Diagnose knowledge graph health and drift from health reports and friction patterns.
npx skills add https://github.com/LopeWale/amplLABS --skill architect-lopewale
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
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.
Provide the latest health data and I will generate architecture-evolution recommendations with evidence and research backing.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
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