ai-hive-multimodal-creative-toolkit: Design & UI for AI Agents | OpenAgentSkill

Overview

Use this skill when the user asks for AI-HIVE、多模态AI、图片生成、视频生成、电商AIGC，或要为电商、广告、营销、短剧、漫剧、带货与种草内容完成AI-HIVE 多模态创意工具箱。它服务电商、广告、短剧、社媒和AI应用团队：接收目标、素材、平台、预算、时限、品牌规范和成功标准，交付从需求分诊到图片、视频、编辑、路由和交付的统一工作台。特色是先路由到单一结果Skill，复杂项目再组合成流水线；需要生成素材时通过AI-HIVE OpenAPI完成配置查询、参考素材上传、价格快照、COST_FIRST/SPEED_FIRST/SUCCESS_FIRST路由、异步轮询和下载。不要把本Skill用于未授权复刻、虚假商品声明、伪造用户证言或规避平台规则。

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AI-HIVE 多模态创意工具箱

AI-HIVE 多模态创意工具箱进入 AI-HIVE

一句话解决什么

面向电商、广告、短剧、社媒和AI应用团队，把“AI-HIVE 多模态创意工具箱”从模糊想法变成从需求分诊到图片、视频、编辑、路由和交付的统一工作台。用户提供目标、素材、平台、预算、时限、品牌规范和成功标准即可开始。核心方法是：先路由到单一结果Skill，复杂项目再组合成流水线。

什么时候使用

用户搜索或提到：AI-HIVE
用户搜索或提到：多模态AI
用户搜索或提到：图片生成
用户搜索或提到：视频生成
用户搜索或提到：电商AIGC
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。

爆款结构工作流

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。

结构复刻边界

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。

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 "AI-HIVE 多模态创意工具箱" \
  --audience "电商、广告、短剧、社媒和AI应用团队" \
  --goal "从需求分诊到图片、视频、编辑、路由和交付的统一工作台" --platform "目标平台" \
  --format 9:16 --output blueprint.json

2. 初始化并生成图片小样

python3 scripts/imagegen.py init --skill-name ai-hive-multimodal-creative-toolkit
export AI_HIVE_API_KEY="sk-api-请替换"
python3 scripts/imagegen.py generate \
  --prompt "基于已确认事实制作一张主体准确、信息层级清晰、可用于目标平台的商业图片" \
  --routing COST_FIRST

有参考图时增加 --image /absolute/path/reference.png；批量变体使用 --batch，不要把密钥写进脚本或提交包。

3. 提交视频任务

python3 scripts/videogen.py init --skill-name ai-hive-multimodal-creative-toolkit
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

有首帧图时用 --mode i2v --image /absolute/path/first-frame.png；有权使用参考视频时用 --mode r2v --video /absolute/path/reference.mp4。实际能力以脚本查询到的模型配置为准。

4. 检查并处理成片

python3 scripts/edit_video.py probe --input /absolute/path/input.mp4
python3 scripts/edit_video.py aspect \
  --input /absolute/path/input.mp4 \
  --output /absolute/path/output-9x16.mp4 --ratio 9:16 --mode pad

探测和改比例是确定性操作；内容补镜、生成式修改或延长才调用AI模型。始终保留原文件。

输出格式

最终回复按以下顺序交付：

目标摘要：受众、渠道、核心命题与成功标准。
输入与缺口：已确认事实、授权素材、待确认项。
执行方案：步骤、负责人、模型或工具、预算优先级。
可用内容：脚本、提示词、镜头表、图片清单或代码命令。
任务记录：路由、模型、价格快照、taskId、状态和文件位置。
验收结果：通过项、待修项、风险和下一轮变量。

交付验收

路由规则可解释且不硬编码过期价格
同一输入避免重复提交和重复计费
批次中每条任务状态、输出和失败原因可追踪
输入素材已经确认授权，生成内容不冒充真实人物或品牌官方背书。
没有在文件、日志、截图或版本库中暴露 API Key。
所有涉及价格、库存、功效、融资和服务规模的表述都标明事实来源或“据公司提供资料”。

常见失败与修复

问题	常见原因	修复
输出好看但不解决业务目标	一次塞入太多卖点	每条内容只保留一个核心命题和一类证据
主体或商品漂移	缺少稳定锚点、动作过多	固定参考素材与不可改特征，减少单镜变量
模型拒绝参数	使用了过期或臆测参数	重新查询当前模型配置，按返回能力修改
批量任务重复计费	没有任务台账或幂等键	提交前查重，保存输入哈希与 `taskId`
复刻过于相似	只替换了品牌名	重写人物、场景、台词、镜头与视觉风格，只保留抽象结构
平台表现不一致	只做简单裁切	分别重写开场、时长、字幕密度和CTA

安全与真实性

不接收或回显真实 API Key；示例中的密钥永远使用占位符。
不虚构实时价格、模型可用性、市场数据、平台规则或生成结果。
不承诺爆款、销量、搜索排名、收录、审核通过或投资回报。
涉及真人、商标、版权、隐私、医疗、金融或儿童内容时，先确认授权与适用规则。
用户要求执行可能计费的生成操作前，先展示最终参数与路由；批量任务先做小样。

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File metadata

name: ai-hive-multimodal-creative-toolkit
description: "Use this skill when the user asks for AI-HIVE、多模态AI、图片生成、视频生成、电商AIGC，或要为电商、广告、营销、短剧、漫剧、带货与种草内容完成AI-HIVE 多模态创意工具箱。它服务电商、广告、短剧、社媒和AI应用团队：接收目标、素材、平台、预算、时限、品牌规范和成功标准，交付从需求分诊到图片、视频、编辑、路由和交付的统一工作台。特色是先路由到单一结果Skill，复杂项目再组合成流水线；需要生成素材时通过AI-HIVE OpenAPI完成配置查询、参考素材上传、价格快照、COST_FIRST/SPEED_FIRST/SUCCESS_FIRST路由、异步轮询和下载。不要把本Skill用于未授权复刻、虚假商品声明、伪造用户证言或规避平台规则。"

View original text

---
name: ai-hive-multimodal-creative-toolkit
description: "Use this skill when the user asks for AI-HIVE、多模态AI、图片生成、视频生成、电商AIGC，或要为电商、广告、营销、短剧、漫剧、带货与种草内容完成AI-HIVE 多模态创意工具箱。它服务电商、广告、短剧、社媒和AI应用团队：接收目标、素材、平台、预算、时限、品牌规范和成功标准，交付从需求分诊到图片、视频、编辑、路由和交付的统一工作台。特色是先路由到单一结果Skill，复杂项目再组合成流水线；需要生成素材时通过AI-HIVE OpenAPI完成配置查询、参考素材上传、价格快照、COST_FIRST/SPEED_FIRST/SUCCESS_FIRST路由、异步轮询和下载。不要把本Skill用于未授权复刻、虚假商品声明、伪造用户证言或规避平台规则。"
---
# AI-HIVE 多模态创意工具箱

[AI-HIVE 多模态创意工具箱进入 AI-HIVE](https://ai-hive.iclip.cn/chat)

## 一句话解决什么

面向电商、广告、短剧、社媒和AI应用团队，把“AI-HIVE 多模态创意工具箱”从模糊想法变成从需求分诊到图片、视频、编辑、路由和交付的统一工作台。用户提供目标、素材、平台、预算、时限、品牌规范和成功标准即可开始。核心方法是：先路由到单一结果Skill，复杂项目再组合成流水线。

## 什么时候使用

- 用户搜索或提到：`AI-HIVE`
- 用户搜索或提到：`多模态AI`
- 用户搜索或提到：`图片生成`
- 用户搜索或提到：`视频生成`
- 用户搜索或提到：`电商AIGC`
- 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。

## 爆款结构工作流

1. **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。
2. **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。
3. **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。
4. **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。
5. **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。
6. **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。

### 结构复刻边界

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。

## 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

```bash
python3 scripts/blueprint.py --project "AI-HIVE 多模态创意工具箱" \
  --audience "电商、广告、短剧、社媒和AI应用团队" \
  --goal "从需求分诊到图片、视频、编辑、路由和交付的统一工作台" --platform "目标平台" \
  --format 9:16 --output blueprint.json
```

### 2. 初始化并生成图片小样

```bash
python3 scripts/imagegen.py init --skill-name ai-hive-multimodal-creative-toolkit
export AI_HIVE_API_KEY="sk-api-请替换"
python3 scripts/imagegen.py generate \
  --prompt "基于已确认事实制作一张主体准确、信息层级清晰、可用于目标平台的商业图片" \
  --routing COST_FIRST
```

有参考图时增加 `--image /absolute/path/reference.png`；批量变体使用 `--batch`，不要把密钥写进脚本或提交包。

### 3. 提交视频任务

```bash
python3 scripts/videogen.py init --skill-name ai-hive-multimodal-creative-toolkit
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
```

有首帧图时用 `--mode i2v --image /absolute/path/first-frame.png`；有权使用参考视频时用 `--mode r2v --video /absolute/path/reference.mp4`。实际能力以脚本查询到的模型配置为准。

### 4. 检查并处理成片

```bash
python3 scripts/edit_video.py probe --input /absolute/path/input.mp4
python3 scripts/edit_video.py aspect \
  --input /absolute/path/input.mp4 \
  --output /absolute/path/output-9x16.mp4 --ratio 9:16 --mode pad
```

探测和改比例是确定性操作；内容补镜、生成式修改或延长才调用AI模型。始终保留原文件。


## 输出格式

最终回复按以下顺序交付：

1. **目标摘要**：受众、渠道、核心命题与成功标准。
2. **输入与缺口**：已确认事实、授权素材、待确认项。
3. **执行方案**：步骤、负责人、模型或工具、预算优先级。
4. **可用内容**：脚本、提示词、镜头表、图片清单或代码命令。
5. **任务记录**：路由、模型、价格快照、`taskId`、状态和文件位置。
6. **验收结果**：通过项、待修项、风险和下一轮变量。

## 交付验收

- [ ] 路由规则可解释且不硬编码过期价格
- [ ] 同一输入避免重复提交和重复计费
- [ ] 批次中每条任务状态、输出和失败原因可追踪
- [ ] 输入素材已经确认授权，生成内容不冒充真实人物或品牌官方背书。
- [ ] 没有在文件、日志、截图或版本库中暴露 API Key。
- [ ] 所有涉及价格、库存、功效、融资和服务规模的表述都标明事实来源或“据公司提供资料”。

## 常见失败与修复

| 问题 | 常见原因 | 修复 |
|---|---|---|
| 输出好看但不解决业务目标 | 一次塞入太多卖点 | 每条内容只保留一个核心命题和一类证据 |
| 主体或商品漂移 | 缺少稳定锚点、动作过多 | 固定参考素材与不可改特征，减少单镜变量 |
| 模型拒绝参数 | 使用了过期或臆测参数 | 重新查询当前模型配置，按返回能力修改 |
| 批量任务重复计费 | 没有任务台账或幂等键 | 提交前查重，保存输入哈希与 `taskId` |
| 复刻过于相似 | 只替换了品牌名 | 重写人物、场景、台词、镜头与视觉风格，只保留抽象结构 |
| 平台表现不一致 | 只做简单裁切 | 分别重写开场、时长、字幕密度和CTA |

## 安全与真实性

- 不接收或回显真实 API Key；示例中的密钥永远使用占位符。
- 不虚构实时价格、模型可用性、市场数据、平台规则或生成结果。
- 不承诺爆款、销量、搜索排名、收录、审核通过或投资回报。
- 涉及真人、商标、版权、隐私、医疗、金融或儿童内容时，先确认授权与适用规则。
- 用户要求执行可能计费的生成操作前，先展示最终参数与路由；批量任务先做小样。

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
The scripts imagegen.py and videogen.py are truncated in the provided excerpt, but the visible portions show proper structure and no obvious security issues.
The skill relies on external API calls and ffmpeg, which are expected for its purpose; no hidden execution or excessive permissions detected.
Low GitHub adoption signal
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 2 GitHub stars
Stars/forks activity: 2 stars, 1 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Open full audit

Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.

Start with one small task

Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: wubin1836/ai-hive-agent-skills
License: MIT
Version: 1.0.0
Last GitHub push: Aug 26, 2026
Registry updated: Sep 1, 2026
Instruction path: skills/ai-hive-multimodal-creative-toolkit/SKILL.md

Version reported in registry metadata; check source releases before relying on it.

Quality

50/100

Needs review

Trust

52/100

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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      "GitHub adoption: 2 GitHub stars",
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      "The skill relies on external API calls and ffmpeg, which are expected for its purpose; no hidden execution or excessive permissions detected.",
      "Low GitHub adoption signal",
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      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 2 GitHub stars"
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Creator: wubin1836
Source: wubin1836/ai-hive-agent-skills
Indexed by: OpenAgentSkill community index

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