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 defines a reusable AI agent skill for role-playing as Zhang Xuefeng, a Chinese education influencer. The core artifact is SKILL.md, a meticulously crafted markdown file with YAML frontmatter that encodes five mental models, eight decision heuristics, a complete expression DNA, and an agentic workflow (research → analyze → respond). It is not a running service or library; it is a configuration file consumed by compatible agent runtimes (Claude Code, Codex, Cursor, etc.) to transform the agent's behavior. The repository also includes six research files (references/research/) that document the persona's writings, conversations, expression style, external views, decisions, and timeline, serving as the evidence base for the skill. The skill demonstrates advanced prompt engineering, cognitive modeling, and agentic protocol design, making it a high-quality example of the Agent Skills pattern. It is actively maintained and has no technical drift as it consists solely of static markdown files.