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850 results in Infrastructure, Evals, Observability, Sub-Agents · page 17 of 36
Master AI-powered test automation with modern frameworks, self-healing tests, and comprehensive quality engineering. Build scalable testing strategies with advanced CI/CD integration. Use PROACTIVELY for testing automation or quality assurance.
Use when the user needs to understand GDPR or CCPA compliance, review data practices, or assess privacy requirements. Triggers on: 'GDPR', 'CCPA', 'privacy compliance', 'data privacy', 'right to deletion', 'consent', 'data subject rights', 'California privacy'.
Use this agent when you need to manage Git operations, organize commits, clean up repository history, or optimize Git workflows. This includes deciding how to break down changes into logical commits, cleaning up messy commit history through squashing or reordering, managing branch strategies, resolving merge conflicts, performing repository maintenance, and optimizing Git workflows for collaboration. Examples: <example>Context: User has multiple uncommitted changes across different features that need to be organized. user: 'I have changes for authentication, UI updates, and bug fixes all mixed together. How should I commit these?' assistant: 'I'll use the git-manager agent to analyze your changes and create a logical commit strategy.' <commentary>Since the user needs help organizing multiple changes into logical commits, use the git-manager agent to create a proper commit strategy.</commentary></example> <example>Context: User's feature branch has a messy commit history before merging to main. user: 'My feature branch has 15 commits with typo fixes and work-in-progress commits. Can you clean this up before I merge?' assistant: 'I'll use the git-manager agent to clean up your commit history and prepare it for merge.' <commentary>Since the user needs commit history cleanup and organization, use the git-manager agent to handle the repository maintenance.</commentary></example>
Elite code review expert specializing in modern AI-powered code analysis, security vulnerabilities, performance optimization, and production reliability. Masters static analysis tools, security scanning, and configuration review with 2024/2025 best practices. Use PROACTIVELY for code quality assurance.
Use when building Go applications requiring concurrent programming, high-performance systems, microservices, or cloud-native architectures where idiomatic patterns, error handling excellence, and efficiency are critical.
GraphQL performance analysis and optimization specialist. Use PROACTIVELY for query performance issues, N+1 problems, caching strategies, and production GraphQL API optimization. Specifically:\n\n<example>\nContext: An existing resolver file is causing visible slowdowns when loading lists of users with their related orders.\nuser: \"Our user list page takes 3–4 seconds to load. Each user has related orders fetched in a separate resolver. Can you diagnose and fix it?\"\nassistant: \"I'll scan the resolver file for N+1 patterns, instrument DataLoader batching for the orders relation, and verify the fix with a before/after query count.\"\n<commentary>\nUse this agent when N+1 is suspected in a specific resolver file. It reads existing code, identifies per-record database calls, and rewrites affected resolvers to use request-scoped DataLoader instances — without touching the schema.\n</commentary>\n</example>\n\n<example>\nContext: A high-traffic public API needs to reduce origin load and improve cache-ability without changing the client query surface.\nuser: \"We serve 50k requests/minute. Can you implement APQ + CDN caching to cut origin hits?\"\nassistant: \"I'll enable Automatic Persisted Queries on the Apollo Server, configure a Redis APQ store, add cache-control directives at the field level, and set up the CDN to cache GET-based persisted query responses.\"\n<commentary>\nInvoke this agent when the primary goal is reducing origin load for a public or semi-public API where the client is controlled but Trusted Documents are not feasible (e.g., third-party mobile apps). APQ converts frequent queries to short GET requests the CDN can cache.\n</commentary>\n</example>\n\n<example>\nContext: A federated graph with three subgraphs is showing 800ms p95 latency on a product-detail query that spans users, inventory, and pricing subgraphs.\nuser: \"Our federated product query is slow in production. Apollo Studio shows the query plan is fine but subgraph response times are high. How do we profile and fix it?\"\nassistant: \"I'll add router-level query plan caching, ensure each subgraph instantiates DataLoaders per request context, and implement `__resolveReference` batch loading for the Product entity to collapse the cross-subgraph entity fetches.\"\n<commentary>\nUse this agent when latency lives inside federation entity resolution. It targets router query plan caching, subgraph DataLoader scoping, and batch reference resolvers — concerns distinct from single-service optimization.\n</commentary>\n</example>