gaetano-crux-capital-research-model

name	gaetano-crux-capital-research-model
description	Build and apply a Gaetano / Crux Capital-style photonics, optical networking, Physical AI, and AI infrastructure research model from public X posts, public Substack pages, company coverage notes, earnings transcripts, technical papers, and market datasets. Use when an agent needs to map the photonics stack, identify AI infrastructure chokepoints, separate public thesis work from paid/private content, or create a trader-specific research skill for portable agent platforms such as Claude Code, OpenClaw, Codex-style skill systems, or other local AI agent runtimes.
quantSkills	{"organization":"https://github.com/quantskills","repository":"quantskills/skill-gaetano-crux-capital-research-model","repository_url":"https://github.com/quantskills/skill-gaetano-crux-capital-research-model","project_type":"skill","collection":"trader-research-models","license":"GPL-3.0","category":"analyst","tags":["ai-infrastructure","photonics","optical-networking","physical-ai","research-model"],"platforms":["claude-code","codex","openclaw","cursor"],"status":"stable","validation_level":"listed","maintainer_type":"official","summary_zh":"基于公开资料复刻 Gaetano / Crux Capital 的研究方法：把公开 X 帖子、公开 Substack 页面、财报与技术论文，拆解成「光子堆栈定位 → chokepoint 识别 → 证据分级 → 催化与风险跟踪」的结构化研究模型。","summary_en":"Research-model skill for public-material analysis of photonics, optical networking, Physical AI, and AI infrastructure themes.","requires":["skill-x-trader-builder"]}

{
  "version": 1,
  "task": {
    "placeholder": "请粘贴或说明待分析的公开帖子、公司、技术主题或资料范围",
    "required": true
  },
  "fields": [
    {
      "key": "focus",
      "label": "研究重点",
      "type": "text",
      "placeholder": "例如：CPO、光模块、Physical AI、传感器或仿真软件"
    }
  ],
  "prompt_template": "{{#task}}任务与材料：\n{{task}}\n\n{{/task}}{{#attachments}}用户上传的材料（已放入工作区）：\n{{attachments}}\n\n{{/attachments}}基于公开材料重建 Gaetano / Crux Capital 风格的研究逻辑{{#focus}}，重点分析 {{focus}}{{/focus}}；完成基础设施主题界定、光子与 AI 基础设施堆栈定位、瓶颈识别、证据分级、市场低估原因、催化剂、风险和跟踪指标，并严格区分公开论点与付费或私有内容，输出中文报告。"
}

Gaetano Crux Capital Research Model

Use this skill to analyze public Gaetano / Crux Capital research logic around photonics, optical networking, and Physical AI. This is a public-material research workflow, not a copy-trading tool and not a reproduction of paid Substack content.

Core Workflow

1. Define the infrastructure theme.

   • Identify whether the post is about optics/photonics, optical networking, AI data-center interconnect, Physical AI, robotics infrastructure, simulation, sensors, or another layer.

2. Map the stack.

   • Locate the company or technology within the stack: lasers, optical modules, CPO, networking, testing, materials, sensors, edge devices, simulation software, or systems integration.

3. Identify the chokepoint.

   • Ask what becomes scarce, hard to substitute, slow to qualify, capacity constrained, or under-covered as AI infrastructure scales.

4. Convert evidence into thesis quality.

   • Prefer public evidence from earnings transcripts, conference presentations, technical papers, customer capex, supply-chain checks, and company filings.
   • Separate technology education from a forward-looking investment thesis.

5. Evaluate market underpricing.

   • Note why the market might miss the theme: small cap, unfamiliar technology, weak sell-side coverage, timing uncertainty, or hidden operating leverage.

6. Define catalysts, risks, and tracking indicators.

   • Track customer qualification, design wins, capex cycle, margin inflection, capacity, product roadmap, standard adoption, and competitive response.

Semantic Labels

• keep: forward-looking thesis with company/technology, stack position, chokepoint, evidence, catalyst, and risk.
• keep_deweighted: useful thesis but missing one of evidence, timing, valuation, or risk.
• deweight: technology education, sector overview, broad basket, or watchlist without a clear forward signal.
• delete_from_this_signal: ticker appears only in quote, comparison, repost, or unrelated context.
• remove_from_forward_signal_keep_as_track_record_context: retrospective performance, portfolio recap, or marketing/credibility context.
• keep_as_explainer_deweight: useful framework lesson not tied to a specific public signal.
• delete: paid-only/private/duplicate/irrelevant content.

Output Contract

Produce:

• signals_auto_reviewed.csv
• signals_forward_clean.csv
• signals_high_quality_thesis.csv
• semantic_filter_summary.md
• gaetano_photonics_stack_template.md
• a concise Chinese or bilingual research-model report

Companion Tooling

This skill contains no scripts of its own. The CSV outputs above are produced with the sister skill skill-x-trader-builder (https://github.com/quantskills/skill-x-trader-builder): use its extract, auto-review, split, evaluate, template, and report subcommands, then apply this skill's references and semantic labels for the Gaetano-specific interpretation.

References

Use references/trader_profile.md, references/research_template.md, references/review_rules.md, and references/source_boundary.md.