Analyze vault notes to identify investment blind spots and macro risks.
npx skills add https://github.com/jameswong2011/InvestmentVault --skill surface
Please help me install this Agent Skill. Skill: surface Source: https://github.com/jameswong2011/InvestmentVault/tree/main/.claude/skills/surface Command: npx skills add https://github.com/jameswong2011/InvestmentVault --skill surface
This Skill automates the analysis of vault content to uncover overlooked connections, insights, and opportunities, helping users make more informed investment decisions.
Ask the AI to analyze your vault for missed connections and risk factors by invoking the surface analysis command with the relevant scope.
High-intent search queries and answers about installing and using this skill.
To find hidden investment opportunities, you need cross-referencing to analyze your vault and identify overlooked connections, value chain nodes, and emerging themes across multiple notes. This process reveals unmonitored dependencies and implied opportunities.
Vault analysis for portfolio optimization involves parsing multiple notes to detect macro sensitivities, assess sector exposures, and flag unmonitored dependencies. It identifies investment blind spots and hidden value chain opportunities to refine your focus.
You can detect macro risks by running a comprehensive vault analysis that cross-references your notes to assess macro exposures and identify contrarian signals. This highlights hidden relationships and risk factors within your specified scope.
Yes, you can analyze multiple note types to find contrarian signals by performing deep multi-note parsing. This cross-referencing identifies patterns, gaps, and emerging themes that suggest contrarian opportunities within your data.
The best way to uncover overlooked value chain nodes is through comprehensive vault analysis that cross-references connections between notes. This identifies hidden relationships and value chain opportunities to refine your investment focus.
Yes, vault analysis works without external dependencies by performing deep multi-note parsing and cross-referencing internally. It focuses on discovering connections, assessing exposures, and detecting contrarian signals within your specified scope.
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