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graph-query
Query the code graph database to understand component relationships, dependencies, and change impact. Use when the user asks to "find callers", "check dependencies", "what uses this", "show relationships", "find serializers", or when reading code and needing to understand what depends on a component before modifications.
graphql
GraphQL gives clients exactly the data they need - no more, no less. One endpoint, typed schema, introspection. But the flexibility that makes it powerful also makes it dangerous. Without proper controls, clients can craft queries that bring down your server. This skill covers schema design, resolvers, DataLoader for N+1 prevention, federation for microservices, and client integration with Apollo/urql. Key insight: GraphQL is a contract. The schema is the API documentation. Design it carefully.
graphql-performance-optimizer
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>
growth-loops
Use when the user wants to design a growth loop, understand PLG mechanics, or build sustainable acquisition. Triggers on: 'growth loop', 'flywheel', 'viral loop', 'PLG growth', 'product-led growth', 'growth mechanics', 'how do we grow', 'word of mouth'.
gtars
High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
guidance
Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework