Skill Brand Onboarding logo

Skill Brand Onboarding

OrganizationPopular
ZJU-REAL
skill-brand-onboarding

创作者/品牌入驻:通过结构化访谈收集视觉风格、内容调性、受众画像和运营目标,生成完整的账号画像档案。 当用户说"账号入驻""建档案""新账号建立画像""品牌入驻""从零建号""完善账号信息""onboarding"时使用。 产出覆盖多维度的完整账号档案;只建声音画像用 skill-voice-builder,只建受众画像用 skill-audience-profiler。

Overview

PublisherZJU-REAL
RepositoryEasel
Skill nameskill-brand-onboarding
Stars
1.2K
Forks
175
Bundled files
2
LicenseApache-2.0
Links
  • 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.

  • 2 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 Brand Onboarding AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. 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.
2

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-brand-onboarding .claude/skills/skill-brand-onboarding
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skill Brand Onboarding 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 Brand Onboarding 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 Brand Onboarding 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.

品牌入驻

通过结构化访谈 + 公开信息采集,为创作者/品牌生成完整的 Easel 账号画像(Profile)。

输入

用户提供品牌/账号名称,以及可选的社媒链接、截图、品牌资料。

输出

profiles/<name>/ 目录,包含:

文件内容
identity.md品牌名、定位、使命、差异化、核心产品/服务
style.md视觉风格、语气调性、内容节奏、Do/Don't 规则
audience.md目标人群画像、用户语言、痛点与需求
platforms.md活跃平台、账号信息、发布频率、标签策略
preferences.md内容支柱、主推产品、禁区话题、合规底线
memory.md初始为空,后续由归因层更新

Phase 0 — 环境准备

  1. 询问画像名称(英文小写,用于目录名,如 my-brand
  2. 检查 profiles/<name>/ 是否已存在:
    • 已存在 → 摘要现有内容,询问:更新还是重建?
    • 不存在 → 继续
  3. 确保 profiles/<name>/ 目录存在

Phase 1 — 信息采集

先采集公开信息,再问用户补缺口。

步骤 1:收集社媒链接

向用户询问(有哪些提供哪些):

  • 小红书 / 抖音 / B站 / 微博主页链接或 ID
  • 个人网站 / 公众号名称
  • 已有的品牌手册、VI 文件、截图(可提供文件路径)

步骤 2:公开信息提取

对每个链接使用 WebFetch 抓取公开页面,提取:

可确认的事实(标注来源):

  • 品牌名、账号昵称、简介/签名
  • 所在地、服务范围
  • 产品或服务品类
  • 品牌价值观(如简介中有声明)
  • 社媒数据:粉丝数、获赞与收藏、笔记/视频数
  • 视觉观察:封面风格、滤镜偏好、排版习惯、主色调

WebFetch 无法获取的信息标记为待确认缺口。

仅用户能回答的缺口:

  • 精确品牌色(hex 值)、字体名称
  • 目标人群描述(ICP)
  • 主推产品/服务、核心差异化
  • 社媒运营目标、当前运营现状
  • 标志性内容格式和真实文案示例
  • 绝对不做的事

Phase 2 — 预填访谈文档

生成面向用户的访谈文档,写入 outputs/品牌名/品牌入驻.md

文档四部分:

第一部分 — 我们已经了解的 将 Phase 1 确认的事实以陈述形式呈现,让用户核对纠正。

"以上信息是否准确?有无遗漏或需要纠正的?"

第二部分 — 需要你来回答的(仅真正缺口)

  1. 目标用户是谁?(ICP)
  2. 主推产品/服务?
  3. 和同类账号最大的不同?
  4. 社媒核心目标?(涨粉 / 带货 / 品牌认知 / 社群 — 选 1-2 个)
  5. 目前运营节奏?什么效果好/不好?

第三部分 — 素材清单 必须:品牌色值、Logo、产品实拍图(高清原图) 有则更好:场景图、品牌手册、代表性帖子截图、欣赏/想避开的账号

第四部分 — 品牌与内容细节

  • 文字排版偏好、标志性内容格式
  • 3-5 条真实文案示例(标注"最有价值的输入")
  • 内容支柱(勾选 + 自定义)
  • 绝对不发的内容、内容形式比例、近期重要节点

根据品牌调性调整文档语气。


Phase 3 — 素材与回复审核

用户返回填写的文档和素材后:

  1. 素材处理 — Logo → profiles/<name>/assets/logo.png;产品图 → assets/products/;场景图 → assets/lifestyle/;示例帖子 → assets/examples/
  2. 回复整合 — 将用户回答与 Phase 1 采集合并,识别剩余缺口
  3. 补充确认 — 如有关键缺口,针对性追问(不超过 3 个问题)

Phase 4 — 生成画像档案

将所有信息综合写入 profiles/<name>/ 下各文件。

profile-templates.md 中的模板结构生成六个文件:

  • identity.md — 基本信息、核心产品、差异化、内容方向
  • style.md — 语气调性、视觉风格、标志性格式、文案示例、Do/Don't
  • audience.md — ICP、用户语言、痛点需求、互动特征
  • platforms.md — 各平台账号数据、内容形式、发布频率、标签
  • preferences.md — 内容支柱、主推产品、禁区、合规底线、运营目标
  • memory.md — 初始为空模板

未获得的信息标记为 [待补充],不编造。从截图估算的标注 (估算)


Phase 5 — 确认定稿

向用户展示生成的完整画像,逐文件确认:

  1. 有没有事实错误?
  2. 有没有不适用的部分需要删除?
  3. 有没有遗漏需要补充?

修改完成后确认:

"画像已保存至 profiles/<name>/。Easel 的所有 SKILL 将自动读取此画像。"


操作备注

  • 必须先采集公开信息再生成访谈文档 — 预填已知信息体现专业度,用户也能更快完成
  • 标志性内容格式和真实文案示例是最重要的输入 — 让生成内容像本人而非通用 AI 的关键
  • 品牌色是第二重要的视觉输入 — 色值错了所有视觉产出都不对,估算的标注"(估算)"
  • 不编造品牌细节 — 未获得的信息写 [待补充]
  • 受众和运营目标不能跳过 — 没有 ICP 和目标,后续内容策划都会泛泛而谈

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 Brand Onboarding AI skill do?

创作者/品牌入驻:通过结构化访谈收集视觉风格、内容调性、受众画像和运营目标,生成完整的账号画像档案。 当用户说"账号入驻""建档案""新账号建立画像""品牌入驻""从零建号""完善账号信息""onboarding"时使用。 产出覆盖多维度的完整账号档案;只建声音画像用 skill-voice-builder,只建受众画像用 skill-audience-profiler。

Why use Skill Brand Onboarding on TypingMind?

Because you install it once and use it with any model. Skill Brand Onboarding 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 Brand Onboarding in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ZJU-REAL/Easel/tree/main/skills/openclaw/skill-brand-onboarding. 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 Brand Onboarding?

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 Brand Onboarding?

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

Is the Skill Brand Onboarding 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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