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Found 31 Skills
Dynamic tier system for right-sizing n8n workflow hardening. Use this skill on ANY n8n workflow request to determine appropriate validation, logging, and error handling levels. Adapts to user needs — from quick prototypes to mission-critical production systems.
Manages Amazon DocumentDB end-to-end — serverless-on-8.0 cluster setup, TLS/VPC/driver config, flexible-schema and vector-search data modeling, MongoDB compatibility assessment, DMS-based migration, slow-query diagnosis, major version upgrades (4.0→5.0→8.0), Well-Architected reviews (41-check wa_review.py), cost estimation, and security hardening. Retrieve for every DocumentDB question and when the user asks to set up or migrate MongoDB to AWS — DocumentDB is AWS's MongoDB-compatible managed database. Triggers: JSON document store, document database, MongoDB on AWS, Nested fields, Lambda cannot connect, TLS handshake, VPC port 27017, IAM auth, Secrets Manager, encryption at rest, $graphLookup, flexible schema, COLLSCAN, compound index, DMS migration, CDC cutover, $vectorSearch, RAG, Global Clusters, DR replication, cost sizing, audit, health check, production-readiness.
Audit Lightning Web Components for SLDS compliance and produce a scored quality report. Runs the SLDS linter, analyzes CSS for theming hook usage and pairing, checks HTML for accessibility attributes, and scores findings across categories into an overall grade. Use when asked to "score my component", "SLDS scorecard", "quality report", "audit SLDS compliance", "how good is my SLDS", "check component quality", "rate my component", "evaluate my component", "is this component ready to ship?", "look at my LWC for issues", "audit this before I submit", "review my component before code review", or any time a user wants a quality assessment or production-readiness check on an LWC or SLDS component. Not for fixing violations (use design-systems-slds2-migrate) or building new components (use design-systems-slds-apply).
Audit Lightning Web Components for SLDS compliance and produce a scored quality report. Runs the SLDS linter, analyzes CSS for theming hook usage and pairing, checks HTML for accessibility attributes, and scores findings across categories into an overall grade. Use when asked to "score my component", "SLDS scorecard", "quality report", "audit SLDS compliance", "how good is my SLDS", "check component quality", "rate my component", "evaluate my component", "is this component ready to ship?", "look at my LWC for issues", "audit this before I submit", "review my component before code review", or any time a user wants a quality assessment or production-readiness check on an LWC or SLDS component. Not for fixing violations (use uplifting-components-to-slds2) or building new components (use applying-slds).
Analyze and transform messy, prototype, overgrown, slop-prone, or hard-to-maintain software repositories into maintainable product-shaped codebases while preserving existing product behavior. Use when the user asks to antislop a codebase, clean up a messy repo, run a maintainability migration, write a refactor plan, modernize structure, improve TypeScript/type boundaries, harden tests, reduce large files, clean architecture, coordinate subagent-driven refactors, or produce a final migration audit/report/microsite. Do not use for broader production-readiness specialties such as security audits, observability/logging programs, compliance hardening, SRE/runbook work, or reliability engineering unless the user explicitly scopes those as part of the maintainability refactor.
Use when the user asks for a broad codebase review, substantial PR/branch review, architecture audit, tech-debt scan, cleanup assessment, structural sanity check, or design-alignment review. Default workflow: use sub-agents when available unless specifically forbidden; do not require the user to mention sub-agents, council mode, delegation, or parallel review. Focus on cruft, duplication, weak boundaries, missed reuse, lifecycle/concurrency risks, test/roadmap drift, and code aesthetics. Do not use for narrow bug fixes, ordinary small-diff reviews, frontend visual QA, repo-onboarding docs, or OpenAI Agents SDK production-readiness review. Output evidence-backed findings first, then pressure points, design alignment, open questions, and follow-through.
Provides GKE golden path configuration defaults, production readiness checklists, and cluster default patterns. Use when designing GKE clusters, verifying GKE production readiness, or checking configurations against GKE defaults. Don't use for setting up node autoscaling specifically (use gke-scaling instead).
FastAPI patterns for async APIs, dependency injection, Pydantic request and response models, OpenAPI docs, tests, security, and production readiness.
Transform applications into production-ready deployments with systematic analysis, improvement, and framework-specific optimization
Orchestrates comprehensive production readiness reviews and assessments for GKE clusters and workloads across scalability, security, reliability, observability, backup/DR, and cost optimization. Use when asked to productionize, prepare, assess, audit, or review a GKE cluster or workload before going live to production. Don't use for deep-dive single-domain implementation (use specific domain skills like gke-scaling, gke-platform-security, gke-workload-security, gke-service-networking, gke-reliability instead).
Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations
Audit completed implementation against the spec and produce a gap report with compliance matrix, risks, remediation steps, and a go/no-go production readiness decision. Use after implementation is complete.