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Full-Stack Architecture & Performance Auditor

porTHIAGONOMA·THIAGONOMA· v1.0.0 · atualizado em 2026-04-12T22:48:12.348Z
87
Score

Audits the full application stack — architecture principles (beyond SOLID: CUPID, CQRS, event-driven), frontend performance (Core Web Vitals 2026), CDN & edge strategy, microservices data patterns, and rendering architecture — producing a scored report with prioritized, actionable improvements.

architectureperformancecupidcqrsmicroservicescdncore-web-vitalsedge-computingevent-drivenfrontendrenderingcache-strategyimage-optimization
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TypeScriptJavaScriptHTMLCSSYAML
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Documento do Skill

SKILL.mdarch-performance-auditor/workflow
Step-by-step skill execution:
1
SENSE — Discovery and context collection
Scan repository structure and identify all entrypoints
Collect existing performance data (PageSpeed URL, Lighthouse JSON, or user-provided metrics)
Detect stack: `cat package.json | grep -E '"next|express|fastify|hono|nestjs"'`
Map services: `ls -la` on `docker-compose.yml`, `k8s/`, `services/` directories
Read CDN config if present: `vercel.json`, `cloudflare.toml`, `_headers`, `nginx.conf`
2
CONTEXTUALIZE — Architecture mapping
Build a service map: which services exist, what databases they use, how they communicate
Identify the rendering strategy per Next.js route:
```ts
// SSG: export const dynamic = 'force-static'
// ISR: export const revalidate = 60
// SSR: export const dynamic = 'force-dynamic'
// Edge: export const runtime = 'edge'
```
Check image delivery: are images served via `<Image>` (Next.js) / Nuxt Image / plain `<img>`?
Verify cache headers: `curl -I https://yourdomain.com/api/products | grep cache-control`
3
HYPOTHESIZE — Multi-axis analysis
Run CUPID checklist against top-3 core modules
Evaluate data flow for CQRS candidates:
```
read:write ratio > 50:1 → CQRS candidate
write involves > 3 services → Saga/Outbox candidate
state must be auditable → Event Sourcing candidate
```
Check Core Web Vitals against 2026 thresholds:
```
LCP: < 2.0s ✅ / 2.0-4.0s 🟠 / > 4.0s 🔴
INP: < 200ms ✅ / 200-500ms 🟠 / > 500ms 🔴
CLS: < 0.08 ✅ / 0.08-0.25 🟠 / > 0.25 🔴
```
4
EVALUATE — Score computation
Calculate dimension scores with available evidence
Compute CAPS = (CWV×0.25) + (CUPID×0.20) + (Data×0.20) + (CDN×0.15) + (Rendering×0.10) + (Assets×0.10)
Classify all findings with severity labels
Rank recommendations by effort-to-impact score
5
RECOMMEND & ACT — Generate deliverables
Produce `ARCH-PERF-AUDIT-REPORT.md`:
```markdown
# Architecture & Performance Audit — [Project Name]
CAPS Score: 67/100 🟠
Executive Summary
Dimension Scores
Critical Findings (must fix)
High-Priority Improvements
Code Examples
Migration Roadmap
```
Generate code fix examples for each critical finding:
```ts
// BEFORE (CDN miss — no cache headers):
export async function GET() {
const data = await db.products.findMany();
return Response.json(data);
}
// AFTER (CDN-cacheable at edge, 60s revalidation):
export async function GET() {
const data = await db.products.findMany();
return Response.json(data, {
headers: { 'Cache-Control': 's-maxage=60, stale-while-revalidate=300' }
});
}
```
Generate Lighthouse CI config:
```json
{
"ci": {
"assert": {
"assertions": {
"categories:performance": ["error", {"minScore": 0.85}],
"largest-contentful-paint": ["error", {"maxNumericValue": 2000}],
"total-blocking-time": ["warn", {"maxNumericValue": 150}]
}
}
}
}
```
6
REFLECT — Validation and telemetry
Verify all code examples match the detected stack version
Confirm CAPS score methodology is transparent in the report
Report execution via MCP SkillsChain
Schedule next audit date in the report footer

Telemetria de Agentes

Execuções
0
total
Taxa de Sucesso
0%
últimos 30d
Latência Média
0.0s
p50
Alucinação
0.0%
detecção
Tokens Entrada
0
avg 0/exec
Tokens Saída
0
avg 0/exec

Uso por Plataforma

Árvore do Skill

Full-Stack Architecture & Performance Auditor
arch-performance-auditor
Fases Cognitivas7
1.SENSE: Perception
2.CONTEXTUALIZE: Contextualization
3.HYPOTHESIZE: Hypothesis
4.EVALUATE: Evaluation
5.RECOMMEND: Recommendation
6.ACT: Action
7.REFLECT: Reflection
Triggers24
audit de arquiteturaarchitecture auditperformance auditalém do SOLIDbeyond SOLIDcore web vitalsCUPID principlesCQRS auditevent-driven reviewmicroservices reviewCDN strategyedge computing auditimage optimization auditLCP INP CLSrendering architecturecache strategy reviewdatabase per servicefrontend performancefull stack auditlatência altaslow pageperformance do sistemagargalo de arquiteturaarchitecture bottleneck

Avaliar este Skill

Score Breakdown

⭐Avaliação Humana0%
🤖Sucesso de Agentes0%
🕐Atualidade100%
🔗Saúde de Dependências100%
🕸️Centralidade no Grafo0%
🛡️Segurança50%
CompositeScore = α·Humano + β·Agente + γ·Recência + δ·Deps + ε·Centralidade + ζ·Segurança

Instalação

$ synaptic mcp download arch-performance-auditor
$ synaptic skills detail arch-performance-auditor
$ synaptic skills live arch-performance-auditor

Links

GitHub Repository