Skill Collab Proposal

OrganizationPopular

ZJU-REAL

skill-collab-proposal

品牌合作方案与联名策划。当用户提到"品牌合作"、"商单方案"、"合作报价"、"联名"、"联动方案"、 "博主合作"、"品牌植入"、"商务合作"、"合作提案"时触发。支持两种模式：商单方案（品牌找上门，基于 KOL 定价表生成报价、内容形式、排期、KPI 预估）和联名策划（与博主/品牌联动，生成双方优势互补、内容分工、互推策略）。

Overview

Publisher	ZJU-REAL
Repository	`Easel`
Skill name	`skill-collab-proposal`
Stars	1.2K
Forks	175
Bundled files	3
License	Apache-2.0
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
3 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by ZJU-REAL on GitHub. Read the source before you install it.

Installation

Install the Skill Collab Proposal AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/ZJU-REAL/Easel.git /tmp/Easel
mkdir -p .claude/skills
cp -r /tmp/Easel/skills/openclaw/skill-collab-proposal .claude/skills/skill-collab-proposal

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skill Collab Proposal in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Skill Collab Proposal on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Skill Collab Proposal is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

合作/联名方案生成

根据合作需求，查询 KOL 定价表计算报价区间，用平台系数公式预估 KPI，输出可直接发送给品牌方或合作方的结构化提案。

输入

参数	必填	说明
合作方信息	是	品牌名称/合作方名称、产品或服务描述
合作模式	否	明确指定 A（商单）或 B（联名），未指定时自动判断
预算范围	否	品牌方预算上限，用于反向匹配内容形式
补充材料	否	品牌 brief、竞品案例、过往合作数据

自动判断规则： 含"报价""商单""品牌植入""接广告"走模式 A；含"联名""联动""博主合作""互推"走模式 B。

输出

Markdown 格式方案文档，保存到 outputs/主题名/collab-proposal.md。两种模式的完整输出结构模板见 references/proposal-templates.md：

模式 A（商单）：账号概况 / 合作形式与报价 / 内容创意 / KPI 预估 / 排期 / 条款建议
模式 B（联名）：双方概况对比 / 受众互补 / 联合内容方案 / 互推策略 / 执行排期 / 预期效果

执行步骤

1. 解析意图与提取信息

识别输入判断走模式 A（商单）还是模式 B（联名）
提取关键信息：品牌/合作方名称、产品方向、合作诉求、预算
缺少关键信息时主动追问，不编造品牌名或产品信息

2. 读取 Profile 数据

identity.md：账号名称、粉丝量、内容垂类、过往商单经验
platforms.md：各平台粉丝数、近 30 天互动率、主力平台
audience.md：受众画像（年龄、性别、地域、消费力）
无 Profile 时留占位符，提示用户补充（见"Profile 感知"节）

3. 确定 KOL 层级

根据 identity.md 粉丝量，查 references/kol-pricing-guide.md 第 1 节层级表（素人 / KOC / 腰部 / 头部 / 顶流）。后续步骤均基于此层级。

4. 计算报价区间（模式 A）

查 references/kol-pricing-guide.md 第 2 节，得该层级 + 平台 + 内容形式的基础报价区间
按第 3 节的调整因子（垂直度/时效节日/独家/二创/多平台等）叠加溢价
计算：调整后报价 = 基础报价 x (1 + 各项溢价之和)
用第 4 节 CPE 公式反向校验合理性，超出合理区间时标注警告

5. 预估 KPI

按 references/kol-pricing-guide.md 第 5 节公式逐层计算，每个数值标注计算过程：

预估曝光 = 粉丝数 x 平台曝光系数 x 内容类型系数（系数查第 5.1 节）
预估互动 = 预估曝光 x 平台互动率基线（查第 5.2 节）
预估转化 = 预估互动 x 内容转化率（查第 5.3 节；无转化链路的曝光类不强估，标注"以曝光为主"）
输出区间（保守值/乐观值），不给单一数字

6. 设计内容方案

推荐 2-3 种内容形式，每种标注：形式（图文/短视频/长视频/直播/混合）、时长或篇幅、创意方向和植入方式（软植入/硬广/口播/场景植入）、脚本概要（3-5 句）
根据 audience.md 受众偏好排序推荐优先级

7. 制定排期

制作周期：脚本确认 → 拍摄/制作 → 品牌审核 → 发布
推荐发布时间：避开竞品密集期，匹配节日/热点
标注审核预留时间（通常 3-5 个工作日）

8. 联名策划补充步骤（模式 B）

对比双方账号数据（粉丝量/互动率/受众画像），用表格呈现
估算受众重叠度：同平台同垂类高重叠（40-60%），跨平台或跨垂类低重叠（10-25%）
设计内容分工矩阵：谁出镜/谁剪辑/谁的号首发/评论区互动脚本
双方各自 KPI 预估（复用步骤 5 的公式）

9. 保存产物

保存到 outputs/合作方案名/collab-proposal.md
文件顶部标注生成时间、适用模式、数据来源

Profile 感知

有 Profile 时

从 identity.md 读账号名称、粉丝量、垂类定位、过往商单经验
从 platforms.md 读各平台粉丝数和互动率，确定主力平台
从 audience.md 读受众年龄/性别/地域/消费力，用于内容方向匹配
报价基于实际粉丝量查表计算，KPI 基于实际互动率公式推导
内容形式推荐匹配账号擅长的类型

无 Profile 时

账号概况留占位符 [请补充：平台/粉丝量/互动率]
报价给该内容形式全层级通用区间，标注"仅供参考，需据实际数据调整"
KPI 预估用行业中位数，标注"基于行业平均值，实际可能有较大偏差"
末尾附注："如提供账号 Profile（含 identity.md / platforms.md / audience.md），可生成精准报价和 KPI 预估"

规则

报价必须有据可查 — 每个报价数字须能追溯到 references/kol-pricing-guide.md 的层级表 + 调整因子，禁止凭空编造
KPI 必须标注公式 — 每个预估值旁标注计算过程（如"10 万粉 x 0.3 系数 = 3 万曝光"）
区间优于单值 — 报价和 KPI 一律给保守值-乐观值区间
缺信息时追问，不编造 — 品牌名、产品信息、预算等关键字段缺失时必须追问
CPE 校验必做 — 报价算完必须用 CPE 公式反向验证，不合理时标注警告
内容形式至少 2 个 — 每种标注优劣
转化预估需谨慎 — 无直接转化链路的合作不强行预估转化率
禁止使用破折号（em dash）

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Skill Collab Proposal AI skill do?

Why use Skill Collab Proposal on TypingMind?

Because you install it once and use it with any model. Skill Collab Proposal is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Skill Collab Proposal in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ZJU-REAL/Easel/tree/main/skills/openclaw/skill-collab-proposal. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Skill Collab Proposal?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Skill Collab Proposal?

As many as you like. As long as a model supports skills, you can use Skill Collab Proposal with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Skill Collab Proposal AI skill free?

Yes. It is published on GitHub by ZJU-REAL under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

合作/联名方案生成

输入

输出

执行步骤

1. 解析意图与提取信息

2. 读取 Profile 数据

3. 确定 KOL 层级

4. 计算报价区间（模式 A）

5. 预估 KPI

6. 设计内容方案

7. 制定排期

8. 联名策划补充步骤（模式 B）

9. 保存产物

Profile 感知

有 Profile 时

无 Profile 时

规则

Bundled files

Frequently asked questions

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