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Opc strategy

Skill opcspace/opcstartup-skill/opc-strategy

AI 一人公司从验证到盈利的执行型 Agent Skills|OPCspace 旗舰项目

Install
npx -y skills add opcspace/opcstartup-skill --skill opc-strategy

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What its author says it does

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AI One-Person Company (OPC) strategy framework for niche selection, MVP validation, and business model design. Use when user is choosing an AI startup direction, validating a product idea, screening business opportunities, or designing pricing and revenue models for a solo AI business. Triggers include "AI创业选方向" "AI赛道" "MVP验证" "商业模式" "定价策略" "AI副业选什么" "一人公司做什么".

SKILL.md

4.2 KB, ~1.5k tokens by cl100k_base, as published. Nobody here has run it

OPC Strategy: 赛道 · MVP · 商业模式

这个 skill 帮新手回答三个问题:卖什么、卖给谁、怎么证明对方愿意付钱。不要从产品功能开始,先从付费用户和痛点开始。

快速诊断

用户状态当前任务推荐入口合格产出
有多个想法选出最值得验证的一个赛道自评打分表TOP1赛道和放弃理由
已选方向找到真实痛点和付费人赛道三维度模型10人访谈记录
已有方案做7天付费验证MVP验证闭环MVP和≥3个付费意向
不知道定价设计低风险付费探针商业模式设计三档报价和首单价格

新手工作流

  1. 写一句话定位:我帮助 [目标人群] 用 AI 解决 [高频痛点],让他们获得 [可衡量结果]
  2. 评估3个候选赛道:每个赛道都用需求刚性、技术成熟度、变现清晰度打分。
  3. 访谈10个目标用户:只问过去行为,不问“你会不会用”。
  4. 设计1个最小交付:只解决一个具体问题,3天内能演示。
  5. 提出付费测试:价格可以低,但必须真实收钱或确认付款动作。
  6. 做Go/No-Go决策:≥3人愿意付费才进入增长阶段。

统一评分标准

assets/toolkit-01-niche-evaluation.md 是主标准。L/T/M 只是解释维度,不再单独做另一套判断。

维度权重新手判断
需求刚性35%用户是否已经为这个问题花钱、花时间或承担损失
技术成熟度30%现有 AI 是否能稳定完成70%以上交付
变现清晰度35%30天内是否能找到决策人并收第一笔钱
总分决策
≥80值得进入7天验证
60-79只做低成本验证,不投入开发
<60暂停或换赛道

硬性否决:变现清晰度低于60分,或找不到明确付费人群,即使总分高也不要继续。

7天付费验证

天数动作产出
Day 1-2完成10人名单和5个访谈痛点排序、原话摘录
Day 3-4做MVP或服务样例演示链接、样例报告、交付流程
Day 5-6向目标用户提出付费测试¥99-199试用、定金或明确付款承诺
Day 7复盘付费意愿Go/延长验证/No-Go

判断标准:不是用户“觉得不错”,而是愿不愿意付钱、给数据、排时间或介绍同类客户。

商业模式选择

形态适合新手吗适用场景
标准化服务最推荐AI文案、数据分析、客服搭建、自动化咨询
模板/工具包推荐已有重复交付经验,可产品化
订阅服务验证后推荐客户每月都有持续需求
纯SaaS不建议一开始做开发、获客、客服、留存复杂度高
高度定制项目谨慎客单高但交付重,容易困在服务陷阱

必须避免

  • 为了“看起来像产品”先做官网、Logo、复杂系统。
  • 用问卷代替真实访谈。
  • 把免费试用人数当作付费验证。
  • 进入自己没有行业理解、没有客户触达渠道的赛道。

配套工具

工具用途位置
赛道自评打分表三维度量化评分assets/toolkit-01-niche-evaluation.md
MVP验证检查清单7天验证任务追踪assets/toolkit-02-mvp-checklist.md
定价计算器成本+竞品+价值三维定价assets/toolkit-03-pricing-calculator.md

Gives 0 of the 12 instructions most roadmap strategy skills give in ~1.5k tokens

Counted across 591 of the 672 authors here whose files we hold, read 2026-08-07

  • read product marketing context before asking questionsin 21 of 591, across 10 files
  • base price on perceived value, not costin 15 of 591, across 4 files
  • compact after finalizing a planin 14 of 591, across 9 files
  • differentiate tiers using features, limits, or supportin 14 of 591, across 3 files
  • use Van Westendorp to find acceptable price rangein 13 of 591, across 2 files
  • use MaxDiff to identify highly valued featuresin 13 of 591, across 2 files
  • map topics to buyer journey stagesin 12 of 591, across 6 files
  • Extract domain capabilities and classify subdomainsin 11 of 591, across 1 file
  • Define bounded contexts around consistency and ownershipin 11 of 591, across 1 file
  • Establish a ubiquitous language glossary and anti-termsin 11 of 591, across 1 file
  • Capture context boundaries in ADRs before implementationin 11 of 591, across 1 file
  • Open the strategic design template if neededin 11 of 591, across 1 file

Said here and by no other author read

  • start from paying users not features
  • score 3 niches on 3 weighted dimensions
  • interview 10 target users
  • design a 3 day deliverable MVP
  • propose a real paid test
  • make a go or no go decision

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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