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startup-validator启动验证器

Agent Skill

startup-validator 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

759

周安装

31

GitHub Stars

3

下载量

246
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:startup-validator(启动验证器)
来源仓库:https://github.com/alpoxdev/hypercore
仓库路径:skills/startup-validator
安装命令:
npx skills add https://github.com/alpoxdev/hypercore --skill startup-validator
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/alpoxdev/hypercore --skill startup-validator

简介

startup-validator 用于对创业想法进行证据驱动的可行性评分,减少乐观偏差。

  • 适用于决定继续推进、调整方向或准备客户验证前的评估。
  • 输出多文件结构化结果,便于后续参考与迭代。startup-validator 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 安装前应确认输入为具体产品想法而非泛化创意列表。
  • 注意不提供技术实现方案,仅聚焦市场与证据分析。

SKILL.md

@rules/evidence-and-scoring.md @references/frameworks.md

Startup Validator

Score startup ideas with explicit evidence and uncertainty instead of optimism. Organize results as a multi-file folder for future reference.

<when_to_use>

Use this skill when:

  • validating a new startup or product idea
  • deciding whether to proceed, pivot, or narrow the market
  • preparing for customer discovery or fundraising conversations

Do not use this skill when:

  • the main job is generating many new ideas
  • the request is technical implementation planning
  • the user wants first-principles redesign rather than validation scoring

Boundary:

  • use genius-thinking for broad ideation
  • use elon-musk for assumption teardown and redesign

Examples:

/startup-validator AI-based education service
/startup-validator subscription healthcare app
/startup-validator B2B purchasing automation

</when_to_use>

<input_check>

If $ARGUMENTS is missing, ask:

Which startup idea should we validate?

If founder, market, or customer evidence is missing, continue with explicit assumptions instead of inventing certainty.

</input_check>

<owned_job>

For each run:

  1. Restate the idea and identify the key unknowns.
  2. Score the idea using the framework set in references/frameworks.md.
  3. Mark evidence quality and uncertainty for each major claim.
  4. Summarize the strongest strengths, critical weaknesses, and next validation steps.

</owned_job>

<document_shape>

Output Structure

.hypercore/startup-validator/[topic-slug]/
├── flow.json           # phase tracking
├── thesis.md           # idea framing + key hypotheses
├── thiel-scores.md     # per-question rationale and scores
├── pmf-forces.md       # PMF checklist + Forces of Progress
└── verdict.md          # final score + grade + weaknesses + roadmap
  • Use ASCII kebab-case for [topic-slug] (e.g., ai-education-service).
  • Each phase produces its own file for organized reference.
  • flow.json tracks progress through phases. See references/flow-schema.md for the schema.
  • If the folder exists from a prior run, read existing files before updating.

</document_shape>

<flow_tracking>

Flow Tracking

Write flow.json at the start and update as each phase completes.

Phase progression

PhaseOutput fileNext
framethesis.md — idea thesis + 3 key hypothesesscore
scorethiel-scores.md — 7 questions with per-question rationalepmf
pmfpmf-forces.md — PMF checklist + Forces of Progress analysisverdict
verdictverdict.md — total score, grade, weaknesses, roadmapdone

Resume support

If flow.json already exists, read it and continue from the last incomplete phase. Do not restart completed phases.

</flow_tracking>

Workflow

PhaseTaskOutput file
1Frame the idea and missing contextthesis.md
2Score the Thiel questionsthiel-scores.md
3Check PMF and switching forcespmf-forces.md
4Apply penalties, summarize, recommend next actionsverdict.md

Scoring rule:

  • do not fabricate precise scores when evidence is thin
  • mark uncertain sections as provisional and explain what evidence would change them

<output_contract>

Each output file should follow the formats defined in the KO version's result_structure section:

  • thesis.md: one-line thesis, value hypothesis, growth hypothesis, key unknowns
  • thiel-scores.md: per-question score + rationale + improvement direction
  • pmf-forces.md: PMF checklist, Forces of Progress (Push/Pull/Habit/Anxiety), switching probability
  • verdict.md: total score/grade, critical weaknesses with severity, improvement roadmap (immediate/30d/90d)

</output_contract>

Before finishing, check:

  • unsupported certainty is called out as provisional
  • evidence quality is distinguished from opinion
  • the score ties back to named frameworks
  • the output includes concrete next validation actions
  • all output files are saved under .hypercore/startup-validator/[topic-slug]/
  • flow.json status is set to completed

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.93%
按下载量换算91

Claude

29.4%
按下载量换算72

Cursor

19.7%
按下载量换算48

Gemini CLI

9.85%
按下载量换算24

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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