What is the SECI-GRAI model?

It is a framework that combines the classic SECI model (Socialization, Externalization, Combination, Internalization) with the GRAI (Generative, Receptive AI) extension to manage knowledge creation in human-AI teams.

When should I use the Combination phase?

You should use it when you have multiple sources of explicit information—such as documents, logs, or specs—that need to be merged, restructured, or systematized into a new, more useful form.

Can this skill improve team collaboration with AI?

Yes, it defines eight specific interaction fields that clarify whether the human or the AI should take the lead in tasks like brainstorming, synthesizing, or teaching.

How does this skill help with documentation?

It helps identify the 'Externalization' phase, providing specific strategies to convert personal intuition and tacit understanding into structured, explicit technical documentation.

What's the difference between tacit and explicit knowledge in this context?

Tacit knowledge is experiential and hard to articulate (like 'knowing how' to solve a bug), while explicit knowledge is codified and easily shared (like technical instructions or code).

SECI-GRAI Knowledge Lifecycle

Name: SECI-GRAI Knowledge Lifecycle
Author: therealchrisrock

bytherealchrisrock

学习与文档

Applies the SECI-GRAI framework to guide human-AI knowledge creation, conversion, and collaboration cycles.

关于

This skill integrates the classic Nonaka-Takeuchi SECI model with the modern GRAI (Generative, Receptive AI) extension to facilitate sophisticated knowledge management within AI-assisted workflows. It enables users to identify their current phase of knowledge creation—moving from tacit intuition to explicit documentation and back again—while optimizing the interaction between human intelligence and generative AI. It is particularly useful for teams looking to formalize unstructured expertise into actionable organizational assets or for individuals seeking to deepen their understanding of complex domains through structured AI collaboration.

主要功能

0 GitHub stars
Guidance on converting tacit expertise into structured technical documentation
GRAI framework integration for optimized Human-AI interaction patterns
Identification of knowledge creation phases: Socialization, Externalization, Combination, and Internalization
Frameworks for synthesizing fragmented information into cohesive knowledge bases
Strategies for internalizing explicit knowledge through AI-supported learning

使用场景

Converting undocumented developer intuition into structured technical specifications
Synthesizing fragmented documentation from multiple sources into a comprehensive organizational wiki
Onboarding team members by facilitating the transition from reading documentation to practical implementation

关于

主要功能

0 GitHub stars
Guidance on converting tacit expertise into structured technical documentation
GRAI framework integration for optimized Human-AI interaction patterns
Identification of knowledge creation phases: Socialization, Externalization, Combination, and Internalization
Frameworks for synthesizing fragmented information into cohesive knowledge bases
Strategies for internalizing explicit knowledge through AI-supported learning

使用场景

Converting undocumented developer intuition into structured technical specifications
Synthesizing fragmented documentation from multiple sources into a comprehensive organizational wiki
Onboarding team members by facilitating the transition from reading documentation to practical implementation