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elon-musk埃隆·马斯克

Agent Skill

elon-musk 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

888

周安装

37

GitHub Stars

3

下载量

296
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:elon-musk(埃隆·马斯克)
来源仓库:https://github.com/alpoxdev/hypercore
仓库路径:skills/elon-musk
安装命令:
npx skills add https://github.com/alpoxdev/hypercore --skill elon-musk
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 elon-musk

简介

elon-musk 引导从第一性原理重新审视约束条件,打破类比思维惯性。

  • 适用于成本、架构或战略决策陷入常规思路时的根本性重构需求。
  • 输出组织为多文件文件夹形式,便于后续参考与迭代优化。
  • 不适用于单纯事实调研或标准实施方案制定,聚焦突破性创新场景。
  • elon-musk 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

@rules/execution.md @references/frameworks.md

Elon Musk

Rebuild decisions from first principles instead of inheriting analogy-driven defaults. Organize results as a multi-file folder for future reference.

<when_to_use>

Use this skill when:

  • the current options all look like slight variations of the same idea
  • cost, structure, or strategy feels constrained by habit
  • you need to separate real constraints from inherited assumptions

Do not use this skill when:

  • the job is mostly factual research with no redesign
  • the request is standard implementation planning
  • the user needs startup validation scoring rather than assumption teardown

Boundary:

  • if the main job is startup scoring and readiness assessment, use startup-validator
  • if the main job is broad idea generation, use genius-thinking

Examples:

/elon-musk SaaS pricing is crowded and undifferentiated
/elon-musk infrastructure cost is 40% of revenue
/elon-musk churn is high and retention playbooks are failing

</when_to_use>

<input_check>

If $ARGUMENTS is missing, ask:

Which problem should we deconstruct using first principles?

</input_check>

<owned_job>

For each run:

  1. Restate the problem and desired outcome.
  2. Gather facts only where the answer depends on current evidence.
  3. Classify assumptions using the A/B/C model from references/frameworks.md.
  4. Rebuild options from the surviving fundamentals.
  5. Stress-test the best path with inversion and pre-mortem.

</owned_job>

<document_shape>

Output Structure

.hypercore/elon-musk/[topic-slug]/
├── flow.json           # phase tracking
├── research.md         # domain research (conventions + facts + innovation cases)
├── assumptions.md      # A/B/C matrix with Socratic questioning
├── redesign.md         # rebuilt options from fundamentals + comparison table
└── execution.md        # inversion + pre-mortem + action plan
  • Use ASCII kebab-case for [topic-slug] (e.g., saas-infra-cost).
  • 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
researchresearch.md — industry conventions, actual facts, innovation casesdeconstruct
deconstructassumptions.md — A/B/C matrix with Socratic questioningredesign
redesignredesign.md — rebuilt options + current vs first-principles comparisonexecute
executeexecution.md — inversion, pre-mortem, action sequencedone

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
1Clarify the decision, gather domain researchresearch.md
2Identify constraints, conventions, unknowns (A/B/C matrix)assumptions.md
3Rebuild alternatives from fundamentalsredesign.md
4Stress-test the best path, recommend next actionsexecution.md

Research rule:

  • if the problem depends on current facts, gather and cite them
  • if the problem is conceptual, do not force web or team workflows

<output_contract>

Each output file should include:

  • research.md: industry conventions, actual data/benchmarks, innovation cases (with source URLs)
  • assumptions.md: A/B/C matrix (A=physical constraints, B=conventions to eliminate, C=needs verification), Socratic questioning applied
  • redesign.md: current vs first-principles comparison table, 3-5 alternative paths with feasibility/impact scores
  • execution.md: inversion failure scenarios (5-7), pre-mortem analysis, prioritized action sequence

</output_contract>

Before finishing, check:

  • conventions are separated from true constraints
  • unknowns that need evidence are labeled explicitly
  • the recommendation does not depend on obsolete tool or model names
  • inversion or pre-mortem was used to challenge the preferred path
  • all output files are saved under .hypercore/elon-musk/[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

33.49%
按下载量换算99

Claude

29.54%
按下载量换算87

Cursor

17.52%
按下载量换算52

Gemini CLI

10.16%
按下载量换算30

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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