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Analyze codebase architecture and design patterns to assess maintainability, scalability, and adherence to best practices
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Based on SOC occupation classification
PR-focused code review with structured checklist, severity ratings, and actionable feedback
Use when a code review finding needs proof — write a focused test in JavaScript or Go that either confirms the issue is real or exposes it as over-engineering hyperbole. Trigger after code-review or code-review-skill findings are presented and evidence is requested.
Use when reviewing Dependabot alerts, npm audit findings, govulncheck output, or CVE reports on a JavaScript/Node.js or Go project — especially when triaging multiple alerts across direct and transitive dependencies to assess real-world risk and produce a remediation plan.
Produce data-driven software delivery estimates by analyzing historical JIRA tickets, git activity, and engineer track records, then matching the new work against the most similar past tickets. Use this skill whenever the user asks "how long will this take", wants to estimate a piece of work, scope an epic, plan a sprint, or estimate delivery for JIRA stories or a Figma design. Also use whenever the user wants developer-to-work assignment recommendations based on history, wants to optimize an estimate by adding or reallocating engineers, or asks "what's the fastest way to ship this" or "who should work on this". Especially trigger when the user provides JIRA ticket IDs, JIRA story links, or Figma designs together with any indication of a team that will execute the work.
Use when auditing an existing test suite for quality and coverage gaps, evaluating Playwright migration readiness, scoring automation against a world-class e-commerce standard, or guiding the creation of new tests. Applicable to Selenium, WebdriverIO, and Playwright suites.
AI-powered observability with Grafana MCP — translates natural language to metrics, logs, and trace queries to diagnose issues like a senior SRE
| name | architecture-review |
| description | Analyze codebase architecture and design patterns to assess maintainability, scalability, and adherence to best practices |
Analyze the overall architecture and design patterns of any codebase to assess maintainability, scalability, and adherence to best practices.
/architecture-review # Full review of current codebase
/architecture-review data-flow # Focus on data flow only
/architecture-review security,scalability # Multiple focus areas
Review the codebase architecture with the following scope: $ARGUMENTS
If $ARGUMENTS specifies a focus area, limit the review to the relevant sections below. If no focus is given, perform a full review but keep each section concise — breadth over depth.
Before analyzing, establish context:
package.json, go.mod, requirements.txt, etc.)Produce a scored report:
## Architecture Review Report
### Overall Score: [X]/100
| Dimension | Score | Status |
|---------------------|-------|--------|
| Structure & Patterns| X/100 | 🟢/🟡/🔴 |
| Data Flow & State | X/100 | 🟢/🟡/🔴 |
| Resilience & Ops | X/100 | 🟢/🟡/🔴 |
| Security | X/100 | 🟢/🟡/🔴 |
| Testability | X/100 | 🟢/🟡/🔴 |
| Evolution & Debt | X/100 | 🟢/🟡/🔴 |
### Key Strengths
- [Top 3 things done well]
### Critical Issues
- [Issues requiring immediate attention]
### Recommended Improvements
1. [Highest impact, with specific file/module references]
2. [...]
3. [...]
### Architecture Evolution Roadmap
- Short-term: [Quick wins]
- Medium-term: [Structural improvements]
- Long-term: [Strategic changes]
Focus on actionable insights with specific file paths and code references. Every recommendation should include why it matters and what to do about it.