This repository, nuwa-skill, is a data_research project that functions as a cognitive framework extraction engine. Its primary output is a collection of structured, testable AI 'perspective skills' (SKILL.md files) that model the thinking patterns, decision heuristics, and expression DNA of prominent public figures (e.g., Elon Musk, Richard Feynman, Steve Jobs, Donald Trump). The system does not run a live service; instead, it provides a rigorous methodology and a set of reusable artifacts for transforming unstructured biographical research into executable AI personas. The core value lies in its extraction framework, fidelity scorecard, and standardized skill template, which together form a complete pipeline for persona distillation. The architecture is a hybrid of a research methodology and a data artifact repository. The data flow begins with a 6-dimension research pipeline (writings, conversations, expression DNA, external views, decisions, timeline) that collects and structures raw source material. This is followed by the application of the Extraction Framework, which uses triple verification to identify mental models and quantify expression DNA. The output is validated against the Fidelity Scorecard through independent dual-agent testing. Supporting scripts (quality_check.py, merge_research.py, srt_to_transcript.py) automate parts of the validation and data processing pipeline, while the CI system (community-pr-check.yml) enforces structural quality gates for community contributions. The most reusable components are the methodology documents (extraction-framework.md, fidelity-scorecard.md, skill-template.md), which solve the recurring data engineering problem of converting unstructured text about a person into a structured, testable cognitive model. The project is actively maintained with a clear contribution model and low technical drift, as its core value is in its markdown-based methodology rather than complex software dependencies. However, reproducibility for creating *new* skills from scratch is low due to the lack of a formalized, dependency-managed environment, making the existing bundled skills the primary reusable asset.
This repository is a knowledge package designed to create an AI agent skill that embodies Charlie Munger's thinking framework. It doesn't contain executable code but rather curates and structures Munger's mental models, decision heuristics, and communication style into a usable format for AI agent runtimes. The core output is the SKILL.md file, which provides detailed instructions for how an AI agent should adopt Munger's perspective when responding to user queries, including role-playing rules, a response workflow requiring research before answering, and specific guidelines for expressing Munger's characteristic communication style. The repository represents a sophisticated approach to expertise capture and encoding for AI systems. It synthesizes information from over 50 sources including Berkshire Hathaway shareholder meetings, USC/Harvard speeches, interview records, and external criticisms to distill Munger's approach into five core mental models, eight decision heuristics, and a detailed expression DNA. The references/ directory contains the source research materials, demonstrating transparency about the knowledge foundation. Examples/show how the skill works in practice through sample conversations about investment decisions, team management, and content creation. While primarily a knowledge resource, the structure of the SKILL.md file itself offers potential reusability as a template for creating similar 'thinking style' skills for other experts or domains. The systematic approach to encoding expertise through role-playing protocols, mental model collections, decision frameworks, and communication guidelines represents a pattern that could be adapted with domain-specific content substitution for other AI agent skills.