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894 results in Hooks, Memory, Sub-Agents · page 21 of 38
Expert en Machine Learning et Data Science avec Python. DOIT ÊTRE UTILISÉ pour l'analyse de données, les modèles ML/AI, le traitement de données, la visualisation avancée, et l'intelligence artificielle. Maîtrise scikit-learn, TensorFlow, PyTorch, pandas, numpy, et l'écosystème data science moderne.
Production machine-learning engineering reviewer for data contracts, feature pipelines, training reproducibility, offline/online evaluation, model serving, monitoring, and rollback. Use when ML, MLOps, model training, inference, feature store, or evaluation code changes.
Use this agent when you need to design and implement ML infrastructure, set up CI/CD for machine learning models, establish model versioning systems, or optimize ML platforms for reliability and automation. Invoke this agent to build production-grade experiment tracking, implement automated training pipelines, configure GPU resource orchestration, and establish operational monitoring for ML systems.
Use this agent when building cross-platform mobile applications requiring native performance optimization, platform-specific features, and offline-first architecture. Use for React Native and Flutter projects where code sharing must exceed 80% while maintaining iOS and Android native excellence.
Expert in secure mobile coding practices specializing in input validation, WebView security, and mobile-specific security patterns. Use PROACTIVELY for mobile security implementations or mobile security code reviews.
Obsidian Map of Content specialist. Use PROACTIVELY when a vault needs new MOCs created, existing MOCs updated, orphaned assets organized, or the overall MOC navigation network audited. Specifically:\n\n<example>\nContext: A developer has added dozens of new notes across several topic folders but no MOC exists to tie them together.\nuser: \"I have a bunch of new AI notes scattered around the vault but there's no top-level MOC for them. Can you sort this out?\"\nassistant: \"I'll use the moc-agent to scan the vault for directories missing MOCs, generate a properly formatted AI Development MOC, and link it into the master index.\"\n<commentary>\nUse moc-agent whenever a directory has grown beyond a handful of notes without a navigation hub. The agent discovers coverage gaps with Glob/Grep and creates spec-compliant MOCs without requiring any external scripts.\n</commentary>\n</example>\n\n<example>\nContext: A knowledge base has accumulated hundreds of unlinked images that are invisible to navigation.\nuser: \"My vault has tons of PNG screenshots and diagrams that aren't linked anywhere. They're just sitting in an attachments folder.\"\nassistant: \"I'll use the moc-agent to identify every orphaned image asset, categorize them by type, and create gallery notes that surface them through the MOC network.\"\n<commentary>\nInvoke moc-agent for orphaned asset triage — it applies a structured gallery-note pattern that reintegrates visual assets into the vault's navigation without moving files.\n</commentary>\n</example>\n\n<example>\nContext: After a large import, MOCs are stale and no longer reflect the current note set.\nuser: \"I just imported 200 notes from Notion. My existing MOCs are out of date and missing most of the new content.\"\nassistant: \"I'll use the moc-agent to diff each existing MOC against the current file tree, add missing note links, prune dead links, and flag any topic areas that need a brand-new MOC.\"\n<commentary>\nUse moc-agent for post-import reconciliation. It audits existing MOCs against live vault content and repairs coverage gaps systematically.\n</commentary>\n</example>
AI model evaluation and benchmarking specialist. Use when selecting the right model for a specific task, designing evaluation benchmarks from scratch, or running post-deployment regression testing. Specifically:\n\n<example>\nContext: A product team needs to choose between Claude Sonnet, GPT-4o, and Gemini 1.5 Pro for a customer support summarization pipeline with a $500/month budget\nuser: \"We need to pick a model for our customer support summarization system. We process 50k tickets/month and need under 2s latency.\"\nassistant: \"I'll start by establishing your success criteria and constraints: accuracy threshold for summarization quality, acceptable hallucination rate, latency P95 target, and cost ceiling. Then I'll design a representative test set of 200+ real tickets (with human-labeled reference summaries), run systematic evaluation against Claude Haiku, Claude Sonnet, GPT-4o-mini, and GPT-4o using ROUGE-L, BERTScore, and human eval, and produce a cost-per-unit vs quality Pareto curve so you can make an informed trade-off decision.\"\n<commentary>\nInvoke model-evaluator when the primary need is picking the best model for a defined task with measurable criteria. Contrast with llm-architect (who designs the serving infrastructure and integration patterns) and prompt-engineer (who optimizes prompts for a chosen model).\n</commentary>\n</example>\n\n<example>\nContext: An ML team is building an internal coding assistant and needs to benchmark several open-source and proprietary code models before committing to infrastructure\nuser: \"Design a benchmark for evaluating code generation models for our internal developer tooling. We care about Python, TypeScript, and SQL.\"\nassistant: \"I'll design a benchmark using HumanEval+ and custom enterprise test cases across Python, TypeScript, and SQL. Evaluation will cover functional correctness (pass@1, pass@5), syntax validity, idiomatic style, and security anti-patterns. I'll set up the EleutherAI lm-evaluation-harness for open-weight models and a Promptfoo config for API models, report results with 95% confidence intervals, and flag statistically significant differences using Cohen's d.\"\n<commentary>\nUse model-evaluator when the task is designing the evaluation methodology itself — test set composition, metric selection, statistical rigor. This is distinct from llm-architect who would design the serving layer once the model is chosen.\n</commentary>\n</example>\n\n<example>\nContext: A deployed LLM pipeline has started producing lower quality outputs after a model provider silently updated their model weights\nuser: \"Our summarization quality scores dropped 8% last week. We think the model changed. How do we confirm and decide whether to roll back or switch models?\"\nassistant: \"I'll set up a regression evaluation: run your existing golden test set against the current model version and compare against your stored baseline scores. I'll use paired statistical tests (Wilcoxon signed-rank) to confirm the degradation is significant, identify which input categories regressed most, then benchmark two alternative models as candidates. I'll also add Promptfoo CI regression checks and Arize Phoenix drift alerts so this is caught automatically going forward.\"\n<commentary>\nInvoke model-evaluator for post-deployment regression investigations and re-evaluation cycles. The agent handles both diagnosing the degradation and designing the monitoring to prevent recurrence, handing off infrastructure changes to llm-architect.\n</commentary>\n</example>
Expert in monorepo architecture, build systems, and dependency management at scale. Masters Nx, Turborepo, Bazel, and Lerna for efficient multi-project development. Use PROACTIVELY for monorepo setup, build optimization, or scaling development workflows across teams.
Expert backend architect specializing in scalable API design, microservices architecture, and distributed systems. Masters REST/GraphQL/gRPC APIs, event-driven architectures, service mesh patterns, and modern backend frameworks. Handles service boundary definition, inter-service communication, resilience patterns, and observability. Use PROACTIVELY when creating new backend services or APIs.
Build React components, implement responsive layouts, and handle client-side state management. Masters React 19, Next.js 15, and modern frontend architecture. Optimizes performance and ensures accessibility. Use PROACTIVELY when creating UI components or fixing frontend issues.
Develop React Native, Flutter, or native mobile apps with modern architecture patterns. Masters cross-platform development, native integrations, offline sync, and app store optimization. Use PROACTIVELY for mobile features, cross-platform code, or app optimization.
Neon database architecture specialist. Use PROACTIVELY for database schema design, Drizzle ORM integration, query optimization, and serverless performance tuning. Expert in connection management and database migrations.
General Neon Serverless Postgres consultant. Use PROACTIVELY for initial Neon setup, general database questions, and coordinating with specialized agents (neon-database-architect for schemas/ORM, neon-auth-specialist for authentication).
Safe Postgres migrations with zero-downtime using Neon's branching workflow. Test schema changes in isolated database branches, validate thoroughly, then apply to production—all automated with support for Prisma, Drizzle, or your favorite ORM.