context-os-basics

name: context-os-basics description: Foundation patterns for building context operating systems model: inherit

Context OS Basics

What is a Context OS?

A context operating system is a structured knowledge system where:

• AI compounds intelligence over time (never re-teach)
• Knowledge persists across sessions
• Concepts link to each other (graph, not files)
• Two layers separate reusable knowledge from operational docs

The Two-Layer Architecture

Layer 1: Atomic Knowledge Graph (knowledge_base/)

Individual reusable concepts:

• Technical knowledge
• Business insights
• Methodologies and patterns

Each node:

• Has structured frontmatter (metadata)
• Links to related concepts via [[wiki-links]]
• Follows lifecycle: emergent → validated → canonical

Layer 2: Operational Documents (00_foundation/)

Strategic artifacts that COMPOSE Layer 1:

• Positioning documents
• Messaging frameworks
• Process documentation

Key principle: Operational docs REFERENCE atomic concepts, they don't redefine them.

Constitutional Documents

taxonomy.yaml

Defines blessed tags for your system:

• Domains (technical, business, methodology)
• Node types (concept, pattern, case-study)
• Status values (emergent, validated, canonical)
• Your custom topic library

Start small. Add tags only after 3+ nodes demonstrate need.

ontology.yaml

Defines how concepts relate:

• Relationship types (enables, supports, implements)
• Linking requirements (minimum links per node)
• Quality thresholds

Evidence-Based Attribution

Every claim needs a source:

• [VERIFIED: file:line] - Direct evidence
• [INFERRED: logic] - Deduced from evidence
• [UNVERIFIABLE] - Cannot confirm (be honest)

Quality standard: If you can't cite a source, don't claim it.

The 4-Phase Framework (Summary)

1. Reconnaissance - Understand domain and requirements
2. Architecture - Design two-layer structure
3. Construction - Build knowledge graph and ops docs
4. Validation - Test and document system

Key Anti-Patterns

1. One Continuous Thread

Problem: Single long conversation without writing to files Fix: Write intermediate results to files, preserve attribution chains

2. Context Explosion

Problem: "Need to read all files to answer this" Fix: Use synthesis docs (5% of content answers 95% of questions)

3. Vague Attribution

Problem: "Many customers want this" (no source) Fix: Quantify everything: "14 of 166 (8.4%)" not "many"

The 3-5 Sample Rule

Never automate without sampling first:

1. Run broad search
2. Sample 3-5 results with context
3. Validate patterns are accurate
4. Refine and scale

Advanced Patterns (Not Covered Here)

For complex implementations:

• Chief of Staff orchestration
• Forcing functions and calibration
• Team governance patterns
• Enterprise taxonomy design
• Multi-agent coordination

These require customization for your specific context. Learn more: https://taste.systems