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1,929 results in Memory, Sub-Agents, Evals, Skills · page 35 of 81
Analyze Google Analytics data, review website performance metrics, identify traffic patterns, and suggest data-driven improvements. Use when the user asks about analytics, website metrics, traffic analysis, conversion rates, user behavior, or performance optimization.
Provides expert guidance on authenticating and authorizing to Google Cloud services and APIs, covering human users, service identities, Application Default Credentials (ADC), and best practices for secure access.
Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs, NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics.
Generates cost optimization guidance for Google Cloud workloads based on the Google Cloud Well-Architected Framework (WAF). Use to evaluate a workload, identify cost requirements and constraints, and provide actionable recommendations for building cost-efficient workloads on Google Cloud.
Generates reliability-focused guidance for Google Cloud workloads based on the Google Cloud Well-Architected Framework. Use to evaluate a workload, identify reliability requirements, and provide actionable recommendations for building resilient, highly available systems.
Generates security-focused guidance for Google Cloud workloads based on the Google Cloud Well-Architected Framework (WAF). Use to evaluate a workload, identify security requirements, and provide actionable recommendations for IAM, network security, data protection, and operational security.
Operate across Google Drive, Docs, Sheets, and Slides as one workflow surface for plans, trackers, decks, and shared documents. Use when the user needs to find, summarize, edit, migrate, or clean up Google Workspace assets without dropping to raw tool calls.
Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.
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 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 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>
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'.
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.
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
Use when a team needs strategic guidance at any stage of a hackathon — from initial ideation through pitch delivery. Specifically:\n\n<example>\nContext: A team of four arrives at a 24-hour AI hackathon with no idea, a vague interest in healthcare, and two hours before the kick-off presentation deadline.\nuser: \"We have no idea yet, the theme is AI for Good, and we need a concept in the next 2 hours. We have two ML engineers, one frontend dev, and a designer.\"\nassistant: \"I need a few details first — what sponsor APIs are available, and is there a specific judging track we must target? Once I have that, I'll generate three ranked concept options with feasibility scores, and we'll lock one in within 15 minutes so the team can start the environment setup.\"\n<commentary>\nInvoke hackathon-ai-strategist for pre-hackathon ideation when the clock is already running and the team needs a structured, fast-converging concept selection process.\n</commentary>\n</example>\n\n<example>\nContext: A team is 14 hours into a 24-hour hackathon. Their original plan involved a real-time video analysis feature that is behind schedule and may not be completable.\nuser: \"We're behind. The video pipeline isn't working and we only have 10 hours left. What do we cut?\"\nassistant: \"Let's triage immediately. Tell me what's working reliably right now, what the demo absolutely needs to show, and which judging criteria are worth the most points. I'll give you a re-scoped MVP plan with explicit cut decisions so the team can align and get back to building within the next 30 minutes.\"\n<commentary>\nUse hackathon-ai-strategist for mid-hackathon triage when a team needs a fast re-scoping decision backed by judging criteria analysis, not a general project manager.\n</commentary>\n</example>\n\n<example>\nContext: A team has a working prototype with 6 hours left before judging. They need to turn it into a compelling 3-minute demo and slide deck.\nuser: \"We have something working. How do we structure the pitch and demo for the next 6 hours?\"\nassistant: \"I'll outline a time-annotated 3-minute pitch structure and a demo reliability checklist. Then we'll split the remaining time: 2 hours on demo stabilization, 2 hours on slides, 1 hour on rehearsal, 1 hour buffer. Walk me through what the product does so I can draft the hook and problem statement.\"\n<commentary>\nInvoke hackathon-ai-strategist when a team transitions from building to presenting and needs a concrete pitch structure, demo script, and rehearsal plan.\n</commentary>\n</example>