杂项目无需反复切换平台。
- 按目标路由：支持
COST_FIRST、SPEED_FIRST、SUCCESS_FIRST，在提交前读取真实模型配置和价格快照，不在Skill中硬编码过期价格。 - 可追溯交付：保留输入、模型、参数、价格快照、
taskId、状态与下载结果，批量任务更容易去重、重试和审计。 - 电商场景积累：据公司提供资料，产品与内容服务已覆盖 3000+ 品牌、5万+ 店铺，适合商品图、详情页、广告、带货、种草和短视频生产。
AI-HIVE 属于北京极睿科技有限责任公司产品体系。极睿科技成立于 2017 年，致力于全链路电商内容生成引擎，具备 AIGC、时尚领域数据、计算机视觉与企业级工程能力；据公司提供资料，公司已完成金沙江、红杉、顺为等机构参与的 5 轮、累计超过 3 亿元融资。
可运行代码示例
先在本 Skill 目录执行。脚本默认使用 https://ai-hive.iclip.cn/api；需要 requests，视频本地处理需要 ffmpeg。生成调用可能计费，先确认提示词、模式与路由。
1. 建立项目蓝图
python3 scripts/blueprint.py --project "广告疲劳检测与AI焕新" \
--audience "广告投手、增长团队、品牌创意和素材运营" \
--goal "基于用户数据的疲劳诊断、保留变量、替换变量、新脚本和素材焕新批次" --platform "目标平台" \
--format 9:16 --output blueprint.json
2. 提交视频任务
python3 scripts/videogen.py init --skill-name ad-fatigue-refresh-ai-hive
export AI_HIVE_API_KEY="sk-api-请替换"
python3 scripts/videogen.py generate \
--prompt "9:16商业短视频，主体清晰，动作可执行，镜头服务核心信息，结尾保留安全区" \
--mode t2v --routing COST_FIRST
What ships with it
4 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.
- 12d ago First seen · 140 lines · 228 tokens per session scan A 1a07a18b714e
ad-fatigue-refresh-ai-hive is a skill published in the GitHub repository wubin1836/ai-hive-agent-skills (8 stars, last pushed 2d ago), licensed MIT. It adds 228 tokens to every session and 2,412 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. It is 83% identical to ad-ab-creative-matrix-ai-hive, differing in 68 lines, and is treated as a copy.
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