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knowledge-priming-refiner

Generate a project knowledge-base.md from guided tech stack and architecture interviews.

176|13|Updated Mar 2, 2026
Search Tags:#architecture#documentation#ai-context#tech-stack#project-knowledge#knowledge-priming
One-click install
npx skills add https://github.com/techygarg/lattice --skill knowledge-priming-refiner
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: knowledge-priming-refiner
Source: https://github.com/techygarg/lattice/tree/main/plugins/lattice/skills/knowledge-priming-refiner
Command: npx skills add https://github.com/techygarg/lattice --skill knowledge-priming-refiner

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Facilitates a structured, project-focused conversation to produce a bespoke knowledge-base document that captures the team's tech stack, architecture, trusted sources, and project structure, ensuring AI has immediate context for development work.

Core Features & Use Cases

  • Interview-led generation of .lattice/standards/knowledge-base.md containing sections for architecture, standards, and sources.
  • Customizable output path via .lattice/config.yaml with a sensible default and overlay capability.
  • Ambient context provided to all subsequent skills and molecules for consistent project alignment.

Quick Start

Initiate the knowledge priming interview to generate the project knowledge base document.

Frequently Asked Questions about knowledge-priming-refiner

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I prime AI context with project knowledge for onboarding?▼

Knowledge priming for AI context involves creating a project-specific knowledge-base.md capturing your tech stack, architecture, and trusted sources. This structured document provides AI with immediate project understanding during onboarding and development.

How do I generate a project knowledge base document for my tech stack?▼

Generate a project knowledge base by initiating an interview to collect your tech stack, architecture, and trusted sources. The process outputs a structured knowledge-base.md file with YAML frontmatter and dedicated template sections for project alignment.

Can I customize the output path for my project knowledge base file?▼

Yes, you can customize the knowledge-base.md output path by specifying it in .lattice/config.yaml. If no custom path is configured, the document defaults to .lattice/standards/knowledge-base.md with overlay capability.

What is the best way to document project architecture and trusted sources for AI?▼

The best way to structure project architecture for AI is using a knowledge-base.md document with dedicated template sections. This captures architecture, standards, and trusted sources, providing ambient context to align subsequent AI skills.

What sections are included in the generated knowledge base markdown?▼

The generated knowledge-base.md includes YAML frontmatter and Knowledge Priming Refiner template sections. These sections document your tech stack, architecture, trusted sources, and project structure for comprehensive AI context.

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