giuseppe-trisciuoglio/developer-kit
Comprehensive developer toolkit providing reusable skills for Java/Spring Boot, TypeScript/NestJS/React/Next.js, Python, PHP, AWS CloudFormation, AI/RAG, DevOps, and more.
89
89%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Risky
Do not use without reviewing
93%
Provides advanced AWS CLI patterns for managing EC2, Lambda, S3, DynamoDB, RDS, VPC, IAM, and CloudWatch. Generates bulk operation scripts, automates cross-service workflows, validates security configurations, and executes JMESPath queries for complex filtering. Triggers on "aws cli help", "aws command line", "aws scripting", "aws automation", "aws batch operations", "aws bulk operations", "aws cli pagination", "aws multi-region", "aws profiles", "aws cli troubleshooting".
79%
Provides AWS CloudFormation patterns for Auto Scaling including EC2, ECS, and Lambda. Use when creating Auto Scaling groups, launch configurations, launch templates, scaling policies, lifecycle hooks, and predictive scaling. Covers template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and best practices for high availability and cost optimization.
84%
Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector stores, setting up content moderation guardrails, managing prompts, orchestrating workflows with flows, and configuring inference profiles for model optimization.
88%
Provides AWS CloudFormation patterns for CloudFront distributions, origins (ALB, S3, Lambda@Edge, VPC Origins), CacheBehaviors, Functions, SecurityHeaders, parameters, Outputs and cross-stack references. Use when creating CloudFront distributions with CloudFormation, configuring multiple origins, implementing caching strategies, managing custom domains with ACM, configuring WAF, and optimizing performance.
81%
Provides AWS CloudFormation patterns for CloudWatch monitoring, metrics, alarms, dashboards, logs, and observability. Use when creating CloudWatch metrics, alarms, dashboards, log groups, log subscriptions, anomaly detection, synthesized canaries, Application Signals, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and CloudWatch best practices for monitoring production infrastructure.
93%
Provides AWS CloudFormation patterns for DynamoDB tables, GSIs, LSIs, auto-scaling, and streams. Use when creating DynamoDB tables with CloudFormation, configuring primary keys, local/global secondary indexes, capacity modes (on-demand/provisioned), point-in-time recovery, encryption, TTL, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references.
93%
Provides AWS CloudFormation patterns for EC2 instances, Security Groups, IAM roles, and load balancers. Use when creating EC2 instances, SPOT instances, Security Groups, IAM roles for EC2, Application Load Balancers (ALB), Target Groups, and implementing template structure with Parameters, Outputs, Mappings, Conditions, and cross-stack references.
85%
Provides AWS CloudFormation patterns for ECS clusters, task definitions, services, container definitions, auto scaling, blue/green deployments, CodeDeploy integration, ALB integration, service discovery, monitoring, logging, template structure, parameters, outputs, and cross-stack references. Use when creating ECS clusters with CloudFormation, configuring Fargate and EC2 launch types, implementing blue/green deployments, managing auto scaling, integrating with ALB and NLB, and implementing ECS best practices.
88%
Provides AWS CloudFormation patterns for ElastiCache Redis or Memcached infrastructure, including subnet groups, parameter groups, security controls, and cross-stack outputs. Use when designing cache tiers, high-availability replication groups, encryption settings, or reusable CloudFormation templates for application caching.
93%
Provides AWS CloudFormation patterns for IAM roles, policies, managed policies, permission boundaries, and trust relationships. Use when modeling least-privilege access, cross-account assumptions, service roles, or reusable IAM stacks that other CloudFormation templates consume.
84%
Provides AWS CloudFormation patterns for Lambda functions, layers, API Gateway integration, event sources, cold start optimization, monitoring, logging, template validation, and deployment workflows. Use when creating Lambda functions with CloudFormation, configuring event sources, implementing cold start optimization, managing layers, integrating with API Gateway, and deploying Lambda infrastructure.
93%
Provides AWS CloudFormation patterns for Amazon RDS databases. Use when creating RDS instances (MySQL, PostgreSQL, Aurora), DB clusters, multi-AZ deployments, parameter groups, subnet groups, and implementing template structure with Parameters, Outputs, Mappings, Conditions, and cross-stack references.
93%
Provides AWS CloudFormation patterns for Amazon S3. Use when creating S3 buckets, policies, versioning, lifecycle rules, and implementing template structure with Parameters, Outputs, Mappings, Conditions, and cross-stack references.
88%
Provides AWS CloudFormation patterns for security infrastructure including KMS encryption, Secrets Manager, IAM security, VPC security, ACM certificates, parameter security, outputs, and secure cross-stack references. Use when implementing security best practices, encrypting data, managing secrets, applying least privilege IAM policies, securing VPC configurations, managing TLS/SSL certificates, and implementing defense in depth strategies.
76%
Provides patterns to deploy ECS tasks and services with GitHub Actions CI/CD. Use when building Docker images, pushing to ECR, updating ECS task definitions, deploying ECS services, integrating with CloudFormation stacks, configuring AWS OIDC authentication for GitHub Actions, and implementing production-ready container deployment pipelines. Supports ECS deployments with proper security (OIDC or IAM keys), multi-environment support, blue/green deployments, ECR private repositories with image scanning, and CloudFormation infrastructure updates.
93%
Provides AWS CloudFormation patterns for VPC foundations, including subnets, route tables, internet and NAT gateways, endpoints, and reusable outputs. Use when creating a new network baseline, segmenting public and private workloads, or preparing CloudFormation networking stacks for application deployments.
90%
Provides structured AWS cost optimization guidance using five pillars (right-sizing, elasticity, pricing models, storage optimization, monitoring) and twelve actionable best practices with executable AWS CLI examples. Use when optimizing AWS costs, reviewing AWS spending, finding unused AWS resources, implementing FinOps practices, reducing EC2/EBS/S3 bills, configuring AWS Budgets, or performing AWS Well-Architected cost reviews.
93%
Creates professional AWS architecture diagrams in draw.io XML format (.drawio files) using official AWS Architecture Icons (aws4 library). Use when the user asks for AWS diagrams, VPC layouts, multi-tier architectures, serverless designs, network topology, or draw.io exports involving Lambda, EC2, RDS, or other AWS services.
82%
Provides AWS Lambda integration patterns for Java with cold start optimization. Use when deploying Java functions to AWS Lambda, choosing between Micronaut and Raw Java approaches, optimizing cold starts below 1 second, configuring API Gateway or ALB integration, or implementing serverless Java applications. Triggers include "create lambda java", "deploy java lambda", "micronaut lambda aws", "java lambda cold start", "aws lambda java performance", "java serverless framework".
82%
Provides AWS Lambda integration patterns for PHP with Symfony using the Bref framework. Creates Lambda handler classes, configures runtime layers, sets up SQS/SNS event triggers, implements warm-up strategies, and optimizes cold starts. Use when deploying PHP/Symfony applications to AWS Lambda, configuring API Gateway integration, implementing serverless PHP applications, or optimizing Lambda performance with Bref. Triggers include "create lambda php", "deploy symfony lambda", "bref lambda aws", "php lambda cold start", "aws lambda php performance", "symfony serverless", "php serverless framework".
82%
Provides AWS Lambda integration patterns for Python with cold start optimization. Use when deploying Python functions to AWS Lambda, choosing between AWS Chalice and raw Python approaches, optimizing cold starts, configuring API Gateway or ALB integration, or implementing serverless Python applications. Triggers include "create lambda python", "deploy python lambda", "chalice lambda aws", "python lambda cold start", "aws lambda python performance", "python serverless framework".
93%
Provides AWS Lambda integration patterns for TypeScript with cold start optimization. Use when creating or deploying TypeScript Lambda functions, choosing between NestJS framework and raw TypeScript approaches, optimizing cold starts, configuring API Gateway or ALB integration, or implementing serverless TypeScript applications. Triggers include "create lambda typescript", "deploy typescript lambda", "nestjs lambda aws", "raw typescript lambda", "aws lambda typescript performance".
93%
Provides patterns to configure AWS RDS (Aurora, MySQL, PostgreSQL) with Spring Boot applications. Configures HikariCP connection pools, implements read/write splitting, sets up IAM database authentication, enables SSL connections, and integrates with AWS Secrets Manager. Use when setting up RDS connections in Spring Boot, configuring connection pooling, or managing database credentials securely.
82%
Provides AWS SAM bootstrap patterns: generates `template.yaml` and `samconfig.toml` for new projects via `sam init`, creates SAM templates for existing Lambda/CloudFormation code migration, validates build/package/deploy workflows, and configures local testing with `sam local invoke`. Use when the user asks about SAM projects, `sam init`, `sam deploy`, serverless deployments, or needs to bootstrap/migrate Lambda functions with SAM templates.
88%
Provides Amazon Bedrock patterns using AWS SDK for Java 2.x. Invokes foundation models (Claude, Llama, Titan), generates text and images, creates embeddings for RAG, streams real-time responses, and configures Spring Boot integration. Use when asking about Bedrock integration, Java SDK for AI models, AWS generative AI, Claude/Llama invocation, embeddings for RAG, or Spring Boot AI setup.
87%
Provides AWS SDK for Java 2.x client configuration, credential resolution, HTTP client tuning, timeout, retry, and testing patterns. Use when creating or hardening AWS service clients, wiring Spring Boot beans, debugging auth or region issues, or choosing sync vs async SDK usage.
88%
Provides Amazon DynamoDB patterns using AWS SDK for Java 2.x. Use when creating, querying, scanning, or performing CRUD operations on DynamoDB tables, working with indexes, batch operations, transactions, or integrating with Spring Boot applications.
88%
Provides AWS Key Management Service (KMS) patterns using AWS SDK for Java 2.x. Use when creating/managing encryption keys, encrypting/decrypting data, generating data keys, digital signing, key rotation, or integrating encryption into Spring Boot applications.
93%
Provides AWS Lambda patterns using AWS SDK for Java 2.x. Use when invoking Lambda functions, creating/updating functions, managing function configurations, working with Lambda layers, or integrating Lambda with Spring Boot applications.
93%
Provides AWS messaging patterns using AWS SDK for Java 2.x for SQS queues and SNS topics. Handles sending/receiving messages, FIFO queues, DLQ, subscriptions, and pub/sub patterns. Use when implementing messaging with SQS or SNS.
89%
Provides AWS RDS (Relational Database Service) management patterns using AWS SDK for Java 2.x. Use when creating, modifying, monitoring, or managing Amazon RDS database instances, snapshots, parameter groups, and configurations.
93%
Provides Amazon S3 patterns and examples using AWS SDK for Java 2.x. Use when working with S3 buckets, uploading/downloading objects, multipart uploads, presigned URLs, S3 Transfer Manager, object operations, or S3-specific configurations.
82%
Provides AWS Secrets Manager patterns for AWS SDK for Java 2.x, including secret retrieval, caching, rotation-aware access, and Spring Boot integration. Use when storing or reading secrets in Java services, replacing hardcoded credentials, or wiring secret-backed configuration into applications.
87%
Provides Better Auth integration patterns for NestJS backend and Next.js frontend with Drizzle ORM and PostgreSQL. Use when setting up Better Auth with NestJS backend, integrating Next.js App Router frontend, configuring Drizzle ORM schema, implementing social login (GitHub, Google), adding plugins (2FA, Organization, SSO, Magic Link, Passkey), implementing email/password authentication with session management, or creating protected routes and middleware.
87%
Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.
93%
Creates new Architecture Decision Record (ADR) documents for significant architectural changes using a consistent template and repository-aware naming and storage guidance. Use when a user or agent decides on an architectural change, needs to document technical rationale, or wants to add a new ADR to the project history.
87%
Provides comprehensive memory file management capabilities including auditing, quality assessment, and targeted improvements for files such as CLAUDE.md. Use when user asks to check, audit, update, improve, fix, maintain, or validate project memory files. Also triggers for "project memory optimization", "CLAUDE.md quality check", "documentation review", or when a project memory file needs to be created from scratch. This skill scans memory files, evaluates quality against standardized criteria, outputs detailed quality reports with scores and recommendations, then makes targeted updates with user approval.
84%
Provides Codex CLI delegation workflows for complex code generation and development tasks using OpenAI's GPT-5.3-codex models, including English prompt formulation, execution flags, sandbox modes, and safe result handling. Use when the user explicitly asks to use Codex for complex programming tasks such as code generation, refactoring, or architectural analysis. Triggers on "use codex", "delegate to codex", "run codex cli", "ask codex", "codex exec", "codex review".
88%
Provides GitHub Copilot CLI task delegation in non-interactive mode with multi-model support (Claude, GPT, Gemini), permission controls, output sharing, and session resume. Use when users ask to hand work to Copilot, compare models, or run Copilot programmatically from Claude Code.
87%
Provides automated documentation updates by analyzing git changes between the current branch and the last release tag. Performs git diff analysis to identify modifications, then updates README.md, CHANGELOG.md following Keep a Changelog standard, and discovers documentation folders for contextual updates. Use when preparing a release, maintaining documentation sync, or before creating a pull request. Triggers on "update docs", "update changelog", "sync documentation", "update readme", "prepare release documentation".
93%
Creates professional logical flow diagrams and logical system architecture diagrams using draw.io XML format (.drawio files). Use when creating: (1) logical flow diagrams showing data/process flow between system components, (2) logical architecture diagrams representing system structure without cloud provider specifics, (3) BPMN process diagrams, (4) UML diagrams (class, sequence, activity), (5) data flow diagrams (DFD), (6) decision flowcharts, or (7) system interaction diagrams. This skill focuses on generic/abstract representations, not AWS/Azure-specific architectures (use aws-drawio-architecture-diagrams for cloud diagrams).
88%
Provides comprehensive Drizzle ORM patterns for schema definition, CRUD operations, relations, queries, transactions, and migrations. Proactively use for any Drizzle ORM development including defining database schemas, writing type-safe queries, implementing relations, managing transactions, and setting up migrations with Drizzle Kit. Supports PostgreSQL, MySQL, SQLite, MSSQL, and CockroachDB.
85%
Provides TypeScript patterns for DynamoDB-Toolbox v2 including schema/table/entity modeling, .build() command workflow, query/scan access patterns, batch and transaction operations, and single-table design with computed keys. Use when implementing type-safe DynamoDB access layers with DynamoDB-Toolbox v2 in TypeScript services or serverless applications.
82%
Provides comprehensive NestJS best practices including modular architecture, dependency injection scoping, exception filters, DTO validation with class-validator, and Drizzle ORM integration. Use when designing NestJS modules, implementing providers, creating exception filters, validating DTOs, or integrating Drizzle ORM within NestJS applications.
90%
Provides Gemini CLI delegation workflows for large-context analysis and complex reasoning using Gemini 3.0 Flash and Gemini 3.0 Pro models, including English prompt formulation, execution flags, and safe result handling. Use when the user explicitly asks to use Gemini for tasks such as broad codebase analysis, fast iterations with Gemini 3 Flash, or deep architectural reasoning with Gemini 3 Pro. Triggers on "use gemini", "delegate to gemini", "run gemini cli", "ask gemini", "use gemini for this task", "use gemini 3 flash", "use gemini 3 pro".
82%
Provides a structured 8-phase workflow for resolving GitHub issues in Claude Code. Covers fetching issue details, analyzing requirements, implementing solutions, verifying correctness, performing code review, committing changes, and creating pull requests. Use when user asks to resolve, implement, work on, fix, or close a GitHub issue, or references an issue URL or number for implementation.
93%
Provides expert guidance for building GraalVM Native Image executables from Java applications. Use when converting JVM applications to native binaries, optimizing cold start times, reducing memory footprint, configuring native build tools for Maven or Gradle, resolving reflection and resource issues in native builds, or implementing framework-specific native support for Spring Boot, Quarkus, and Micronaut. Triggers include "graalvm native image", "native executable java", "java cold start optimization", "native build tools", "ahead of time compilation java", "reflection config graalvm", "native image build failure".
93%
Provides patterns to build declarative AI Services with LangChain4j for LLM integration, chatbot development, AI agent implementation, and conversational AI in Java. Generates type-safe AI services using interface-based patterns, annotations, memory management, and tools integration. Use when creating AI-powered Java applications with minimal boilerplate, implementing conversational AI with memory, or building AI agents with function calling.
82%
Provides LangChain4j patterns for implementing MCP (Model Context Protocol) servers, creating Java AI tools, exposing tool calling capabilities, and integrating MCP clients with AI services. Use when building a Java MCP server, implementing tool calling in Java, connecting LangChain4j to external MCP servers, or securing tool exposure for agent workflows.
79%
Provides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge bases, semantic search over document repositories, or knowledge-enhanced AI applications with source attribution.
82%
Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.
88%
Provides unit test, integration test, and mock AI patterns for LangChain4j applications. Creates mock LLM responses, tests retrieval chains, validates RAG workflows, and implements Testcontainers-based integration tests for Java AI services. Use when unit testing AI services, integration testing LangChain4j components, mocking AI models, or testing LLM-based Java applications.
93%
Provides and generates LangChain4j tool and function calling patterns: annotates methods as tools with @Tool, configures tool executors, registers tools with AiServices, validates tool parameters, and handles tool execution errors. Use when building AI agents that call tools, define function specifications, manage tool responses, or integrate external APIs with LLM-driven applications.
79%
Provides configuration patterns for LangChain4J vector stores in RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.
85%
Provides comprehensive NestJS framework patterns with Drizzle ORM integration for building scalable server-side applications. Generates REST/GraphQL APIs, implements authentication guards, creates database schemas, and sets up microservices. Use when building NestJS applications, setting up APIs, implementing authentication, working with databases, or integrating Drizzle ORM.
93%
Provides comprehensive code review capability for NestJS applications, analyzing controllers, services, modules, guards, interceptors, pipes, dependency injection, and database integration patterns. Use when reviewing NestJS code changes, before merging pull requests, after implementing new features, or for architecture validation. Triggers on "review NestJS code", "NestJS code review", "check my NestJS controller/service".
90%
Generates complete CRUD modules for NestJS applications with Drizzle ORM. Use when building server-side features in NestJS that require database operations, including creating new entities with full CRUD endpoints, services with Drizzle queries, Zod-validated DTOs, and unit tests. Triggered by requests like "generate a user module", "create a product CRUD", "add a new entity with endpoints", or when setting up database-backed features in NestJS.
88%
Provides patterns and code examples for building Next.js 16+ applications with App Router architecture. Use when creating projects with App Router, implementing Server Components and Client Components ("use client"), creating Server Actions for forms, building Route Handlers (route.ts), configuring caching with "use cache" directive (cacheLife, cacheTag), setting up parallel routes (`@slot`) or intercepting routes, migrating to proxy.ts, or working with App Router file conventions (layout.tsx, page.tsx, loading.tsx, error.tsx).
93%
Provides authentication implementation patterns for Next.js 15+ App Router using Auth.js 5 (NextAuth.js). Use when setting up authentication flows, implementing protected routes, managing sessions in Server Components and Server Actions, configuring OAuth providers, implementing role-based access control, or handling sign-in/sign-out flows in Next.js applications.
88%
Provides comprehensive code review capability for Next.js applications, validates Server Components, Client Components, Server Actions, caching strategies, metadata, API routes, middleware, and performance patterns. Use when reviewing Next.js App Router code changes, before merging pull requests, after implementing new features, or for architecture validation. Triggers on "review Next.js code", "Next.js code review", "check my Next.js app".
88%
Provides Next.js App Router data fetching patterns including SWR and React Query integration, parallel data fetching, Incremental Static Regeneration (ISR), revalidation strategies, and error boundaries. Use when implementing data fetching in Next.js applications, choosing between server and client fetching, setting up caching strategies, or handling loading and error states.
93%
Provides comprehensive patterns for deploying Next.js applications to production. Use when configuring Docker containers, setting up GitHub Actions CI/CD pipelines, managing environment variables, implementing preview deployments, or setting up monitoring and logging for Next.js applications. Covers standalone output, multi-stage Docker builds, health checks, OpenTelemetry instrumentation, and production best practices.
82%
Expert Next.js performance optimization skill covering Core Web Vitals, image/font optimization, caching strategies, streaming, bundle optimization, and Server Components best practices. Use when optimizing Next.js applications for Core Web Vitals (LCP, INP, CLS), implementing next/image and next/font, configuring caching with unstable_cache and revalidateTag, converting Client Components to Server Components, implementing Suspense streaming, or analyzing and reducing bundle size. Supports Next.js 16 + React 19 patterns.
90%
Enables interaction with Google NotebookLM for advanced RAG (Retrieval-Augmented Generation) capabilities via the notebooklm-mcp-cli tool. Use when querying project documentation stored in NotebookLM, managing research notebooks and sources, retrieving AI-synthesized information, generating audio podcasts or reports from notebooks, or performing contextual queries against curated knowledge bases. Triggers on "notebooklm", "nlm", "notebook query", "research notebook", "query documentation in notebooklm".
88%
Provides comprehensive Nx monorepo management guidance for TypeScript/JavaScript projects. Use when creating Nx workspaces, generating apps/libraries/components, running affected commands, setting up CI/CD, configuring Module Federation, or implementing NestJS backends within Nx
76%
Provides workflows to write, debug, and optimize prompts for LLMs, including few-shot example selection, chain-of-thought structuring, system prompt design, and template composition. Use when the user asks to write or improve a prompt, wants help with few-shot examples, chain-of-thought, system prompts, prompt templates, or asks how to get better results from an LLM.
88%
Provides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.
82%
Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.
79%
Provides comprehensive code review capability for React applications, validates component architecture, hooks usage, React 19 patterns, state management, performance optimization, accessibility compliance, and TypeScript integration. Use when reviewing React code changes, before merging pull requests, after implementing new features, or for component architecture validation. Triggers on "review React code", "React code review", "check my React components".
88%
Provides comprehensive React 19 patterns for Server Components, Server Actions, useOptimistic, useActionState, useTransition, concurrent features, Suspense boundaries, and TypeScript integration. Generates executable code patterns, validates security for public endpoints, and optimizes performance with React Compiler or manual memoization. Proactively use when building React 19 applications with Next.js App Router, implementing optimistic UI, or optimizing concurrent rendering.
88%
Provides complete shadcn/ui component library patterns including installation, configuration, and implementation of accessible React components. Use when setting up shadcn/ui, installing components, building forms with React Hook Form and Zod, customizing themes with Tailwind CSS, or implementing UI patterns like buttons, dialogs, dropdowns, tables, and complex form layouts.
88%
Provides Spring Boot MCP server patterns that create Model Context Protocol servers with Spring AI by defining tool handlers, exposing resources, configuring prompt templates, and setting up transports for AI function calling and tool calling. Use when building MCP servers to extend AI capabilities with Spring's official AI framework, implementing AI tools, custom function calling, or MCP client integration.
93%
Provides patterns to configure Spring Boot Actuator for production-grade monitoring, health probes, secured management endpoints, and Micrometer metrics across JVM services. Use when setting up monitoring, health checks, or metrics for Spring Boot applications.
87%
Provides patterns for implementing Spring Boot caching: configures Redis/Caffeine/EhCache providers with TTL and eviction policies, applies @Cacheable/@CacheEvict/@CachePut annotations, validates cache hit/miss behavior, and exposes metrics via Actuator. Use when adding caching to Spring Boot services, configuring cache expiration, evicting stale data, or diagnosing cache misses.
90%
Provides and generates complete CRUD workflows for Spring Boot 3 services. Creates feature-focused architecture with Spring Data JPA aggregates, repositories, DTOs, controllers, and REST APIs. Validates domain invariants and transaction boundaries. Use when modeling Java backend services, REST API endpoints, database operations, web service patterns, or JPA entities for Spring Boot applications.
93%
Provides dependency injection patterns for Spring Boot projects, including constructor-first design, optional collaborator handling, bean selection, and wiring validation. Use when creating services and configurations, replacing field injection, or troubleshooting ambiguous or fragile Spring wiring.
87%
Provides Event-Driven Architecture (EDA) patterns for Spring Boot — creates domain events, configures ApplicationEvent and @TransactionalEventListener, sets up Kafka producers and consumers, and implements the transactional outbox pattern for reliable distributed messaging. Use when implementing event-driven systems in Spring Boot, setting up async messaging with Kafka, publishing domain events from DDD aggregates, or needing reliable event publishing with the outbox pattern.
88%
Provides patterns to generate comprehensive REST API documentation using SpringDoc OpenAPI 3.0 and Swagger UI in Spring Boot 3.x applications. Use when setting up API documentation, configuring Swagger UI, adding OpenAPI annotations, implementing security documentation, or enhancing REST endpoints with examples and schemas.
90%
Creates and scaffolds a new Spring Boot project (3.x or 4.x) by downloading from Spring Initializr, generating package structure (DDD or Layered architecture), configuring JPA, SpringDoc OpenAPI, and Docker Compose services (PostgreSQL, Redis, MongoDB). Use when creating a new Java Spring Boot project from scratch, bootstrapping a microservice, or initializing a backend application.
93%
Provides fault tolerance patterns for Spring Boot 3.x using Resilience4j. Use when implementing circuit breakers, handling service failures, adding retry logic with exponential backoff, configuring rate limiters, or protecting services from cascading failures. Generates circuit breaker, retry, rate limiter, bulkhead, time limiter, and fallback implementations. Validates resilience configurations through Actuator endpoints.
85%
Provides REST API design standards and best practices for Spring Boot projects. Use when creating or reviewing REST endpoints, DTOs, error handling, pagination, security headers, HATEOAS and architecture patterns.
90%
Provides distributed transaction patterns using the Saga Pattern for Spring Boot microservices. Use when implementing distributed transactions across services, handling compensating transactions, ensuring eventual consistency, or building choreography or orchestration-based sagas with Kafka, RabbitMQ, or Axon Framework.
88%
Provides JWT authentication and authorization patterns for Spring Boot 3.5.x covering token generation with JJWT, Bearer/cookie authentication, database/OAuth2 integration, and RBAC/permission-based access control using Spring Security 6.x. Use when implementing authentication or authorization in Spring Boot applications.
88%
Provides comprehensive testing patterns for Spring Boot applications covering unit, integration, slice, and container-based testing with JUnit 5, Mockito, Testcontainers, and performance optimization. Use when writing tests, @Test methods, @MockBean mocks, or implementing test suites for Spring Boot applications.
88%
Provides patterns to implement persistence layers with Spring Data JPA. Use when creating repositories, configuring entity relationships, writing queries (derived and `@Query`), setting up pagination, database auditing, transactions, UUID primary keys, multiple databases, and database indexing.
79%
Provides Spring Data Neo4j integration patterns for Spring Boot applications. Use when you need to work with a graph database, Neo4j nodes and relationships, Cypher queries, or Spring Data Neo4j. Creates node entities with @Node annotation, defines relationships with @Relationship, writes Cypher queries using @Query, configures imperative and reactive Neo4j repositories, implements graph traversal patterns, and sets up testing with embedded databases.
85%
Provides comprehensive Tailwind CSS utility-first styling patterns including responsive design, layout utilities, flexbox, grid, spacing, typography, colors, and modern CSS best practices. Use when styling React/Vue/Svelte components, building responsive layouts, implementing design systems, or optimizing CSS workflow.
87%
Skill for creating and managing a Design System using Tailwind CSS and shadcn/ui. Use when defining design tokens, setting up theming with CSS variables, building a consistent UI component library, initializing a design system configuration, or wrapping shadcn/ui components into design system primitives.
93%
Provides comprehensive Turborepo monorepo management guidance for TypeScript/JavaScript projects. Use when creating Turborepo workspaces, configuring turbo.json tasks, setting up Next.js/NestJS apps, managing test pipelines (Vitest/Jest), configuring CI/CD, implementing remote caching, or optimizing build performance in monorepos
85%
Generates comprehensive TypeScript documentation using JSDoc, TypeDoc, and multi-layered documentation patterns for different audiences. Use when creating API documentation, architectural decision records (ADRs), code examples, and framework-specific patterns for NestJS, Express, React, Angular, and Vue.
93%
Provides security review capability for TypeScript/Node.js applications, validates code against XSS, injection, CSRF, JWT/OAuth2 flaws, dependency CVEs, and secrets exposure. Use when performing security audits, before deployment, reviewing authentication/authorization implementations, or ensuring OWASP compliance for Express, NestJS, and Next.js. Triggers on "security review", "check for security issues", "TypeScript security audit".
85%
Provides patterns for unit testing Spring application events. Validates event publishing with ApplicationEventPublisher, tests @EventListener annotation behavior, and verifies async event handling. Use when writing tests for event listeners, mocking application events, or verifying events were published in your Spring Boot services.
88%
Provides patterns for unit testing Jakarta Bean Validation (JSR-380), including @Valid, @NotNull, @Min, @Max, @Email constraints with Hibernate Validator. Generates custom validator tests, constraint violation assertions, validation groups, and parameterized validation tests. Validates data integrity logic without Spring context. Use when writing validation tests, bean validation tests, or testing custom constraint validators.
85%
Provides edge case, corner case, boundary condition, and limit testing patterns for Java unit tests. Validates minimum/maximum values, null cases, empty collections, numeric overflow/underflow, floating-point precision, and off-by-one scenarios using JUnit 5 and AssertJ. Use when writing .java test files to ensure code handles limits, corner cases, and special inputs correctly.
79%
Provides patterns for unit testing Spring Cache annotations (@Cacheable, @CachePut, @CacheEvict). Generates test code that mocks cache managers, verifies cache hit/miss behavior, tests cache key generation with SpEL expressions, validates eviction strategies, and checks conditional caching scenarios. Triggers: caching tests, test Spring cache, mock cache, Spring Boot caching, cache hit/miss verification, @Cacheable testing.
88%
Provides patterns for unit testing `@ConfigurationProperties` classes with `@ConfigurationPropertiesTest`. Validates property binding, tests validation constraints, verifies default values, checks type conversions, and mocks property sources for Spring Boot configuration properties. Use when testing application configuration binding, validating YAML or application.properties files, verifying environment-specific settings, or testing nested property structures.
85%
Provides patterns for unit testing REST controllers using MockMvc and @WebMvcTest. Generates controller tests that validates request/response mapping, validation, exception handling, and HTTP status codes. Use when testing web layer endpoints in isolation for API endpoint testing, Spring MVC tests, mock HTTP requests, or controller layer unit tests.
85%
Provides patterns for unit testing `@ExceptionHandler` and `@ControllerAdvice` in Spring Boot applications. Validates error response formatting, mocks exceptions, verifies HTTP status codes, tests field-level validation errors, and asserts custom error payloads. Use when writing Spring exception handler tests, REST API error tests, or mocking controller advice.
85%
Provides patterns for unit testing JSON serialization/deserialization with Jackson and `@JsonTest`. Validates JSON mapping, custom serializers, date formats, and polymorphic types. Use when testing JSON serialization, validating custom serializers, or writing JSON unit tests in Spring Boot applications.
93%
Provides patterns for unit testing mappers, converters, and bean mappings. Validates entity-to-DTO and model transformation logic in isolation. Generates executable mapping tests with MapStruct and custom converter test coverage. Use when writing mapping tests, converter tests, entity mapping tests, or ensuring correct data transformation between DTOs and domain objects.
85%
Provides parameterized testing patterns with JUnit 5, generates data-driven unit tests using @ParameterizedTest, @ValueSource, @CsvSource, @MethodSource. Creates tests that run the same logic with multiple input values. Use when writing data-driven Java tests, multiple test cases from single method, or boundary value analysis.
88%
Provides patterns for unit testing Spring `@Scheduled` and `@Async` methods using JUnit 5, CompletableFuture, Awaitility, and Mockito. Covers mocking task execution and timing, verifying execution counts, testing cron expressions, validating retry behavior, and simulating thread pool behavior. Use when testing background tasks, cron jobs, periodic execution, scheduled tasks, or thread pool behavior.
88%
Provides patterns for unit testing service layer with Mockito. Creates isolated tests that mock repository calls, verify method invocations, test exception scenarios, and stub external API responses. Use when testing service behaviors and business logic without database or external services.
90%
Provides patterns for testing utility classes, static methods, and helper functions. Validates pure functions, null handling, edge cases, and boundary conditions. Generates AssertJ assertions and @ParameterizedTest for string utils, math utils, validators, and collection helpers. Use when testing utils, test helpers, helper functions, static methods, or verifying utility code correctness.
88%
Provides patterns for unit testing external REST APIs using WireMock. Stubs API responses, verifies request details, simulates failures (timeouts, 4xx/5xx errors), and validates HTTP client behavior without real network calls. Use when testing service integrations with external APIs or mocking HTTP endpoints.
85%
Provides patterns and configurations for running WireMock as a standalone Docker container. Generates mock HTTP endpoints, creates stub mappings for testing, validates integration scenarios, and simulates error conditions. Use when you need to mock APIs, create a mock server, stub external services, simulate third-party APIs, or fake API responses for integration testing.
79%
Provides WordPress theme development patterns using Sage (roots/sage) framework. Use when creating, modifying, or debugging WordPress themes with Sage, including (1): creating new Sage themes from scratch, (2): setting up Blade templates and components, (3): configuring build tools (Vite, Bud), (4): working with WordPress theme templates and hierarchy, (5): implementing ACF fields integration, (6): theme customization and asset management.
90%
Creates reusable Zod v4 schemas, validates API payloads, forms, and configuration input, transforms and coerces data safely, and handles validation errors with strong type inference for TypeScript applications. Use when designing validation layers, parsing `z.string()`, `z.object()`, or `z.email()` schemas, or implementing runtime type-safe data validation.
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an excellent skill description that clearly defines its scope around prompt engineering workflows, lists specific concrete capabilities, and includes a comprehensive 'Use when...' clause with natural trigger terms. It uses proper third-person voice throughout and is concise without being vague. The description would allow Claude to confidently select this skill from a large pool when users need prompt engineering assistance.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'write, debug, and optimize prompts', 'few-shot example selection', 'chain-of-thought structuring', 'system prompt design', and 'template composition'. These are clearly defined capabilities. | 3 / 3 |
Completeness | Clearly answers both 'what' (workflows to write, debug, and optimize prompts with specific sub-capabilities) and 'when' (explicit 'Use when...' clause listing multiple trigger scenarios). Both halves are well-developed. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'write or improve a prompt', 'few-shot examples', 'chain-of-thought', 'system prompts', 'prompt templates', 'get better results from an LLM'. These are all phrases users naturally use when seeking prompt engineering help. | 3 / 3 |
Distinctiveness Conflict Risk | The skill occupies a clear niche around prompt engineering for LLMs. Terms like 'few-shot examples', 'chain-of-thought', 'system prompt design', and 'prompt templates' are highly specific to this domain and unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
42%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill has good structural organization and progressive disclosure, with clear references to supporting files. However, it is significantly too verbose for Claude, explaining well-known concepts at length and using abstract placeholder templates instead of concrete, executable examples. The workflows provide reasonable sequencing but lack the specificity and concrete validation tooling needed for full actionability.
Suggestions
Cut explanatory text that describes concepts Claude already knows (e.g., what few-shot learning is, what CoT reasoning is, what system prompts do) — keep only the novel decision rules and concrete patterns.
Replace placeholder-heavy templates (e.g., `{break_down_the_problem}`, `{detailed_reasoning}`) with fully worked, copy-paste-ready examples for at least one real domain.
Add concrete validation commands or scripts to the workflows — e.g., a specific Python snippet or shell command to measure accuracy across test cases, rather than just saying 'measure performance'.
Remove the 'Integration with Other Skills' section entirely — it adds no actionable guidance and wastes tokens on cross-references Claude can discover on its own.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is excessively verbose for its target audience (Claude). It explains concepts Claude already knows well (what few-shot learning is, what chain-of-thought reasoning is, what system prompts are). Many sections are padded with generic advice ('Measure baseline performance before optimization attempts', 'Implement single-variable changes for accurate attribution') that Claude inherently understands. The 'Integration with Other Skills' section and repeated references to the same files add bloat. The template examples use placeholder variables rather than being truly actionable, making them filler. | 1 / 3 |
Actionability | The sentiment classification few-shot example is concrete and usable, and the workflows provide step-by-step processes. However, most templates use abstract placeholders (e.g., `{break_down_the_problem}`, `{detailed_reasoning}`) that are essentially pseudocode rather than executable guidance. The optimization workflow describes what to do conceptually but doesn't provide concrete commands, scripts, or measurement tools. The CoT template and system prompt framework are structural outlines rather than copy-paste-ready artifacts. | 2 / 3 |
Workflow Clarity | The three workflows are clearly sequenced with numbered steps, and Workflow 2 includes a feedback loop (revert if accuracy < baseline, return to step 2 if < 90%). However, validation checkpoints are somewhat vague — 'test with at least 3 inputs' lacks specifics on how to measure or what tools to use. The quality gates section adds concrete thresholds (>90% accuracy, <5% variance) but these aren't integrated into the workflow steps as explicit checkpoints. Missing concrete validation mechanisms for the 'Scale Prompt Systems' workflow. | 2 / 3 |
Progressive Disclosure | The skill effectively uses progressive disclosure by keeping the main workflow in SKILL.md and pointing to five clearly-labeled reference files for deeper pattern details. References are one level deep, well-signaled throughout the document (inline mentions plus a dedicated Resources section), and the instruction to 'load the targeted reference files only for the pattern you are applying' is explicit. Content is appropriately split between overview and detailed references. | 3 / 3 |
Total | 8 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
Total | 10 / 11 Passed | |
Reviewed
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