ai-work-booster

What problem does it solve?

Many AI-generated analyses look plausible but are not directly actionable or reliable in practice. This Skill constrains AI reasoning with engineering methodology to convert vague suggestions into accurate, executable architecture and migration plans that reduce implementation risk.

Core Features & Use Cases

• Structured diagnosis: Produces a seven-dimension diagnostic matrix (single-responsibility, testability, extensibility, permissions, performance, fault isolation, observability) with severity markings and priorities.
• Targeted remediation: Generates prioritized improvement plans for high-severity issues, includes ASCII architecture diagrams, trade-off analysis, and Strangler Fig migration routes for incremental refactoring.
• Architectural patterns & guardrails: Recommends microkernel + pipeline-filter refactors, zero-trust policy engines, registry + DAG tool scheduling, tiered caching, adaptive retry and backpressure strategies.
• Use cases: Repository architecture review, refactoring planning for large agent loops, security & permission audits, and performance optimization of AI-driven systems.

Quick Start

Analyze the architecture of this repository and produce a seven-dimension diagnostic matrix, prioritized remediation plans for red and orange issues, an ASCII architecture diagram, and a Strangler Fig migration route.