安装核心技能包

代理已运行，现在给它们第一个真实任务。 BALIA OS 内置三个随时可部署的技能。

技能	用途
lightpanda	快速网页搜索与抓取
summarize	自动摘要长文本或网页
clawsec	安全监控与新技能隔离

如果你已在第四节下载了安装程序，可以直接使用。 （如果没有，请返回第四节下载 install_core_pack.py。）

让代理运行安装程序

> 你的智能体ID是`.env`文件中`AGENT_ID`的值。
openclaw agent --agent [你的代理ID] "请执行以下操作:
python install_core_pack.py --lang zh-CN
(The token will be read automatically from your .env file)
完成后向 Discord 汇报。"

代理将自动执行：

1. 验证令牌
2. 下载核心技能包（lightpanda / summarize / clawsec）
3. 部署到 ~/.opengoat/skills/
4. 更新 _SKILL_INDEX.md
5. 向 Discord 报告"核心技能包已安装"

收到 Discord 确认后，继续往下阅读。

---

什么是技能与Cassette

你的智能体已上线。它们拥有各自的身份、汇报流程和Discord频道。但目前，它们还不知道自己所处的业务领域——不知道自己是在管理一家餐厅、运营一个交易团队，还是在支持法律事务。

技能是你编写并提供给智能体的知识文件。每项技能教会智能体如何完成一项具体任务——如何管理预订、如何生成损益报告、如何起草SNS发帖。你给智能体的技能越多，它能做的事情就越多。

Cassette是围绕某一主题打包在一起的技能文件夹。 它代表一种运营模式。餐厅运营者可能拥有一个restaurant-ops cassette，内容创作者可能拥有youtube cassette和social-media cassette。同一个智能体可以根据当天的工作需要在不同cassette之间切换。

Cassette系统基于模式运作。 只需一个文件——ACTIVE_MODE.txt——就能告诉每个智能体当前激活的是哪个cassette。切换模式时，更新该文件即可。智能体在会话开始时读取该文件，并自动加载对应的技能。

这意味着：

• 今天：ACTIVE_MODE.txt显示youtube → 智能体加载YouTube技能
• 明天：你将其改为trade → 智能体加载交易技能
• 智能体本身不变，变的是模式。

而当你下周切换回youtube时，智能体会从上次中断的地方继续——LIVE_LEDGER记录了进度，KNOWLEDGE保存了积累的经验。每种模式都独立维护自己的记忆。

---

Cassette文件夹结构

INFO

~/.openclaw/workspace-[agentid]/ └── cassettes/ ├── ACTIVE_MODE.txt ← 一个词：当前激活的模式名称 ├── archive/ ← LIVE_LEDGER和KNOWLEDGE的溢出存档 ├── youtube/ │ ├── SKILL_YOUTUBE.md ← 该模式的技能文件 │ ├── WORKFLOW_YOUTUBE.md ← 分步操作流程 │ ├── LIVE_LEDGER_YOUTUBE.md ← 进度与下一步行动（每次会话后更新） │ └── KNOWLEDGE_YOUTUBE.md ← 积累的学习成果（每次会话后更新） ├── restaurant-ops/ │ ├── SKILL_RESTAURANT.md │ ├── WORKFLOW_RESTAURANT.md │ ├── LIVE_LEDGER_RESTAURANT.md │ └── KNOWLEDGE_RESTAURANT.md └── trade/ ├── SKILL_TRADE.md ├── WORKFLOW_TRADE.md ├── LIVE_LEDGER_TRADE.md └── KNOWLEDGE_TRADE.md

ACTIVE_MODE.txt 只包含一个词——当前激活的cassette文件夹名称。例如：

youtube

每次会话开始时，每个智能体读取ACTIVE_MODE.txt，然后从对应文件夹加载四个文件：SKILL、WORKFLOW、LIVE_LEDGER和KNOWLEDGE。LIVE_LEDGER告诉它们上次会话结束的位置，KNOWLEDGE提供该领域迄今为止积累的所有经验。

会话结束时，智能体更新LIVE_LEDGER（进度、下一项任务）和KNOWLEDGE（新发现的规律）。当任一文件超过800行时，RECENT部分将移至archive/——SUMMARY和PINNED部分始终保留在顶部。

计划任务（如每日健康检查或夜间报告）不受模式影响，始终执行——它们定义在智能体的AGENTS.md中，而非任何cassette中。

---

选择AI引擎

不同的cassette适合不同的AI模型。在构建cassette时一次性完成配置即可。

Cassette类型	推荐引擎	原因
加密货币交易 / 市场分析	Claude	逻辑推理、模式分析、细致判断
编程 / 技术自动化	Claude	代码质量、调试能力、多步骤逻辑
法律研究 / 合同审查	Claude	严谨推理、细腻语言表达
财务 / 会计 / 报告	Claude	结构化分析、精确计算
Google Sheets / Docs / NotebookLM / Drive	Gemini	原生Google服务集成
YouTube / 视频内容	Gemini	直接分析视频；与YouTube Studio集成
Gmail / 日历自动化	Gemini	深度Google Workspace集成
销售 / 推广 / 文案撰写	GPT-4o	自然对话、邮件生成
多语言沟通	GPT-4o或Gemini	强大的多语言表现
电商 / 产品研究	Claude或GPT-4o	研究深度、结构化输出

如果Claude满足你的使用需求，你已经准备就绪——它已在第3节中完成配置。

如需使用Gemini或GPT-4o，你需要在.env文件中添加对应的API密钥。具体操作说明见下方AI礼宾助手的第7步。

---

各行业技能参考

不确定要构建哪些技能？以下是各使用场景的参考起点。

餐饮 / 食品与饮料

• 预订管理与每日排班
• 供应商订单追踪与库存预警
• 客户SNS发帖（每日特色菜、活动信息）
• 评价监控与回复起草
• 每周营收摘要

电商 / 实体商品

• 商品详情页生成
• 竞争对手价格监控
• 订单状态汇总
• 客户咨询回复起草
• 月度销售报告

内容创作 / YouTuber

• 标题与缩略图创意生成
• 脚本大纲起草
• 上传计划管理
• 表现数据指标汇总
• 社区评论回复起草

自由职业者 / 顾问

• 客户沟通邮件起草
• 项目进度汇报
• 发票追踪与跟进
• 合同条款审查（标记异常条款）
• 工时记录汇总

小型企业 / 通用场景

• 每日运营摘要
• 员工沟通内容起草
• 法律问题初步筛查（标记需要律师处理的事项）
• 从收据和记录中生成财务摘要
• 供应商沟通管理

你的行业不在列表中？ 没关系——本节的AI礼宾助手将帮助你根据团队实际需求，从零开始定义专属技能。

---

本节使用方式

使用以下AI礼宾助手提示词来：

1. 定义智能体每天应完成的工作
2. 确定从哪个cassette名称和模式开始
3. 选择合适的AI引擎
4. 生成你的SKILL和WORKFLOW文件
5. 配置ACTIVE_MODE.txt标志文件
6. 验证智能体是否加载了正确的模式

将提示词粘贴到Claude、ChatGPT或Gemini中，并附上第2节中的团队设计文档。

---

▶ 将此粘贴到您的 AI 中以开始

发送前请附上你的团队设计文档。

You are helping me build Skills and a Cassette for BALIA OS —
a multi-agent AI operating system.

I have attached my Team Design Document. Read it before we begin
so you know my agent names, roles, and workspace structure.

CONCEPT TO UNDERSTAND BEFORE WE START:

Skills are knowledge files I write and give to my agents.
Each skill teaches an agent how to do a specific task.

A Cassette is a folder of skills, bundled around a theme (a mode).
My agents can switch between cassettes depending on what they 
need to work on.

The cassette system is mode-based. One file — ACTIVE_MODE.txt —
tells every agent which cassette is currently active.

Folder structure:
~/.openclaw/workspace-[agentid]/
  cassettes/
    ACTIVE_MODE.txt              ← active mode name (one word)
    archive/                     ← overflow for LIVE_LEDGER and KNOWLEDGE
    [mode-name]/
      SKILL_[MODENAME].md        ← skills for this mode
      WORKFLOW_[MODENAME].md     ← procedures for this mode
      LIVE_LEDGER_[MODENAME].md  ← progress, next actions (updated every session)
      KNOWLEDGE_[MODENAME].md    ← accumulated learning (updated every session)

At session start, each agent:
1. Reads ACTIVE_MODE.txt
2. Loads SKILL, WORKFLOW, LIVE_LEDGER, and KNOWLEDGE from that folder
3. Resumes from where LIVE_LEDGER says they left off
4. Applies accumulated learning from KNOWLEDGE
5. Operates in that mode for the session

At session end, the agent:
1. Updates LIVE_LEDGER (progress, next task)
2. Updates KNOWLEDGE (new patterns learned)
3. If either file exceeds 800 lines: moves RECENT section to archive/
   SUMMARY and PINNED always stay at the top

Scheduled tasks run regardless of mode — they are in AGENTS.md.

Guide me step by step. Ask one question at a time.
Wait for my answer before moving on.

STEP 1 — What I want my team to do
Ask me: "Describe what you want your AI team doing for you every day.
Don't think about how — just describe the output you want
and the work you want done."

Help me turn my answer into a list of at least 5 specific,
recurring tasks. Ask follow-up questions until we have
concrete tasks with a clear schedule (daily, weekly, on-demand).

STEP 2 — Map tasks to agents
Read my Team Design Document.
Match each task to the most appropriate agent based on their role.
Show me the mapping: "Task → Agent"
Ask: "Does this feel right? Anything you'd move?"

STEP 3 — Name the cassette (mode)
Based on the tasks, suggest a cassette name — the mode name
(short, lowercase, hyphens: e.g. restaurant-ops, youtube, trade).
This becomes the folder name and the value in ACTIVE_MODE.txt.
Ask me to confirm or suggest a different name.

STEP 4 — Choose the AI engine
For each agent with tasks mapped, recommend the best engine:

- Claude: logic, analysis, coding, legal, finance, crypto
- Gemini: Google services (Sheets, Docs, Drive, Gmail, Calendar,
  YouTube Studio, NotebookLM) — anything in the Google ecosystem
- GPT-4o: sales emails, outreach, copywriting, natural conversation

Explain your reasoning in one sentence per agent.
If all tasks work well with Claude, confirm no extra API keys needed.
If Gemini or GPT-4o needed, note it — we handle the key in Step 7.

Ask me to confirm before moving on.

STEP 5 — Generate SKILL_[MODENAME].md
Generate the skill file using this structure:

---
# SKILL_[MODENAME].md
Mode: [mode-name]
Version: 1.0
Last Updated: [today's date]
Primary Agent: [agent name]
AI Engine: [engine]

## What This Mode Does
[2-3 sentences: what domain this covers, what it produces]

## Skills in This Mode
[Bullet list of specific skills — what the agent knows how to do]

## Tools Available
[List tools usable in this mode: web search, file read/write,
Discord reporting, API calls, Google services, etc.]

## Operating Rules
1. Read ACTIVE_MODE.txt at session start. If it says [mode-name],
   load this file, WORKFLOW_[MODENAME].md, LIVE_LEDGER_[MODENAME].md,
   and KNOWLEDGE_[MODENAME].md immediately.
2. Resume work from where LIVE_LEDGER says you left off.
3. Apply patterns and lessons from KNOWLEDGE before starting tasks.
4. [Key rule specific to this domain]
5. [Key rule specific to this domain]
6. Report all completed tasks to Discord before ending session.
7. Never act outside this mode's scope without Commander approval.
8. Save all outputs to cassettes/[mode-name]/outputs/

## Session End Rules
At the end of every session, update both memory files:

LIVE_LEDGER_[MODENAME].md:
- SUMMARY: refresh the 3-5 line overview at the top
- CURRENT: update with today's progress and the next task
- RECENT LOG: append this session's activity
- If over 800 lines: move RECENT LOG to archive/

KNOWLEDGE_[MODENAME].md:
- SUMMARY: refresh the overview at the top
- PATTERNS: add any new success or failure patterns discovered today
- RECENT: append today's learnings
- If over 800 lines: move RECENT to archive/

## What This Mode Does NOT Handle
[Specific boundaries — what's out of scope for this mode]

## Output Format
[What the agent produces: reports, files, Discord messages, etc.]
---

Show it completely. Ask if anything needs adjustment.

STEP 6 — Generate WORKFLOW_[MODENAME].md
Generate the workflow file using this structure:

---
# WORKFLOW_[MODENAME].md
Mode: [mode-name]
Version: 1.0
Last Updated: [today's date]

## Mode Activation (Every Session Start)
1. Read cassettes/ACTIVE_MODE.txt — confirm it says [mode-name]
2. Load SKILL_[MODENAME].md
3. Read LIVE_LEDGER_[MODENAME].md — resume from last session
4. Read KNOWLEDGE_[MODENAME].md — apply accumulated learning
5. Report to Discord: "📦 Mode: [mode-name] active. Resuming: [current task from LIVE_LEDGER]."

## Task Procedures