Summary

This repository offers a suite of "Codex skills" engineered for quantitative investment research. It translates market news and fundamental data into testable investment hypotheses and robust valuation frameworks, catering to power users and researchers seeking structured, data-driven financial analysis. The skills provide advanced capabilities for alpha generation, intrinsic growth estimation, valuation health scoring, and TAM-adjusted PEG analysis.

How It Works

Each skill employs a distinct analytical methodology. serenity-alpha converts market news into alpha hypotheses through a framework mapping news to demand, financials, small-cap elasticity, and validation. bayesian-intrinsic-growth-valuation estimates a company's intrinsic 3-5 year growth rate using Bayesian hypothesis updates, contrasting it with market-implied growth and FOMO. gf-dma-health-index scores stock valuation and trend health by integrating fundamental growth speed, DMA trends, divergence, and estimate revisions. tam-adj-peg refines traditional PEG ratios by adjusting for Total Addressable Market (TAM) runway and business quality factors.

Quick Start & Requirements

Installation involves copying the skill subdirectories into a local Codex skills folder, typically located at "${CODEX_HOME:-$HOME/.codex}/skills". The primary prerequisite is the "Codex" environment, the specifics of which are not detailed in this README. For users preferring to avoid local setup and environment management, a hosted, subscription-based version is available at app.k2ai.dev.

Highlighted Details

• serenity-alpha constructs 1-4 quarter verification chains and falsification points for hypotheses, framing output as research, not personalized advice.
• bayesian-intrinsic-growth-valuation separates intrinsic growth updates from FOMO, narrative heat, and valuation multiple expansion.
• gf-dma-health-index scores fundamental-DMA match, price-DMA divergence, trend parallelism, and revision confirmation.
• tam-adj-peg differentiates growth speed from duration, TAM capture, pricing power, cyclicality, dilution, and execution risk.

Maintenance & Community

The provided README offers no specific details regarding maintainers, community channels (e.g., Discord, Slack), or project roadmaps. A hosted version is accessible via subscription through @iamai_omni.

Licensing & Compatibility

The project is licensed under the MIT license. This permissive license generally allows for commercial use and modification, provided attribution is maintained.

Limitations & Caveats

A significant adoption blocker is the dependency on an unspecified "Codex" environment, requiring users to either manage this external system or opt for the hosted solution. The skills are explicitly framed as research tools, not personalized investment advice, and their utility is contingent on the underlying Codex framework's capabilities and data inputs.