architecture-audit · v1.0.0 · 2026-04-06 · sha256 49d1227179886225
architecture-audit v1.0.0A
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--- name: architecture-audit description: > Perform a structured audit of a software architecture or design decision, surfacing quality attributes, risks, constraint violations, and concrete improvement recommendations. Use when asked to review, audit, or assess a system design, ADR, or architectural choice. version: 1.0.0 authors: - Marie-Lynne Block tags: - architecture - audit - design - quality-attributes --- ## What this skill does Provides a repeatable, evidence-based framework for reviewing software architectures. It evaluates a design against quality attributes (scalability, maintainability, security, reliability, performance, operability), checks for constraint violations, and produces a prioritised list of findings with actionable recommendations. ## When to use it - User asks to "review", "audit", or "assess" an architecture, design document, or ADR. - User shares a system diagram, architecture description, or tech-stack decision and wants structured feedback. - User wants to identify risks or trade-offs in a design before committing to it. - User needs a report suitable for a design review meeting. ## Instructions 1. **Understand scope.** Clarify what is in scope (components, boundaries, interfaces) and what quality attributes matter most to the stakeholder. If not stated, default to: scalability, maintainability, security, reliability, performance, and operability. 2. **Collect evidence.** Read any provided diagrams, ADRs, documentation, or code. Note what is explicit, what is implied, and what is missing. 3. **Evaluate each quality attribute.** For each attribute in scope: - State the current approach (how the architecture addresses it). - Rate the approach: `Strong` / `Adequate` / `Weak` / `Not addressed`. - Identify specific risks or gaps. 4. **Check constraints.** Identify whether known constraints (team size, budget, compliance requirements, existing systems) are respected or at risk. 5. **Identify trade-offs.** Call out explicit design trade-offs and assess whether they are intentional and well-reasoned. 6. **Prioritise findings.** Rank findings by impact × likelihood: - `Critical` — must address before proceeding. - `High` — address in the next iteration. - `Medium` — schedule for backlog. - `Low` / `Informational` — note for awareness. 7. **Write recommendations.** For each Critical/High finding, provide at least one concrete, actionable recommendation. 8. **Produce the report** using the output format below. ## Output format ```markdown # Architecture Audit: [Subject] **Date:** YYYY-MM-DD **Auditor:** [name or AI-assisted] **Scope:** [what was reviewed] --- ## Quality Attribute Summary | Attribute | Rating | Key finding | |-----------------|-----------|--------------------------------------| | Scalability | Adequate | ... | | Maintainability | Weak | ... | | Security | Strong | ... | | Reliability | Weak | ... | | Performance | Adequate | ... | | Operability | Not addressed | ... | --- ## Findings ### [CRITICAL/HIGH/MEDIUM/LOW] Finding title **Evidence:** [what in the architecture leads to this finding] **Risk:** [what could go wrong] **Recommendation:** [concrete action] --- ## Trade-off Register | Decision | What was gained | What was sacrificed | Intentional? | |----------|----------------|---------------------|--------------| | ... | ... | ... | Yes / No / Unknown | --- ## Summary [2–4 sentence overall assessment. Is the architecture fit for purpose? What is the single most important thing to address?] ``` ## Examples ### Example 1 — Microservices migration proposal **Input:** User shares an ADR proposing to migrate a monolith to 12 microservices. **Expected output:** Audit report rating each quality attribute, flagging operational complexity and distributed tracing gap as High findings, noting the trade-off between deployment independence and increased latency. ### Example 2 — Database technology choice **Input:** User asks to review a decision to use a document store for a heavily relational dataset. **Expected output:** Report highlighting the data integrity (Critical) and query complexity (High) risks, recommending either a relational store or a hybrid approach with explicit join patterns. ## Notes - If the user provides insufficient detail, ask one targeted clarifying question before proceeding — do not invent architecture details. - Do not rate a quality attribute as `Strong` without citing specific evidence from the provided material. - Keep findings to the most impactful items; a ten-page list of nitpicks is less useful than three actionable priorities.