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Review system designs with a seven-step method to surface risks and improvements.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "systems-design-review-methodology" skill from askskill: 1. Download https://raw.githubusercontent.com/microsoft/amplifier-bundle-systems-design/main/skills/systems-design-review-methodology/SKILL.md 2. Save it as ~/.claude/skills/systems-design-review-methodology/SKILL.md 3. Reload skills and tell me it's ready
Use the systems-design-review-methodology seven-step process to review this e-commerce microservices architecture: summarize goals and constraints, classify the system, evaluate fit with the existing codebase and team capabilities, run adversarial risk analysis, validate scalability/consistency/cost tradeoffs, then provide conclusions and action items.
A structured design review with major risks, key tradeoffs, mismatches with the current state, and actionable improvements.
Apply the seven-step design review method to this log processing system: it handles 50TB of logs per day and requires low-latency search with low-cost storage. Focus on bottlenecks, failure scenarios, security boundaries, and whether the technical tradeoffs are justified.
A review report covering performance, reliability, security, and cost, with prioritized optimization actions.
I plan to migrate a monolith to an event-driven architecture. Review this migration design using the systems-design-review-methodology, focusing on fit with the current codebase, deployment workflow, observability stack, and team maintainability, and identify key decisions that need user validation.
A migration-focused review describing architectural fit, implementation risks, and prerequisites that require confirmation.
Companion skill for the /systems-design-review mode. The mode gates tools; this skill governs behavior.
For automated staging with approval gates, use the systems-design-review recipe instead of this manual flow:
recipes(operation="execute", recipe_path="@systems-design:recipes/systems-design-review.yaml", context={"target_path": "<path>"})
The recipe automates reconnaissance, classification, multi-perspective analysis, and report generation with human checkpoints between stages.
You handle the CONVERSATION. Agents handle the ANALYSIS.
You guide the user through the review, synthesize findings, and facilitate decisions. For deep analysis work, delegate to systems-design:systems-design-critic or use the adversarial-review skill.
Before any critique, reconstruct the designer's reasoning:
Apply the Comprehending Existing lens:
Understanding original intent prevents reviews that recommend "fixing" things the designer already considered and deliberately chose.
Ask: "Is this the complete design, or is there additional context I should know about?"
This step is mandatory. Do not skip it. Do not proceed to analysis without completing it.
Based on what you learned in Step 1, classify the system under review. Produce a brief taxonomy:
System types -- which of these apply? List ALL that match, not just the primary one:
| Type | Skill | Applies when... |
|---|---|---|
| Web service / API | system-type-web-service | HTTP endpoints, REST/GraphQL, request-response |
| Event-driven | system-type-event-driven | Message queues, event logs, pub/sub, hooks, reactive patterns |
| Data pipeline | system-type-data-pipeline | Batch/streaming processing, ETL, DAG scheduling |
| Workflow orchestration | system-type-workflow-orchestration | Multi-step processes, sagas, durable execution |
| CLI tool | system-type-cli-tool | Command-line interface, subcommands, plugin architecture |
| Real-time | system-type-real-time | WebSockets, persistent connections, state sync |
| Multi-tenant SaaS | system-type-multi-tenant-saas | Tenant isolation, shared infrastructure, billing |
| ML serving | system-type-ml-serving | Model serving, feature stores, inference pipelines |
| Distributed system | system-type-distributed | Consensus, replication, partitioning, multi-node coordination |
| Enterprise integration | system-type-enterprise-integration | Legacy modernization, API gateways, data integration |
| Edge / offline-first | system-type-edge-offline | Offline operation, sync protocols, constrained resources |
| Single-page app | system-type-spa | Client-side routing, state management, rendering strategies |
| Peer-to-peer | system-type-peer-to-peer | P2P topologies, NAT traversal, decentralized coordination |
| Azure-hosted | system-type-azure | Azure compute, identity, networking, managed services |
Design philosophies -- which does the system claim or embody?
| Philosophy | Skill | Applies when... |
|---|---|---|
| Linux/Unix | design-philosophy-linux | Mechanism vs policy, composability, small sharp tools |
| Domain-driven | design-philosophy-domain-driven | Bounded contexts, ubiquitous language, aggregates |
…
Design and assess enterprise integration patterns, legacy modernization, and orchestration strategies.
Evaluate system designs through Unix/Linux principles for simplicity and composability.
Design or assess Azure system architectures, operations, and cloud service choices.
Design and evaluate data pipelines, streaming systems, and ETL architecture patterns.
Design and assess multi-tenant SaaS architecture, isolation, billing, and resilience.
Design offline-first edge systems with sync, conflict handling, and weak-network resilience.
Structure system design discussions across architecture, tradeoffs, risks, and migration planning.
Review system designs from six critical perspectives and produce unified risk assessments.
Review architecture or code for security risks, vulnerabilities, and compliance issues.
Review code or branches for correctness, compatibility, architecture, tests, performance, and security.
Generate or audit design systems and review styling consistency changes.
Review implementation plans for gaps, assumptions, and sequencing before coding starts