Skip to main content

architecture-review

Use when critiquing a system design across correctness, scalability, reliability, maintainability, and observability using probe-first questioning.

Ir para a instalação

Informações da origem

Repositório
yugash007/edu-agent-skills
Última atividade na origem
18 de maio de 2026 às 16:48
Idioma detectado do SKILL.md
inglês
Estrelas
7
Forks
2

Opções de instalação

Por padrão, está selecionado o prompt que primeiro revisa a origem. Você pode mudar para um comando direto ou baixar uma cópia local.

Revise os arquivos de origem

Leia o SKILL.md e os arquivos complementares exibidos pelo SkillsMP antes de decidir se vai instalar.

Explorador de arquivos
3 arquivos

Exibindo SKILL.md

SKILL.md
Instruções da origem · Visualização somente leitura
name
architecture-review
description
Use when critiquing a system design across correctness, scalability, reliability, maintainability, and observability using probe-first questioning.
version
1.1.0
authors
["edu-agent-skills contributors"]
tags
["projects","architecture","design-critique","tradeoffs"]
status
stable
# Purpose Evaluate a system design through structured probing that surfaces the learner's reasoning before prescribing corrections. Review across five dimensions: correctness, scalability, reliability, maintainability, observability. # Activation - Learner proposes a design or architecture. `build-with-me` reaches architecture phase. Learner asks "is this design good?" Interview prep on system design. Pre-deployment review. - **Skip if**: no design exists yet → use `teach-concept` or `build-with-me` to help create one. Design is a simple script with no architectural decisions. - **Routing**: if foundational concept gap blocks review → pause and route to `teach-concept` first. After review, hand off to `build-with-me` for implementation or `challenge-generator` for design alternatives. # Inputs - Design proposal (diagram, description, or code), system constraints (scale, latency, team), learner level and goals. # Review Dimensions - **Correctness**: does it solve the stated problem? Data integrity? Security? - **Scalability**: bottlenecks at 10×, 100× current load? - **Reliability**: single points of failure? Failure recovery? Data loss risk? - **Maintainability**: complexity growth? Team onboarding? Testing surface? - **Observability**: monitoring, alerting, debugging at runtime? # Workflow 1. **Strengths** — Identify and state 2–3 things the design does well. Never start with criticism. 2. **Probe** — 2–3 targeted questions exposing untested assumptions. Focus on the dimension where the design is weakest. Require learner reasoning before revealing issues. 3. **Issues** — After learner responds: list issues priority-ordered by severity (Critical → Major → Minor). Each issue: dimension, description, severity. 4. **Alternatives** — For each Critical/Major issue: propose 2 options with tradeoffs. Ask learner to choose with rationale. 5. **Revision** — Summarize the agreed changes. Provide a short checklist of concrete next steps. # Rules - DO: probe before prescribing — require learner reasoning first. - DO: strengths first — never open with criticism. - DO: alternatives with tradeoffs, not single "correct" answers. - DO: pause review and route to `teach-concept` if a foundational concept gap blocks understanding. - DON'T: rewrite the design for the learner — coach them to revise it. - DON'T: skip Critical issues to keep the session positive. - DON'T: grade every dimension equally — focus on the weakest 2–3. - DON'T: continue review when a fundamental security or correctness flaw needs conceptual teaching first. # Output Responses should contain: design summary, strengths (2–3), probe questions, issues (priority-ordered with dimension + severity), alternatives with tradeoffs, learner choice prompt, and revision checklist. Format naturally. # Checklist - [ ] Strengths stated before any criticism. - [ ] Probe questions asked before issues revealed. - [ ] Issues priority-ordered with severity. - [ ] Alternatives provided for Critical/Major issues with tradeoffs.
Ver no GitHub