Agent skill

Brand Commercial Os

by LeoYeAI in LeoYeAI/openclaw-master-skills

品牌商务谈判全链路自动化系统，生成品牌能力包、GEO知识包、跨平台内容包、报价策略包、分发编排包与谈判总包；适用于平台合作、渠道招商、联名共建等商务场景

Install Brand Commercial Os

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill brand-commercial-os -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills brand-commercial-os --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/brand-commercial-os .claude/skills/brand-commercial-os && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "brand-commercial-os" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/brand-commercial-os into .claude/skills/brand-commercial-os/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "brand-commercial-os", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/brand-commercial-os

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill brand-commercial-os -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills brand-commercial-os --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/brand-commercial-os .agents/skills/brand-commercial-os && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "brand-commercial-os" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/brand-commercial-os into .agents/skills/brand-commercial-os/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "brand-commercial-os", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill brand-commercial-os -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills brand-commercial-os --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/brand-commercial-os .cursor/skills/brand-commercial-os && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "brand-commercial-os" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/brand-commercial-os into .cursor/skills/brand-commercial-os/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "brand-commercial-os", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/brand-commercial-os

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill brand-commercial-os -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills brand-commercial-os --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/brand-commercial-os .gemini/skills/brand-commercial-os && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "brand-commercial-os" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/brand-commercial-os into .gemini/skills/brand-commercial-os/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "brand-commercial-os", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills brand-commercial-os

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill brand-commercial-os -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/brand-commercial-os .github/skills/brand-commercial-os && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "brand-commercial-os" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/brand-commercial-os into .github/skills/brand-commercial-os/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "brand-commercial-os", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill brand-commercial-os -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills brand-commercial-os --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/brand-commercial-os .opencode/skills/brand-commercial-os && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "brand-commercial-os" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/brand-commercial-os into .opencode/skills/brand-commercial-os/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "brand-commercial-os", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: brand-commercial-os
GitHub stars: 2.2k
Token cost: ~4.9k tokens
SKILL.md length: 582 words
Files: 7 (incl. scripts, references)
Skills in repo: 1,235
Repo updated: Jul 20, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Works in 3 steps: 接收用户输入 → 路由决策 → 调用 N2 Brand_Hub_Service
Sales & Support work in your project
SKILL.md covers 技能描述, 作者, 版本 and 分类, plus 8 more sections
Runs Python scripts from its folder

What it does

Brand Commercial Os is an agent skill from LeoYeAI/openclaw-master-skills. 品牌商务谈判全链路自动化系统，生成品牌能力包、GEO知识包、跨平台内容包、报价策略包、分发编排包与谈判总包；适用于平台合作、渠道招商、联名共建等商务场景

Its SKILL.md is about 4.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `_meta.json`, `references/brand_profile_template.json` and `references/faq_matrix_template.json`).

It sits in Sales & Support. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

Sales & Support work in your project

Example prompts

“/brand-commercial-os”

Requirements

Python 3

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

接收用户输入
路由决策
调用 N2 Brand_Hub_Service

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Brand Commercial Os loads about 4.9k tokens when it runs, and up to ~9.9k if it reads all its reference files. Until then it costs about 24 tokens; SKILL.md has 582 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~24
When it runs · the whole SKILL.md, loaded when a task matches: ~4.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~9.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 582 words, ~4,913 tokens.

Download SKILL.mdSave it as .claude/skills/brand-commercial-os/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.

name: brand-commercial-os
description: 品牌商务谈判全链路自动化系统，生成品牌能力包、GEO知识包、跨平台内容包、报价策略包、分发编排包与谈判总包；适用于平台合作、渠道招商、联名共建等商务场景

Brand Commercial OS（品牌商务中枢）

技能描述

把品牌谈判变成可复用的"品牌能力包 + GEO知识包 + 跨平台内容包 + 报价策略包 + 分发编排包 + 谈判总包"的一键生成系统。

作者

Coze AI

版本

1.0.0

分类

商业智能/品牌管理

Skill 总声明（必须遵守）

0.1 不删减原则

对用户输入与约束：不得删减、不得擅自精简、不得改写为抽象口号
信息不足处理：允许"基于假设"推进，但必须显式标注"假设区"，并输出"补齐清单"

0.2 角色边界

唯一对外接口：N1 Negotiation_Agent（智能经纪人）
调用限制：其他节点（N2/N3/N4）不得越级直接对话用户
品牌事实层：不得在未获得 Brand_Hub 的品牌事实层之前编造品牌事实

0.3 口径一致性

品牌信息：任何涉及"品牌是谁/能承诺什么/不能说什么/参数与资质"的输出必须来自 Brand_Hub 的事实层与证据层
跨平台内容：任何跨平台内容必须以 GEO 标准答案母本（standard_answer）与 FAQ 矩阵为知识底座，禁止漂移

0.4 合规与风险

禁止输出：诱导违规、虚假背书、无法验证的承诺、夸大数据
硬编数据拒绝：若用户要求"硬编数据/硬造背书"，必须拒绝，并给可替代方案（用真实可验证证据、或改成"建议指标/可选实验"）

Skill 外观（上架/对外介绍可直接用）

1.1 一句话

把品牌谈判变成可复用的"品牌能力包 + GEO知识包 + 跨平台内容包 + 报价策略包 + 分发编排包 + 谈判总包"的一键生成系统。

1.2 适用场景

平台生态合作
硬件厂出厂预装
联名共建
渠道招商
ToB企业解决方案合作
技能商店服务节点售卖
品牌内容系统化输出
GEO优化落地

1.3 交付物（一次运行至少产出 6 件）

O1 Brand_Profile_Summary（品牌档案摘要 + 版本）

品牌定位与差异化
核心能力与交付模式
事实层/证据层/禁忌清单
版本号与更新日志

O2 GEO_Knowledge_Pack（标准答案母本 + FAQ矩阵 + 高引用结构模板 + 技术抓取细节）

GEO核心概念与价值
标准答案母本（结构化）
FAQ矩阵（每条包含结论+解释+对比点+案例+边界）
高引用结构模板（标题公式/开篇/主体/结尾）
技术优化包（关键词/格式/多模态/Schema/权威背书）

O3 Cross_Platform_Content_Bundle（多平台内容包）

小红书：标题/正文/评论/话题标签
抖音：钩子/脚本/镜头建议/字幕
公众号：标题/大纲/全文/CTA
合作提案版：一页纸要点/演示脚本/FAQ

O4 Pricing_Package（报价方案包 A/B/C + 价格带 + 底线 + 可退让点）

机会等级评估（S/A/B/C）
A/B/C 三套方案（一次性费用/分成/包含项/排除项/条件/风险）
价格带（最低价/目标价/上限价）
谈判筹码（可退让点/必须坚持/交换条件）
谈判话术（价值证明/反压价/买断反制/收口推进）

O5 Distribution_Orchestration_Plan（分发编排：阶段节奏 + 渠道动作 + 资源位打法 + 数据回流）

阶段节奏（预热/首发/巩固/复盘）
分渠道计划（内容绑定/频率/资源位/KPI/追踪标签）
资源位打法（Banner/App入口/联名话题/线下联动）
数据回流钩子（指标/频率/解释/如何用于下一轮谈判）

O6 Negotiation_Master_Kit（经纪人谈判总包：脚本 + 提案要点 + 下一步动作）

开场话术
发现问题
定位话术
证明点
方案摘要
报价交付
异议处理（太贵/买断/排他/快速交付）
收口推进
需要对方提供的信息
后续材料清单

可选增强交付：

O7 One_Pager_Proposal（对外一页纸）
O8 Compliance_Risk_List（合规/风险清单）
O9 Metrics_Scoreboard_Draft（数据指标看板草案，用于复盘与下一轮提价）

Skill 节点架构

2.1 顶层 4 节点（对外可编排）

N1 Negotiation_Agent（智能经纪人｜唯一对外接口｜总控编排器 Orchestrator）

职责：
- 唯一对外接口，接收用户输入
- 意图识别与路由决策
- 调用 N2/N3/N4 并合流结果
- 生成谈判总包与下一步动作
输入：用户自然语言 + 标准输入对象（见 3.2）
输出：标准输出对象（见 3.3）+ 6件核心交付物

N2 Brand_Hub_Service（品牌中枢大服务｜对外统一API｜对内 Router + A/B/C）

职责：
- 对外提供统一API接口
- 对内路由到 BH-A/B/C 模块
- 维护品牌资产与知识库
内部模块（见 2.2）

N3 Pricing_Strategy_Service（报价策略服务）

职责：
- 机会等级评估
- 生成 A/B/C 三套报价方案
- 计算价格带与谈判筹码
- 生成谈判话术

N4 Distribution_Orchestrator（多渠道分发编排服务）

职责：
- 阶段节奏编排
- 分渠道行动计划
- 资源位打法设计
- 数据回流钩子设计

2.2 Brand_Hub 内部模块（对外不可见，只能由 N2 自己调度）

BH-R Router（内部路由层）

职责：根据 route 参数决定调用哪个内部模块
路由规则：
- route=INIT_BRAND_ASSETS → 只走 A（建档/版本）
- route=GEO_BRAND_QA → 只走 C（标准答案/FAQ）
- route=GENERATE_CROSS_PLATFORM_CONTENT → 只走 B（内容生成，但必须读取 A 的 brand_profile，且优先读取 C 的知识母本）
- route=MIXED → 先走 C（产出 standard_answer/FAQ）再走 B（把知识母本注入内容生产）

BH-A BrandAssets（品牌资产层）

职责：建档/更新/版本化/禁忌/术语表/证据层
产出：brand_profile（完整结构见 4.1）

BH-B ContentFactory（内容工厂层）

职责：跨平台内容生产（小红书/抖音/公众号/提案版）
产出：content_bundle（完整结构见 4.3）

BH-C GEOAdvisor（GEO权威问答层）

职责：标准答案母本/FAQ矩阵/引用结构/抓取细节
产出：geo_knowledge_pack（完整结构见 4.2）

2.3 严格调用关系（禁止越级）

用户/外部 → N1
    ↓
N1 →（按需）N2 / N3 / N4
    ↓
N2 内部 → Router → A/B/C

调用规则：

用户/外部只能通过 N1 交互
N1 可按需调用 N2/N3/N4
N2 内部由 Router 决定调用 A/B/C
N2/N3/N4 彼此不直接调用（避免耦合）
一切由 N1 编排合流

Skill 运行入口（对话触发与调用协议）

3.1 触发语（用户自然语言）

"把品牌总控工作流封装成一个大服务节点"
"我要跟某平台/硬件厂谈合作，给我完整链路"
"把产品手册改成 GEO 可被 AI 引用的问答知识包"
"要报价策略 + 分发计划 + 谈判脚本"
"把这些模块做成当前最火的 Skill 形式"

3.2 标准输入对象（N1 接收）

json

{
  "brand": {
    "brand_name": "string（品牌名称）",
    "brand_id": "string|null（品牌ID，若为新建则为null）",
    "company_legal_name": "string|null（公司法定名称）",
    "products_or_services": ["string|null（产品或服务列表）"],
    "industry": "string|null（所属行业）",
    "region": "string|null（地区）"
  },
  "goal": {
    "primary_goal": "string（主要目标）",
    "success_metrics": ["string|null（成功指标列表）"]
  },
  "partner": {
    "partner_type": "string（合作伙伴类型）",
    "partner_name": "string|null（合作伙伴名称）",
    "partner_size": "string|null（合作伙伴规模）",
    "partner_resources": ["string|null（合作伙伴资源列表）"]
  },
  "constraints": {
    "time_window": "string|null（时间窗口）",
    "budget_preference": "string|null（预算偏好）",
    "forbidden_claims": ["string|null（禁止声明列表）"],
    "tone_preference": "string|null（语气偏好）",
    "red_lines": ["string|null（红线清单）"]
  },
  "assets": {
    "official_materials": ["string|null（官方资料列表）"],
    "product_specs": ["string|null（产品规格列表）"],
    "existing_content": ["string|null（现有内容列表）"],
    "case_studies": ["string|null（案例研究列表）"]
  },
  "route": "string（路由类型：INIT_BRAND_ASSETS / GEO_BRAND_QA / GENERATE_CROSS_PLATFORM_CONTENT / MIXED / FULL_CHAIN）"
}

3.3 标准输出对象（N1 返回）

json

{
  "outputs": {
    "brand_profile_summary": {
      "brand_name": "string",
      "version": "string",
      "last_updated": "string（ISO 8601）",
      "positioning": "string",
      "core_capabilities": ["string"],
      "fact_layer": {
        "brand_identity": "string",
        "delivery_model": "string",
        "differentiation": "string"
      },
      "evidence_layer": ["string"],
      "forbidden_claims": ["string"],
      "update_log": ["string"]
    },
    "geo_knowledge_pack": {
      "geo_concepts": {
        "definition": "string",
        "value": "string",
        "key_benefits": ["string"]
      },
      "standard_answer": {
        "title": "string",
        "summary": "string",
        "sections": [
          {
            "heading": "string",
            "content": "string",
            "key_points": ["string"]
          }
        ],
        "faq_references": ["string"]
      },
      "faq_matrix": [
        {
          "question": "string",
          "answer": "string",
          "explanation": "string",
          "comparison": "string",
          "case_example": "string",
          "boundary": "string"
        }
      ],
      "high_citation_templates": {
        "title_formulas": ["string"],
        "opening_hooks": ["string"],
        "body_structures": ["string"],
        "closing_ctas": ["string"]
      },
      "technical_optimization": {
        "keywords": ["string"],
        "formatting": ["string"],
        "multimedia": ["string"],
        "schema_markup": "string",
        "authority_signals": ["string"]
      }
    },
    "content_bundle": {
      "xiaohongshu": {
        "title": "string",
        "body": "string",
        "comments": ["string"],
        "hashtags": ["string"]
      },
      "douyin": {
        "hook": "string",
        "script": "string",
        "shot_suggestions": ["string"],
        "subtitles": "string"
      },
      "wechat_official": {
        "title": "string",
        "outline": ["string"],
        "full_text": "string",
        "cta": "string"
      },
      "proposal_version": {
        "one_pager_points": ["string"],
        "presentation_script": "string",
        "faq": ["string"]
      }
    },
    "pricing_package": {
      "opportunity_grade": "string（S/A/B/C）",
      "package_a": {
        "name": "string",
        "one_time_fee": "number",
        "revenue_share": "number",
        "inclusions": ["string"],
        "exclusions": ["string"],
        "conditions": ["string"],
        "risks": ["string"]
      },
      "package_b": {
        "name": "string",
        "one_time_fee": "number",
        "revenue_share": "number",
        "inclusions": ["string"],
        "exclusions": ["string"],
        "conditions": ["string"],
        "risks": ["string"]
      },
      "package_c": {
        "name": "string",
        "one_time_fee": "number",
        "revenue_share": "number",
        "inclusions": ["string"],
        "exclusions": ["string"],
        "conditions": ["string"],
        "risks": ["string"]
      },
      "price_range": {
        "minimum": "number",
        "target": "number",
        "maximum": "number"
      },
      "negotiation_chips": {
        "concessions": ["string"],
        "must_holds": ["string"],
        "exchange_conditions": ["string"]
      },
      "negotiation_scripts": {
        "value_proof": "string",
        "price_pushback": "string",
        "buyout_counter": "string",
        "closing_advance": "string"
      }
    },
    "distribution_plan": {
      "phases": [
        {
          "phase_name": "string",
          "objectives": ["string"],
          "timeline": "string",
          "key_actions": ["string"]
        }
      ],
      "channel_plans": [
        {
          "channel": "string",
          "content_binding": "string",
          "frequency": "string",
          "resource_positions": ["string"],
          "kpis": ["string"],
          "tracking_tags": ["string"]
        }
      ],
      "resource_tactics": {
        "banner": ["string"],
        "app_entry": ["string"],
        "joint_topics": ["string"],
        "offline_activation": ["string"]
      },
      "data_feedback_hooks": {
        "metrics": ["string"],
        "frequency": "string",
        "interpretation": "string",
        "next_round_usage": "string"
      }
    },
    "negotiation_master_kit": {
      "opening_script": "string",
      "discovery_questions": ["string"],
      "positioning_script": "string",
      "proof_points": ["string"],
      "proposal_summary": "string",
      "quotation_delivery": "string",
      "objection_handling": {
        "too_expensive": "string",
        "buyout_request": "string",
        "exclusivity_demand": "string",
        "fast_delivery": "string"
      },
      "closing_advance": "string",
      "required_info_from_partner": ["string"],
      "next_materials": ["string"]
    }
  },
  "metadata": {
    "execution_time": "string（ISO 8601）",
    "route_used": "string",
    "outputs_generated": ["string"],
    "next_actions": ["string"]
  }
}

Skill 内部数据结构

4.1 Brand_Profile 完整结构（BH-A 产出）

json

{
  "brand_info": {
    "brand_name": "string",
    "brand_id": "string|null",
    "company_legal_name": "string|null",
    "industry": "string|null",
    "region": "string|null"
  },
  "positioning": {
    "brand_positioning": "string",
    "target_audience": ["string"],
    "unique_value_proposition": "string",
    "differentiation_points": ["string"]
  },
  "core_capabilities": {
    "products_services": ["string"],
    "delivery_model": "string",
    "key_strengths": ["string"],
    "certifications": ["string"]
  },
  "fact_layer": {
    "brand_identity": "string",
    "what_we_can_commit": ["string"],
    "what_we_cannot_commit": ["string"],
    "parameters": {
      "service_level": "string",
      "response_time": "string",
      "capacity": "string"
    }
  },
  "evidence_layer": {
    "case_studies": ["string"],
    "testimonials": ["string"],
    "performance_metrics": ["string"],
    "awards": ["string"]
  },
  "forbidden_claims": ["string"],
  "terminology": {
    "preferred_terms": ["string"],
    "avoided_terms": ["string"]
  },
  "version_control": {
    "version": "string",
    "last_updated": "string（ISO 8601）",
    "update_summary": "string",
    "changelog": ["string"]
  }
}

4.2 GEO_Knowledge_Pack 完整结构（BH-C 产出）

json

{
  "geo_concepts": {
    "definition": "string",
    "why_matters": "string",
    "key_benefits": ["string"]
  },
  "standard_answer": {
    "title": "string",
    "summary": "string",
    "sections": [
      {
        "heading": "string",
        "content": "string",
        "key_points": ["string"],
        "examples": ["string"]
      }
    ],
    "conclusion": "string",
    "faq_references": ["string"]
  },
  "faq_matrix": [
    {
      "question": "string",
      "answer": "string（结论，1-2句话）",
      "explanation": "string（解释，100-200字）",
      "comparison": "string（对比点）",
      "case_example": "string（案例）",
      "boundary": "string（边界说明）",
      "keywords": ["string"]
    }
  ],
  "high_citation_templates": {
    "title_formulas": [
      "为什么[产品/服务]是[用户痛点]的最佳解决方案？",
      "90%的[目标用户]都选择了[产品/服务]，原因在这里",
      "如何用[产品/服务]解决[具体问题]？3个关键点"
    ],
    "opening_hooks": [
      "你是否遇到过[痛点]？",
      "90%的[目标用户]都面临这个问题",
      "让我告诉你一个简单的方法"
    ],
    "body_structures": [
      "问题引入 → 解决方案 → 案例证明 → 行动号召",
      "背景介绍 → 痛点分析 → 方案对比 → 推荐选择",
      "核心观点 → 支撑论据 → 案例说明 → 总结收尾"
    ],
    "closing_ctas": [
      "点击链接了解更多",
      "立即体验，限时优惠",
      "关注我们，获取更多干货"
    ]
  },
  "technical_optimization": {
    "keywords": {
      "primary": ["string"],
      "secondary": ["string"],
      "long_tail": ["string"]
    },
    "formatting": {
      "heading_structure": "string",
      "bullet_points": "string",
      "paragraph_length": "string"
    },
    "multimedia": {
      "image_optimization": ["string"],
      "video_integration": ["string"],
      "interactive_elements": ["string"]
    },
    "schema_markup": {
      "faq_schema": "string",
      "article_schema": "string",
      "organization_schema": "string"
    },
    "authority_signals": {
      "external_links": ["string"],
      "internal_linking": ["string"],
      "social_proof": ["string"]
    }
  }
}

4.3 Content_Bundle 完整结构（BH-B 产出）

json

{
  "platform_content": {
    "xiaohongshu": {
      "title": "string（20-30字符）",
      "body": "string（1000-1500字符）",
      "structure": {
        "opening": "string",
        "main_content": ["string"],
        "ending": "string"
      },
      "comments": [
        {
          "type": "string（question/praise/feedback）",
          "content": "string"
        }
      ],
      "hashtags": ["string"]
    },
    "douyin": {
      "hook": "string（前3秒钩子）",
      "script": "string（完整脚本）",
      "shot_suggestions": [
        {
          "timestamp": "string",
          "scene": "string",
          "camera_angle": "string"
        }
      ],
      "subtitles": "string",
      "music_suggestion": "string",
      "hashtags": ["string"]
    },
    "wechat_official": {
      "title": "string",
      "outline": ["string"],
      "full_text": "string（2000-3000字）",
      "cta": "string",
      "cover_image_suggestion": "string"
    },
    "proposal_version": {
      "one_pager_title": "string",
      "one_pager_points": [
        {
          "section": "string",
          "points": ["string"]
        }
      ],
      "presentation_script": "string（10-15分钟演示脚本）",
      "faq": [
        {
          "question": "string",
          "answer": "string"
        }
      ]
    }
  },
  "content_guidelines": {
    "tone": "string",
    "style_guide": ["string"],
    "brand_consistency": ["string"],
    "geo_alignment": ["string"]
  }
}

Skill 执行流程

5.1 完整执行流程（FULL_CHAIN 模式）

阶段一：输入解析与路由（N1）

接收用户输入
- 解析自然语言意图
- 提取结构化输入参数
- 验证输入完整性
路由决策
- 根据 route 参数决定执行路径
- FULL_CHAIN 模式：完整执行所有节点
调用 N2 Brand_Hub_Service
- 传入 route=MIXED
- N2 内部路由：先 C（GEO）→ 再 B（内容）→ A（品牌资产）

阶段二：品牌知识生产（N2 内部）

BH-C GEOAdvisor 执行
- 生成 GEO 标准答案母本
- 构建 FAQ 矩阵
- 设计高引用结构模板
- 输出技术优化包
BH-B ContentFactory 执行
- 读取 BH-C 的 GEO 知识包
- 读取 BH-A 的品牌档案
- 生成跨平台内容（小红书/抖音/公众号/提案版）
- 确保内容与 GEO 标准答案对齐
BH-A BrandAssets 执行
- 建档/更新品牌档案
- 维护事实层与证据层
- 更新禁忌清单与术语表
- 版本化管理

Show full SKILL.md (222 more words)Show less

阶段三：策略生成（N3 & N4）

N3 Pricing_Strategy_Service 执行
- 评估机会等级（S/A/B/C）
- 生成 A/B/C 三套报价方案
- 计算价格带与谈判筹码
- 生成谈判话术脚本
N4 Distribution_Orchestrator 执行
- 规划分发阶段节奏
- 设计分渠道行动计划
- 规划资源位打法
- 设计数据回流钩子

阶段四：结果合流与总包生成（N1）

合流所有输出
- 从 N2 获取：品牌档案摘要、GEO知识包、内容包
- 从 N3 获取：报价方案包
- 从 N4 获取：分发编排计划
生成谈判总包（Negotiation_Master_Kit）
- 生成开场话术
- 生成发现问题和定位话术
- 整合证明点
- 编写方案摘要和报价交付话术
- 生成异议处理脚本
- 编写收口推进话术
- 列出需要对方提供的信息
- 整理后续材料清单
返回标准输出对象
- 打包所有 6 件核心交付物
- 添加执行元数据
- 提供下一步行动建议

Skill 使用指南

6.1 快速开始

最小化输入示例

json

{
  "brand": {
    "brand_name": "示例品牌"
  },
  "goal": {
    "primary_goal": "partner_coop"
  },
  "partner": {
    "partner_type": "platform"
  },
  "route": "FULL_CHAIN"
}

完整输入示例

json

{
  "brand": {
    "brand_name": "科技先锋",
    "brand_id": null,
    "company_legal_name": "先锋科技有限公司",
    "products_or_services": ["AI数据分析平台", "智能营销工具"],
    "industry": "科技",
    "region": "中国"
  },
  "goal": {
    "primary_goal": "partner_coop",
    "success_metrics": ["品牌曝光量", "用户转化率", "合作签约数"]
  },
  "partner": {
    "partner_type": "platform",
    "partner_name": "某电商平台",
    "partner_size": "top",
    "partner_resources": ["App首页Banner", "会员推荐位", "联合营销活动"]
  },
  "constraints": {
    "time_window": "Q2 2024",
    "budget_preference": "中等",
    "forbidden_claims": ["保证100%成功", "行业第一"],
    "tone_preference": "专业、创新、可靠",
    "red_lines": ["不得贬低竞争对手"]
  },
  "assets": {
    "official_materials": ["品牌手册.pdf", "产品介绍.pptx"],
    "product_specs": ["API文档.docx", "技术白皮书.pdf"],
    "existing_content": ["官网文案", "宣传视频"],
    "case_studies": ["某客户成功案例.pdf"]
  },
  "route": "FULL_CHAIN"
}

6.2 路由模式选择

INIT_BRAND_ASSETS

用途：仅初始化或更新品牌资产
执行：BH-A BrandAssets
产出：brand_profile

GEO_BRAND_QA

用途：仅生成 GEO 标准答案和 FAQ
执行：BH-C GEOAdvisor
产出：geo_knowledge_pack

GENERATE_CROSS_PLATFORM_CONTENT

用途：仅生成跨平台内容
前置：必须有 brand_profile 和 geo_knowledge_pack
执行：BH-B ContentFactory
产出：content_bundle

MIXED

用途：生成 GEO 知识包 + 跨平台内容
执行：BH-C → BH-B
产出：geo_knowledge_pack + content_bundle

FULL_CHAIN（推荐）

用途：完整执行全链路
执行：N1 → N2（A/B/C）→ N3 → N4 → N1（合流）
产出：6件核心交付物

6.3 输出使用建议

Brand_Profile_Summary

用于品牌对外介绍
用于合作伙伴初步了解
用于内部知识管理

GEO_Knowledge_Pack

用于 AI 问答系统训练
用于 FAQ 页面内容
用于客户服务知识库

Cross_Platform_Content_Bundle

直接发布到对应平台
根据实际反馈微调
保持多平台风格一致性

Pricing_Package

用于谈判准备
根据实际情况选择方案 A/B/C
参考价格带和谈判筹码

Distribution_Orchestration_Plan

按计划执行分发
定期检查数据回流
用于下一轮谈判复盘

Negotiation_Master_Kit

谈判前准备脚本
谈判中参考话术
谈判后跟踪执行

Skill 注意事项与最佳实践

7.1 合规要求

禁止输出诱导违规、虚假背书、无法验证的承诺、夸大数据
必须基于 Brand_Hub 的事实层与证据层输出
任何跨平台内容必须以 GEO 标准答案母本为知识底座

7.2 角色边界

N1 Negotiation_Agent 是唯一对外接口
N2/N3/N4 不得越级直接对话用户
所有调用必须通过 N1 编排

7.3 内容一致性

品牌信息必须来自 Brand_Hub 的事实层
跨平台内容必须与 GEO 标准答案对齐
避免品牌信息在不同平台漂移

7.4 质量控制

定期更新品牌档案
验证 GEO 知识包准确性
监控跨平台内容表现
根据数据回流优化策略

Skill 附录

A1 常见问题（FAQ）

Q1：如何更新品牌档案？ A：使用 route=INIT_BRAND_ASSETS，传入新的品牌信息即可。

Q2：如何只生成跨平台内容？ A：确保已有 brand_profile 和 geo_knowledge_pack，使用 route=GENERATE_CROSS_PLATFORM_CONTENT。

Q3：报价方案如何选择？ A：根据机会等级（S/A/B/C）和实际谈判情况，选择合适的方案 A/B/C，并参考价格带和谈判筹码。

Q4：如何评估合作伙伴类型？ A：根据合作伙伴的业务属性选择：platform（平台）、hardware_vendor（硬件厂）、enterprise_client（企业客户）、channel（渠道）。

Q5：数据回流如何用于下一轮谈判？ A：分析执行数据，评估策略效果，用实际成果证明价值，在下一轮谈判中调整报价和方案。

A2 术语表

术语	定义
GEO	Generative Engine Optimization，生成式引擎优化
Brand_Hub	品牌中枢服务，管理品牌资产与知识库
Negotiation_Agent	智能经纪人，唯一对外接口
Route	路由参数，决定执行路径
Opportunity Grade	机会等级，S/A/B/C 四级评估
Price Range	价格带，最低价/目标价/上限价

A3 参考资料

脚本文件：见 scripts/brand_commercial_os_core.py
品牌档案模板：见 references/brand_profile_template.json
GEO 标准答案模板：见 references/geo_standard_answer_template.md
FAQ 矩阵模板：见 references/faq_matrix_template.json
报价方案模板：见 references/pricing_package_template.json

版本历史

v1.0.0（2024-02-11）：初始版本发布

Files

SKILL.md and 6 other files (scripts, references) in skills/brand-commercial-os of LeoYeAI/openclaw-master-skills.

SKILL.md
_meta.json
references/brand_profile_template.json
references/faq_matrix_template.json
references/geo_standard_answer_template.md
references/pricing_package_template.json
scripts/brand_commercial_os_core.py

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Brand Commercial Os next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Questions about Brand Commercial Os

What does Brand Commercial Os do?

品牌商务谈判全链路自动化系统，生成品牌能力包、GEO知识包、跨平台内容包、报价策略包、分发编排包与谈判总包；适用于平台合作、渠道招商、联名共建等商务场景. Brand Commercial Os is an agent skill from LeoYeAI/openclaw-master-skills.

When should I use Brand Commercial Os?

Brand Commercial Os fits situations like: sales & Support work in your project.

How do I install Brand Commercial Os in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill brand-commercial-os -a claude-code`. Or copy the skill folder (skills/brand-commercial-os in LeoYeAI/openclaw-master-skills) into .claude/skills/brand-commercial-os in your project. Claude Code loads it when a task matches its description.

How do I install Brand Commercial Os in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill brand-commercial-os -a codex`. Or copy the skill folder (skills/brand-commercial-os in LeoYeAI/openclaw-master-skills) into .agents/skills/brand-commercial-os in your project. Codex loads it when a task matches its description.

Can I use Brand Commercial Os in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill brand-commercial-os -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/brand-commercial-os, .gemini/skills/brand-commercial-os, .github/skills/brand-commercial-os and .opencode/skills/brand-commercial-os in your project.

What does Brand Commercial Os need to run?

Going by SKILL.md and its folder, Brand Commercial Os needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Brand Commercial Os access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Brand Commercial Os safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Brand Commercial Os use?

Brand Commercial Os is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Brand Commercial Os use?

About 4.9k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5k tokens, read only when the agent opens those files.

What are the alternatives to Brand Commercial Os?

Skills that share tags, products or a category with Brand Commercial Os: Cold Outbound Optimizer (ericosiu/ai-marketing-skills, 3.6k stars), Doc Coauthoring (aws-samples/sample-strands-agent-with-agentcore, 195 stars), Amazon Buy Box Monitor (browser-act/skills, 6.1k stars) and Review Analysis (liangdabiao/amazon-sorftime-research-MCP-skill, 959 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Brand Commercial Os?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Brand Commercial Os

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Brand Commercial OS（品牌商务中枢）

技能描述

作者

版本

分类

标签

Skill 总声明（必须遵守）

0.1 不删减原则

0.2 角色边界

0.3 口径一致性

0.4 合规与风险

Skill 外观（上架/对外介绍可直接用）

1.1 一句话

1.2 适用场景

1.3 交付物（一次运行至少产出 6 件）

O1 Brand_Profile_Summary（品牌档案摘要 + 版本）

O2 GEO_Knowledge_Pack（标准答案母本 + FAQ矩阵 + 高引用结构模板 + 技术抓取细节）

O3 Cross_Platform_Content_Bundle（多平台内容包）

O4 Pricing_Package（报价方案包 A/B/C + 价格带 + 底线 + 可退让点）

O5 Distribution_Orchestration_Plan（分发编排：阶段节奏 + 渠道动作 + 资源位打法 + 数据回流）

O6 Negotiation_Master_Kit（经纪人谈判总包：脚本 + 提案要点 + 下一步动作）

可选增强交付：

Skill 节点架构

2.1 顶层 4 节点（对外可编排）

N1 Negotiation_Agent（智能经纪人｜唯一对外接口｜总控编排器 Orchestrator）

N2 Brand_Hub_Service（品牌中枢大服务｜对外统一API｜对内 Router + A/B/C）

N3 Pricing_Strategy_Service（报价策略服务）

N4 Distribution_Orchestrator（多渠道分发编排服务）

2.2 Brand_Hub 内部模块（对外不可见，只能由 N2 自己调度）

BH-R Router（内部路由层）

BH-A BrandAssets（品牌资产层）

BH-B ContentFactory（内容工厂层）

BH-C GEOAdvisor（GEO权威问答层）

2.3 严格调用关系（禁止越级）

Skill 运行入口（对话触发与调用协议）

3.1 触发语（用户自然语言）

3.2 标准输入对象（N1 接收）

3.3 标准输出对象（N1 返回）

Skill 内部数据结构

4.1 Brand_Profile 完整结构（BH-A 产出）

4.2 GEO_Knowledge_Pack 完整结构（BH-C 产出）

4.3 Content_Bundle 完整结构（BH-B 产出）

Skill 执行流程

5.1 完整执行流程（FULL_CHAIN 模式）

阶段一：输入解析与路由（N1）

阶段二：品牌知识生产（N2 内部）

阶段三：策略生成（N3 & N4）

阶段四：结果合流与总包生成（N1）

Skill 使用指南

6.1 快速开始

最小化输入示例

完整输入示例

6.2 路由模式选择

INIT_BRAND_ASSETS

GEO_BRAND_QA

GENERATE_CROSS_PLATFORM_CONTENT

MIXED

FULL_CHAIN（推荐）

6.3 输出使用建议

Brand_Profile_Summary

GEO_Knowledge_Pack

Cross_Platform_Content_Bundle

Pricing_Package

Distribution_Orchestration_Plan