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 ace3000chao/book2startup --skill customer-complaint-signalgit clone --depth 1 https://github.com/ace3000chao/book2startupWrote 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/ace3000chao/book2startup/customer-complaint-signal)<a href="https://agentmods.dev/skills/ace3000chao/book2startup/customer-complaint-signal"><img src="https://agentmods.dev/badge/skills/ace3000chao/book2startup/customer-complaint-signal/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/ace3000chao/book2startup/customer-complaint-signal"><img src="https://agentmods.dev/badge/skills/ace3000chao/book2startup/customer-complaint-signal.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 | $0.00150 | $0.02576 |
| Opus 5 | $0.00075 | $0.01288 |
| Sonnet 5 | $0.00030 | $0.00515 |
| Haiku 4.5 | $0.00015 | $0.00258 |
Grade A, and why
customer-complaint-signal 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Complaint Signal（客户投诉是最好的创新信号）
R — 原文 (Reading)
"Each senior leader formally ask customers questions that are more about gathering market intel, especially about competitors, than discerning whether they like your particular product."
— Verne Harnish, Scaling Up, 第1章 Overview
I — 方法论骨架 (Interpretation)
Harnish提出的市场情报收集原则与常规 wisdom 相反：与其问客户"喜不喜欢你的产品"，不如问"竞争对手在做什么"以及"客户在抱怨什么"。
为什么"喜不喜欢"这个问题价值有限？
客户的"喜欢"是滞后的情感指标——他们基于过去使用经验做判断，无法预见新可能的解决方案。更重要的是，客户通常不擅长表达他们真正需要什么（Henry Ford："如果你问顾客想要什么，他们会说是更快的马"）。
为什么"抱怨"比"喜欢"更有价值？
客户投诉是未被满足需求的直接信号。每一次投诉背后都藏着一个真实的问题，这个问题往往被投诉的表达方式掩盖了，但问题的存在是真实的。
两类高价值信号：
- 客户抱怨："你们的产品XXX太差了" → 背后是未被满足的需求
- 竞品动态：客户在流失给你的竞品 → 背后是竞品做对了什么
实操方法论：
- 高管亲自做客户访谈（每月至少2次），不是销售团队代劳
- 问题设计不是为了"满意度调研"，而是为了"情报收集"
- 特别关注客户提到的竞品——这比内部竞品分析更及时、更真实
A1 — 书中的应用 (Past Application)
案例 1: Intuit 的"客户在你家后院"深度访谈
- 问题: Intuit在1980年代是小型财务软件公司，不知道下一步产品方向
- 方法论的使用: CEO Scott Cook没有依赖传统调研公司，而是亲自去用户家里观察他们如何管理财务账目
- 发现：用户最头疼的不是"算账"，而是"找不到收据"
- 围绕"找不到收据"这个高频抱怨开发了QuickBooks
- 结论: 抱怨背后藏着产品方向，抱怨高频处即是市场机会所在
- 结果: QuickBooks成为小型企业财务软件市场领导者
案例 2: 某SaaS公司的"竞品流失预警"
- 问题: 销售团队发现好几个客户在续费时要求折扣，但没说原因；续费率开始下滑
- 方法论的使用: CEO没有直接打折，而是安排高管做流失客户访谈
- 发现：流失客户转向了竞品，原因是"竞品的报表功能更强"
- 内部研发评估：报表功能开发难度不大，但一直没有优先级
- 结论: 客户流失是竞品动态的预警信号，比任何内部报告都及时
- 结果: 快速迭代报表功能，续费率在Q3回升
案例 3: Amazon AWS 的"抱怨即需求"案例
- 问题: 2000年代初，开发者社区频繁抱怨IT基础设施部署复杂、成本高
- 方法论的使用: Bezos要求所有高管必须每月参加客户支持轮值，直接听客户投诉
- 发现：开发者抱怨的核心是"我不想管服务器，我只想用服务"
- 这直接导向了AWS的核心理念：基础设施即服务（IaaS）
- 结论: 大型创新往往源于听到了重复性、高频的抱怨，而非传统市场调研
- 结果: AWS开创了云计算行业，成为Amazon最赚钱的业务
A2 — 触发场景 (Future Trigger) ★
用户会在什么情境下需要这个 skill?
- 产品迭代方向不清晰 — 研发团队做了很多功能但客户使用率不高，不知道真正该做什么
- 竞争对手有新动作但反应总是慢半拍 — 内部竞品分析渠道有限，总是被市场推着走
- 客户满意度分数高但续费率低 — NPS和实际留存行为脱节，"满意但不买单"的假象
- 创新投入没有市场回报 — 做了很多创新项目但客户不买单，浪费了大量研发资源
- 高管与客户脱节 — 高管层不了解一线客户真实想法，只听汇报，信息层层衰减
语言信号 (用户的话里出现这些就应激活)
- "客户总是抱怨这个但不知道是不是真的重要"
- "创新没有方向"
- "我们的产品迭代靠老板拍脑袋"
- "竞争对手有什么动向我们总是最后一个知道"
- "客户流失了但不知道为什么"
- "调研说客户满意但他们还是走了"
What ships with it
1 file 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.
- 11d ago First seen · 169 lines · 150 tokens per session scan A 23accb33de19
customer-complaint-signal is a skill published in the GitHub repository ace3000chao/book2startup (80 stars, last pushed 4mo ago), licensed MIT. It adds 150 tokens to every session and 2,576 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
remindme
⏰ simple Telegram reminders for OpenClaw. cron, zero dependencies.
shaman-musk
A Chinese-language problem-solving method based on first principles: reducing a problem to basic facts, challenging assumptions, rebuilding the solution, checking feasibility, and implementing it.
frame-bold-signal
Bold colored card on a dark gradient — big section number, nav breadcrumb, orange card sliding in, title rising.
ai-augmentation-not-automation
Makes the strategic and moral case for AI augmentation over pure automation, providing leaders with frameworks for job redesign, creativity-driven AI deployment, and long-term workforce investment. Draws on the centaur model from chess and case studies across industries to demonstrate that human-AI collaboration…
creative-flow-and-inspiration
Teaches the experiential practice of courting creative inspiration, sustaining openness, creating conditions for breakthrough ideas, staying in creative mode continuously, harnessing spontaneity, practising connected detachment, and using play as a serious creative method. Use when seeking breakthrough product ideas…
appreciative-inquiry
Guides strengths-based organizational change using Cooperrider's Appreciative Inquiry 4D cycle: Discovery, Dream, Design, and Delivery. Surfaces peak experiences and positive patterns to amplify what works rather than fixing what is broken. Use when addressing team morale, redesigning processes, integrating…