architect

Analyze knowledge-graph health and derivation friction to produce ranked evolution recommendations.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/hellofrommorgan/intent-computer --skill architect-hellofrommorgan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: architect
Source: https://github.com/hellofrommorgan/intent-computer/tree/main/packages/plugin/src/plugin-skills/architect
Command: npx skills add https://github.com/hellofrommorgan/intent-computer --skill architect-hellofrommorgan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides research-backed recommendations to evolve a knowledge system by analyzing health data, friction signals, and derivation history, while ensuring user approval for any changes.

Core Features & Use Cases

  • Health-driven evolution recommendations
  • Friction pattern analysis across operational surfaces
  • Derivation-aligned proposals grounded in research
  • Explicit user approval before any modification
  • Traceable evidence chains linking recommendations to research claims

Quick Start

Prompt me to generate an evolution proposal for my knowledge graph.

Frequently Asked Questions about architect

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

FAQPage Schema
How do I get actionable recommendations for evolving my knowledge graph system?▼

To get actionable recommendations for evolving your knowledge graph, analyze health data, friction patterns, and derivation history to produce 3-5 ranked proposals mapped to traceable evidence with file-level implementation steps.

What is derivation-based constraint analysis for system health?▼

Derivation-based constraint analysis identifies config drift and operational health issues by examining derivation history, returning concrete implementation plans with time estimates and risk assessments tied to knowledge-graph claims.

How do I detect friction patterns across my operational surfaces?▼

Detect friction patterns across operational surfaces by analyzing health data and derivation history to identify health issues, generating ranked evolution proposals grounded in traceable research evidence.

Can I review and approve proposed changes before my knowledge system is modified?▼

Yes, you can review and approve proposed changes because the system requires explicit user approval before any modification, ensuring you validate file-level steps, time estimates, and risk assessments mapped to knowledge-graph claims.

What's the best way to trace evidence chains linking system recommendations to research claims?▼

Trace evidence chains by generating evolution proposals that map 3-5 ranked recommendations directly to derivation history and knowledge-graph claims, providing traceable evidence for health-driven system modifications.

Does knowledge system evolution guidance work without external dependencies?▼

Yes, knowledge system evolution guidance works without external dependencies, analyzing internal health data, friction signals, and config drift independently to produce actionable, research-backed recommendations for your system.