architect

Diagnose knowledge graph health and drift from health reports and friction patterns.

Updated Feb 3, 2026

Search Tags:#knowledge-management #derivation #evidence-based #architect #friction #health-analysis #system-evolution

Ready to run

npx skills add https://github.com/LopeWale/amplLABS --skill architect-lopewale

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: architect
Source: https://github.com/LopeWale/amplLABS/tree/main/.claude/skills/architect
Command: npx skills add https://github.com/LopeWale/amplLABS --skill architect-lopewale

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps maintain and evolve complex knowledge systems by providing research-backed recommendations based on health reports, friction patterns, and derivation histories. Proposals are clearly justified with research evidence and require explicit user approval before any changes are made.

Core Features & Use Cases

Health-driven evolution proposals: analyzes system health summaries to identify drift and weakness.
Friction-aware recommendations: detects recurring workflow hurdles and suggests design-level improvements.
Research-backed justification: ties each recommendation to concrete claims from the knowledge graph and reference material.

Quick Start

Provide the latest health data and I will generate architecture-evolution recommendations with evidence and research backing.

Frequently Asked Questions about architect

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

FAQPage Schema

How do I diagnose knowledge management system drift using health reports?▼

Architecture evolution proposals are generated by analyzing health reports, friction patterns, and derivation histories. This process diagnoses system health and drift across your knowledge management pipeline, requiring explicit user approval before any modification is applied.

Can I get research-backed recommendations for fixing workflow friction patterns?▼

Yes, you can get research-backed recommendations for fixing workflow friction patterns. The system detects recurring hurdles and suggests targeted design-level improvements, tying each recommendation to concrete evidence and reference material claims from your knowledge base for full traceability.

What is the best way to maintain knowledge graph consistency during system evolution?▼

Maintaining knowledge graph consistency during system evolution requires evidence-based architecture modifications. The system proposes targeted changes derived from health analysis and derivation history, ensuring every recommendation is traceable to research claims and subject to explicit user approval before execution.

Does the architecture evolution process require manual approval before applying changes?▼

Yes, the architecture evolution process requires explicit manual approval before applying any changes. All recommendations for your knowledge management pipeline are proposed with research justification but remain inactive until you review and approve the targeted modifications to your system.

Why does my knowledge management pipeline need derivation history analysis?▼

Derivation history analysis is needed to diagnose system health and detect drift over time. By reviewing past derivations alongside health reports and friction patterns, the system can propose targeted, evidence-backed changes to maintain knowledge graph consistency and architecture evolution.