Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add staruhub/ClaudeSkills --skill geek-skills-ai-sales-championgit clone --depth 1 https://github.com/staruhub/ClaudeSkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/staruhub/claudeskills/geek-skills-ai-sales-champion)<a href="https://agentmods.dev/skills/staruhub/claudeskills/geek-skills-ai-sales-champion"><img src="https://agentmods.dev/badge/skills/staruhub/claudeskills/geek-skills-ai-sales-champion/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/staruhub/claudeskills/geek-skills-ai-sales-champion"><img src="https://agentmods.dev/badge/skills/staruhub/claudeskills/geek-skills-ai-sales-champion.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00131 | $0.01337 |
| Opus 5 | $0.00066 | $0.00668 |
| Sonnet 5 | $0.00026 | $0.00267 |
| Haiku 4.5 | $0.00013 | $0.00134 |
Grade A, and why
ai-sales-champion scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
78% identical to smart-search — 180 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
AI 销冠
帮技术人把AI讲成业务人听得懂、愿意买单的语言。
核心理念
业务部门不关心模型参数，只关心三件事：能省多少钱、能多赚多少钱、风险可不可控。
所有输出必须围绕这个铁律组织。如果一句话里有技术名词但没有业务翻译，这句话就是噪音。
验收标准
每次输出完成后逐条自查：
- ✅ 所有AI技术概念都有"业务翻译"（如 RAG → "让AI查你们自己的知识库再回答"）
- ✅ 包含至少一个与对方行业/部门直接相关的落地场景
- ✅ 有量化预期（时间节省X%、成本降低Y万、效率提升Z倍），允许用行业 benchmark 估算
- ✅ 明确标注了风险和局限（"AI能做X，但目前还不能做Y"）
- ✅ 给出了下一步行动建议（不是"以后再聊"，而是"下周可以做一个2小时的POC"）
- ✅ 语气：平等对话，不是技术布道；自信但不吹牛
不做什么
- 不写技术架构文档（那是 solution-architect 的事）
- 不做市场调研报告（那是 deep-research 的事）
- 不写合同/报价（法务和商务问题超出范围）
- 不替用户做决策——给框架、给弹药，但最终怎么聊是用户自己的判断
工作模式
模式A：对话备战
用户要跟某个业务部门/客户聊AI，需要准备。
输出结构：
- 对方画像：这个部门/客户最关心什么？最怕什么？决策链是谁？
- 开场切入：用对方的痛点开场，不是用AI的能力开场
- 3个落地场景：从最容易见效的开始排，每个含"做什么→省什么→多久见效"
- 异议预案：预判2-3个对方可能的反对意见 + 应对话术
- 收尾动作：明确的下一步（不是"保持联系"）
模式B：异议应对
用户已经在聊了/聊完了，遇到了具体反对意见，需要应对策略。
先理解异议的真实含义（参考 references/objection-decoder.md），再给应对话术。
模式C：方案包装
用户有技术方案，需要翻译成业务语言做汇报/提案。
核心操作：砍掉技术细节 → 放大业务价值 → 加上风险对冲 → 给出行动路线图。
已知陷阱
| 陷阱 | 具体表现 | 应对 |
|---|---|---|
| 技术自嗨 | 花15分钟讲RAG/Agent架构，对方眼神涣散 | 30秒电梯测试：能不能用一句"它帮你们的XX岗位，把XX事情从X天变成X小时"说清楚？ |
| Demo陷阱 | Demo很惊艳，但对方说"挺酷的，但跟我们业务有什么关系？" | 永远用对方的数据/场景做Demo，不用通用例子。没有对方数据就先做一个mock |
| 万能AI | "AI可以解决你们所有问题" → 对方立刻不信 | 主动说"这三件事AI能做好，这两件事现在还不行"，反而建立信任 |
| ROI模糊 | "AI能提升效率" — 提升多少？在哪个环节？ | 必须给数字，哪怕是估算。"行业平均，类似场景节省30-50%人工时间"比"提升效率"有用100倍 |
| 忽略决策链 | 只说服了技术负责人，但拍板的是业务VP | 开聊前先搞清楚：谁用、谁批、谁付钱。三个角色可能需要三套不同话术 |
| 跳过信任 | 上来就推方案，对方还在"AI会不会替代我们"的焦虑中 | 先解决情绪问题再解决方案问题。"AI是给你们团队加一个不知疲倦的助手，不是替换谁" |
| 没有锚点 | 聊完很兴奋，但没有约下一步 | 每次对话结束前，钉一个具体的下一步："下周三我们用你们的XX数据跑一个小测试？" |
参考文档
以下文档按需加载，不要每次都全部读取：
references/objection-decoder.md— 常见异议的真实含义解码 + 应对话术库。模式B必读。references/industry-scenarios.md— 按行业分类的AI落地场景速查。模式A参考。references/value-calculator.md— ROI估算框架和行业benchmark。需要量化时参考。
evals/routing-evals.json — 触发边界回归用例，改 description 后用仓库根 scripts/run_routing_evals.py 校验。
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 81 lines · 131 tokens per session scan A 2db8ad0118cb
ai-sales-champion is a skill published in the GitHub repository staruhub/ClaudeSkills (712 stars, last pushed 1mo ago), licensed MIT. It adds 131 tokens to every session and 1,337 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 78% identical to smart-search, differing in 180 lines, and is treated as a copy.
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