How does this skill handle unverified information?

It includes a dedicated 'Curate' phase that uses cross-validation and real-world testing to separate theoretical claims from actual results, preventing the adoption of 'paper-only' knowledge.

Can I use this for non-technical learning?

Yes, while useful for technical research, the methodology is domain-agnostic and works effectively for finance, marketing, design, and other professional fields.

What is the 4C framework used in this skill?

The 4C framework consists of four stages: Collect (gathering diverse sources), Curate (verifying and filtering information), Connect (building mental models), and Create (producing output to prove mastery).

How does this skill integrate with Claude Code's /evolve command?

The skill is configured to be automatically identified and loaded by the /evolve command when Claude detects keywords related to learning, research, or entering new domains.

Knowledge Acquisition 4C

Name: Knowledge Acquisition 4C
Author: miles990

bymiles990

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Learning & Documentation

Implements a systematic 4C methodology to help users research, verify, and master any new domain from scratch.

About

The Knowledge Acquisition 4C skill provides a structured framework for Claude to guide users through the process of mastering unfamiliar fields. By following the 4C process—Collect, Curate, Connect, and Create—the skill ensures that learning is not just about gathering data, but also about rigorous verification, logical synthesis, and practical application. It integrates the Dreyfus Model of skill acquisition and the Feynman Technique to help users move from novice to expert while building durable mental models and high-quality documentation.

Key Features

Feynman Technique integration for simplifying complex concepts
Four-phase systematic learning: Collect, Curate, Connect, and Create
Information source hierarchy for tiered credibility assessment
Dreyfus Model alignment to track progress from novice to expert
1 GitHub stars
Rigorous verification standards to separate theory from practical reality

Use Cases

Rapidly onboarding into a completely new industry or technical domain
Synthesizing fragmented research into structured knowledge maps
Verifying theoretical data against real-world scenarios to avoid common pitfalls

About

Key Features

Feynman Technique integration for simplifying complex concepts
Four-phase systematic learning: Collect, Curate, Connect, and Create
Information source hierarchy for tiered credibility assessment
Dreyfus Model alignment to track progress from novice to expert
1 GitHub stars
Rigorous verification standards to separate theory from practical reality

Use Cases

Rapidly onboarding into a completely new industry or technical domain
Synthesizing fragmented research into structured knowledge maps
Verifying theoretical data against real-world scenarios to avoid common pitfalls