How does /recommend differ from the /setup skill?

/recommend is a purely advisory tool that provides a research-backed sketch and rationale without creating files, whereas /setup is the generative tool that implements the system.

What happens if I don't know my system constraints yet?

If your input is sparse, the skill enters 'conversational mode,' asking at most two targeted questions to help determine your goals before providing a recommendation.

What research is used for these recommendations?

Recommendations are grounded in the arscontexta research graph, which focuses on cognitive load, information retrieval patterns, and established 'Tools for Thought' (TFT) methodologies.

Does this skill support Obsidian or Notion structures?

Yes, it considers platform capabilities like semantic search and linking types to recommend architectures that fit specific tools like Obsidian, Claude Code, or plain-file systems.

Knowledge Architecture Advisor

Name: Knowledge Architecture Advisor
Author: LopeWale

byLopeWale

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학습 및 문서화

Provides research-backed structural recommendations for building optimized personal and organizational knowledge management systems.

The recommend skill acts as a specialized advisory layer for designing sophisticated knowledge systems (PKM/OKM) grounded in 'Tools for Thought' (TFT) research. By analyzing your specific use cases, constraints, and processing style, it maps requirements across eight architectural dimensions—including granularity, organization, and automation—to provide a tailored system blueprint. It references pre-validated presets like Zettelkasten, PARA, and Evergreen notes, ensuring every structural decision is supported by specific research claims to prevent 'navigational vertigo' and cognitive friction before you commit to a specific implementation.

주요 기능

01Evidence-based architecture mapping across 8 distinct system dimensions

02Conversational discovery mode for users with undefined requirements

03Direct integration with a research claim graph for citing decision rationale

04Pre-validated tradition presets for Zettelkasten, PARA, Evergreen, and Cornell methods

05Automated constraint validation to identify and resolve structural conflicts

061 GitHub stars

사용 사례

01Determining the ideal metadata density and automation level for an AI-assisted documentation repository

02Comparing structural trade-offs between different organizational traditions for a specific project

03Designing a scalable personal knowledge base for academic research or long-form creative writing

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add lopewale/dev_ignition recommend

For use in Claude.ai and ChatGPT

Download Skill