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-ab-creative-matrix-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-ab-creative-matrix-ai-hive)<a href="https://agentmods.dev/skills/wubin1836/ai-hive-agent-skills/ad-ab-creative-matrix-ai-hive"><img src="https://agentmods.dev/badge/skills/wubin1836/ai-hive-agent-skills/ad-ab-creative-matrix-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-ab-creative-matrix-ai-hive"><img src="https://agentmods.dev/badge/skills/wubin1836/ai-hive-agent-skills/ad-ab-creative-matrix-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 | $0.00219 | $0.02522 |
| Opus 5 | $0.00110 | $0.01261 |
| Sonnet 5 | $0.00044 | $0.00504 |
| Haiku 4.5 | $0.00022 | $0.00252 |
Grade A, and why
ad-ab-creative-matrix-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.
Copies of this mod
8 near-identical copies found in the catalogue:
- ad-creative-failure-diagnosis-ai-hive — 91% identical, 68 lines differ
- ai-hive-multimodal-creative-toolkit — 89% identical, 38 lines differ
- ad-hook-variant-generator-ai-hive — 89% identical, 66 lines differ
- ad-creative-score-rewrite-ai-hive — 88% identical, 68 lines differ
- ai-comic-drama-full-workflow-ai-hive — 88% identical, 58 lines differ
- ai-content-disclosure-check-ai-hive — 86% identical, 54 lines differ
- ad-first-frame-animation-ai-hive — 86% identical, 68 lines differ
- ad-fatigue-refresh-ai-hive — 83% identical, 68 lines differ
How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
爆款广告A/B创意矩阵｜AI-HIVE
一句话解决什么
面向高频投放商家、广告优化师和素材生产团队，把“爆款广告A/B创意矩阵”从模糊想法变成钩子×证据×场景×CTA矩阵、最小测试批次、生成任务和命名规则。用户提供商品事实、受众、目标指标、预算、历史素材和可测试变量即可开始。核心方法是：一次只改变一个主要变量，让测试结果可以解释。
什么时候使用
- 用户搜索或提到：
A/B广告 - 用户搜索或提到：
创意矩阵 - 用户搜索或提到：
素材测试 - 用户搜索或提到：
广告变体 - 用户搜索或提到：
投放素材 - 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
需求分诊、模型或Skill路由、成本与时延策略、任务队列和回退方案。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。
批量路由与测试工作流
- 把需求归类为分析、图片、视频、编辑或组合任务：先形成可检查的中间结果，再进入下一步。
- 确定质量、时限、预算和成功条件：先形成可检查的中间结果，再进入下一步。
- 选择COST_FIRST、SPEED_FIRST或SUCCESS_FIRST：先形成可检查的中间结果，再进入下一步。
- 先执行最小样本并记录快照：先形成可检查的中间结果，再进入下一步。
- 扩大批次并限制并发：先形成可检查的中间结果，再进入下一步。
- 失败分类、回退与人工抽检：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
不得把小样结果推广为因果结论；测试规模和统计判断由广告团队确认。 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 "爆款广告A/B创意矩阵" \
--audience "高频投放商家、广告优化师和素材生产团队" \
--goal "钩子×证据×场景×CTA矩阵、最小测试批次、生成任务和命名规则" --platform "目标平台" \
--format 9:16 --output blueprint.json
2. 初始化并生成图片小样
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.
- 12d ago First seen · 152 lines · 219 tokens per session scan A 1c9dfa6361bb
ad-ab-creative-matrix-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 219 tokens to every session and 2,522 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…