| name | performance-scalability-reviewer |
| description | Review load behavior, bottlenecks, caching, database access, queue behavior, scaling, timeouts, and resource limits. |
| version | 1.1.0 |
| since | 2026-06-17 |
| last_modified | 2026-06-17 |
| authors | ["platform-engineering"] |
| stability | stable |
| min_platform_version | {"codex":"unknown","amazon-q":"unknown","antigravity":"unknown","auggie":"unknown","bob":"unknown","claude-code":"unknown","cline":"unknown","codebuddy":"unknown","continue":"unknown","costrict":"unknown","crush":"unknown","github-copilot":"unknown","gitlab-duo":"unknown","factory":"unknown","forgecode":"unknown","opencode":"unknown","openhands":"unknown","cursor":"unknown","roo-code":"unknown","kiro":"unknown","junie":"unknown","gemini-cli":"unknown","iflow":"unknown","kilocode":"unknown","kimi":"unknown","lingma":"unknown","pi":"unknown","qoder":"unknown","qwen":"unknown","windsurf":"unknown","ollama":"unknown"} |
| deprecated_since | null |
| replaces | null |
| supersedes | [] |
| changelog | [{"version":"1.1.0","date":"2026-06-17","change":"Initial generated production-ready SDLC / DevSecOps skill"}] |
Performance Scalability Reviewer
Purpose
Review load behavior, bottlenecks, caching, database access, queue behavior, scaling, timeouts, and resource limits. Treat regulatory, security, and operational references as review and evidence guidance, not legal advice.
When to use
- performance and scalability decisions, controls, or operating practices need independent review.
- A change affects performance and scalability artifacts such as load test, database query, cache strategy, queue consumer, autoscaling rule, resource limit.
- The user needs evidence-oriented findings for risks such as N+1 query, cache stampede, queue backlog, timeout cascade, CPU saturation, scaling bottleneck.
- Audit, security, operations, or platform stakeholders need a concise readiness position.
- Existing documentation, tickets, tests, or logs must be turned into actionable remediation items.
Operating model
- Identify the relevant performance and scalability artifacts, owners, systems, environments, and review boundary.
- Compare the available artifacts against expected signals such as latency percentile, throughput chart, query plan, cache hit rate, queue depth, capacity forecast.
- Separate confirmed gaps from assumptions, missing evidence, and advisory improvement opportunities.
- Rate findings by operational, security, compliance, customer, and auditability impact.
- Recommend minimal remediation steps, validation evidence, owners, and review cadence.
Spec-Driven Change Context
- Treat repository specs, ADRs, runbooks, change proposals, design notes, and task files as durable context that outlives a chat session.
- For non-trivial changes, prefer a checked-in change artifact or equivalent proposal/design/tasks record before implementation begins.
- Capture requirement deltas explicitly: added, modified, removed, deprecated, or unchanged behavior.
- Keep implementation tasks traceable to acceptance criteria, affected specs, validation commands, and owners.
- During verification, compare the implementation against the proposal, design decisions, task checklist, and spec deltas.
- After completion, sync or archive completed change artifacts so the repository's source of truth reflects the final behavior.
- If the repository has no spec workflow yet, report the missing artifact and provide a minimal proposal/spec/tasks outline instead of relying on chat-only intent.
Skill-Specific Review Scope
- Primary artifacts: load test, database query, cache strategy, queue consumer, autoscaling rule, resource limit.
- Risk themes: N+1 query, cache stampede, queue backlog, timeout cascade, CPU saturation, scaling bottleneck.
