review-codebase
Acerca de
Esta habilidad realiza una revisión integral de código en múltiples fases, abarcando arquitectura, seguridad, calidad de código y UX/accesibilidad en una única pasada coordinada. Genera una tabla de hallazgos priorizados con niveles de severidad que pueden convertirse directamente en incidencias de GitHub mediante la habilidad create-github-issues. Úsala para revisiones profundas y holísticas de proyectos completos o subproyectos en todas las dimensiones de calidad.
Instalación rápida
Claude Code
Recomendadonpx skills add pjt222/agent-almanac -a claude-code/plugin add https://github.com/pjt222/agent-almanacgit clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/review-codebaseCopia y pega este comando en Claude Code para instalar esta habilidad
Documentación
Review Codebase
Multi-phase deep codebase review producing severity-rated findings w/ fix-order rec. Unlike review-pull-request (scoped to diff) or single-domain reviews (security-audit-codebase, review-software-architecture), covers entire project/subproject across all quality dims in one pass.
Use When
- Whole-project or subproject review (not PR-scoped)
- New codebase onboarding — building mental model of what exists + needs attention
- Periodic health checks after sustained dev
- Pre-release quality gate across architecture, security, code quality, UX
- Output should feed directly into issue creation or sprint planning
In
- Required:
target_path— root dir of codebase/subproject - Optional:
scope— phases to run:full(default),security,architecture,quality,uxoutput_format—findings(table only),report(narrative),both(default)severity_threshold— min severity:LOW(default),MEDIUM,HIGH,CRITICAL
Do
Step 1: Census
Inventory codebase → est scope + ID review targets.
- Count files by lang/type:
find target_path -type f | sort by extension - Measure total line counts per lang
- ID test dirs + estimate coverage (files w/ tests vs without)
- Check dep state: lockfiles present, outdated deps, known vulns
- Note build system, CI/CD config, docs state
- Record census as opening section of report
→ Factual inventory — file counts, langs, test presence, dep health. No judgments yet.
If err: target empty/inaccessible → stop + report. Specific subdirs inaccessible → note + continue w/ available.
Step 2: Architecture Review
Assess structural health: coupling, duplication, data flow, separation of concerns.
- Map module/dir structure + ID primary architectural pattern
- Check code duplication — repeated logic across files, copy-paste
- Assess coupling — how many files must change for single feature mod
- Eval data flow — clear boundaries between layers (UI, logic, data)?
- ID dead code, unused exports, orphaned files
- Check consistent patterns — codebase follows own conventions?
- Rate each: CRITICAL, HIGH, MEDIUM, LOW
→ List of architectural findings w/ severity + file refs. Common: mode dispatch duplication, missing abstraction layers, circular deps.
If err: codebase too small for meaningful review (<5 files) → note + skip Step 3. Architecture review needs enough code to have structure.
Step 3: Security Audit
ID security vulns + defensive coding gaps.
- Scan injection vectors: HTML (
innerHTML), SQL, command injection - Check authn + authz patterns (if applicable)
- Review error handling — silently swallowed? Leak internals?
- Audit dep versions vs known CVEs
- Check hardcoded secrets, API keys, creds
- Review Docker/container security: root user, exposed ports, build secrets
- Check localStorage/sessionStorage for sensitive data
- Rate each: CRITICAL, HIGH, MEDIUM, LOW
→ List of security findings w/ severity, affected files, remediation. CRITICAL = injection vulns + exposed secrets.
If err: no security-relevant code (pure docs project) → note + skip Step 4.
Step 4: Code Quality
Eval maintainability, readability, defensive coding.
- ID magic numbers + hardcoded values should be named consts
- Check consistent naming across codebase
- Find missing input validation at system boundaries
- Assess error handling — consistent? Useful messages?
- Check commented-out code, TODO/FIXME, incomplete impls
- Review test quality — testing behavior or impl details?
- Rate each: CRITICAL, HIGH, MEDIUM, LOW
→ List of quality findings → maintainability. Common: magic numbers, inconsistent patterns, missing guards.
If err: codebase generated/minified → note + adjust expectations. Generated code has diff quality criteria than hand-written.
Step 5: UX + a11y (if frontend exists)
Eval UX + a11y compliance.
- Check ARIA roles, labels, landmarks on interactive
- Verify keyboard nav — all interactive reachable via Tab?
- Test focus mgmt — focus moves logically when panels open/close?
- Check responsive — test at common breakpoints (320px, 768px, 1024px)
- Verify color contrast meets WCAG 2.1 AA
- Check screen reader compat — dynamic content changes announced?
- Rate each: CRITICAL, HIGH, MEDIUM, LOW
→ List of UX/a11y findings w/ WCAG refs. No frontend → "N/A — no frontend code detected."
If err: frontend exists but can't render (missing build step) → audit source code statically + note runtime testing not possible.
Step 6: Findings Synthesis
Compile all findings → prioritized summary.
- Merge findings from all phases → single table
- Sort by severity (CRITICAL first, then HIGH, MEDIUM, LOW)
- Within each severity, group by theme (security, architecture, quality, UX)
- Each finding: severity, phase, file(s), one-line description, suggested fix
- Produce rec fix order considering deps between fixes
- Summarize: total findings by severity, top 3 priorities, est effort level
→ Findings table w/ columns: #, Severity, Phase, File(s), Finding, Fix. Fix-order rec accounting for deps (e.g. "refactor architecture before adding tests").
If err: no findings produced → finding itself — codebase exceptionally clean or review too shallow. Re-examine ≥1 phase deeper.
Check
- All requested phases done (or explicitly skipped w/ justification)
- Every finding has severity rating (CRITICAL/HIGH/MEDIUM/LOW)
- Every finding refs ≥1 file or dir
- Findings table sorted by severity
- Fix-order recs account for deps between findings
- Summary has total counts by severity
- If
output_formatincludesreport, narrative sections accompany table
Scaling w/ Rest
Between review phases, use /rest as checkpoint — esp between phases 2-5 needing diff analytical perspectives. Checkpoint rest (brief, transitional) prevents momentum of one phase biasing next. See rest "Scaling Rest" for guidance on checkpoint vs full rest.
Traps
- Boiling ocean: Reviewing every line of large codebase produces noise. Focus high-impact: entry points, security boundaries, architectural seams.
- Severity inflation: Not every finding CRITICAL. Reserve CRITICAL for exploitable vulns + data-loss risks. Most architectural = MEDIUM.
- Missing forest for trees: Individual code quality matters less than systemic patterns. Magic numbers in 20 files = 1 architectural finding not 20 quality.
- Skip census: Census (Step 1) seems bureaucratic but prevents reviewing code that doesn't exist or missing entire dirs.
- Phase bleed: Security findings during architecture, or quality during security audit. Note for correct phase, no mix concerns — produces cleaner table.
→
security-audit-codebase— deep-dive when review-codebase security phase reveals complex vulnsreview-software-architecture— detailed architecture review for specific subsystemsreview-ux-ui— comprehensive UX/a11y audit beyond phase 5review-pull-request— diff-scoped review for individual changesclean-codebase— impl code quality fixes ID'd by this reviewcreate-github-issues— convert findings → tracked GH issues
Repositorio GitHub
Habilidades relacionadas
content-collections
MetaEsta habilidad proporciona una configuración probada en producción para Content Collections, una herramienta centrada en TypeScript que transforma archivos Markdown/MDX en colecciones de datos con tipado seguro mediante validación Zod. Úsala al construir blogs, sitios de documentación o aplicaciones Vite + React con mucho contenido para garantizar seguridad de tipos y validación automática de contenido. Abarca todo, desde la configuración del plugin de Vite y compilación MDX hasta la optimización de despliegue y validación de esquemas.
polymarket
MetaEsta habilidad permite a los desarrolladores crear aplicaciones con la plataforma de mercados de predicción Polymarket, incluyendo la integración de API para operaciones y datos de mercado. También proporciona transmisión de datos en tiempo real a través de WebSocket para monitorear operaciones en vivo y actividad del mercado. Úsela para implementar estrategias de trading o crear herramientas que procesen actualizaciones de mercado en tiempo real.
creating-opencode-plugins
MetaEsta habilidad ayuda a los desarrolladores a crear complementos de OpenCode que se conectan a más de 25 tipos de eventos, como comandos, archivos y operaciones LSP. Proporciona la estructura del complemento, las especificaciones de la API de eventos y los patrones de implementación para módulos en JavaScript/TypeScript. Úsala cuando necesites interceptar, monitorear o extender el ciclo de vida del asistente de IA de OpenCode con lógica personalizada basada en eventos.
sglang
MetaSGLang es un framework de alto rendimiento para el servicio de LLM que se especializa en generación rápida y estructurada para JSON, expresiones regulares y flujos de trabajo de agentes utilizando su caché de prefijos RadixAttention. Ofrece una inferencia significativamente más rápida, especialmente para tareas con prefijos repetidos, lo que lo hace ideal para salidas complejas y estructuradas, y conversaciones multiturno. Elige SGLang sobre alternativas como vLLM cuando necesites decodificación restringida o estés construyendo aplicaciones con uso extensivo de prefijos compartidos.
