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933 results in Sub-Agents, Identity, Hooks, Evals · page 22 of 39
memory-manager
PROACTIVELY USE this agent when project memory becomes cluttered, CLAUDE.md files are getting too long, outdated information needs to be removed, project knowledge needs reorganization for better efficiency, or when optimizing information organization for long-term projects. This agent MUST BE USED for memory optimization and knowledge organization tasks. Examples: <example>Context: Project has accumulated extensive documentation and context over time. user: 'Our CLAUDE.md file is getting very long and contains outdated information mixed with current requirements' assistant: 'I'll use the memory-manager agent to organize and optimize our project memory for better efficiency.' <commentary>Since project memory needs optimization and organization, use the memory-manager to curate and structure information.</commentary></example> <example>Context: User notices Claude is struggling with information retrieval due to cluttered context. user: 'I feel like Claude is having trouble finding relevant information quickly because there's too much context' assistant: 'Let me use the memory-manager agent to streamline and organize our project knowledge for more efficient access.' <commentary>The memory-manager should be used to optimize information organization and improve retrieval efficiency.</commentary></example>
memory-persistence-bundle
Hook bundle: Reference lifecycle hook definitions for ECC memory persistence. The production hook graph is hooks/hooks.json.. Use to persist and reload agent session state across compaction.
minecraft-bukkit-pro
Master Minecraft server plugin development with Bukkit, Spigot, and Paper APIs. Specializes in event-driven architecture, command systems, world manipulation, player management, and performance optimization. Use PROACTIVELY for plugin architecture, gameplay mechanics, server-side features, or cross-version compatibility.
ml-data-expert
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.
mle-reviewer
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.
mlops-engineer
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.
mobile-developer
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.
mobile-security-coder
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.
moc-agent
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>
model-evaluator
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>