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2,024 results in Observability, Skills, Sub-Agents, Hooks · page 47 of 85

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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 My existing MOCs are out of date and missing most of the new content.\"\nassistant: \"I'll use the moc-agent to diff each existing MOC against the current file tree, add missing note links,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>

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obsidian-ops-teamsubagents

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modal

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

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modalskills

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modal-serverless-gpu

Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.

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infrastructure-modalskills

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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>

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ai-specialistssubagents

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model-merging

Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.

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emerging-techniques-model-mergingskills

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model-pruning

Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.

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emerging-techniques-model-pruningskills

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modernization

Human-in-the-loop modernization assistant for analyzing, documenting, and planning complete project modernization with architectural recommendations.

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expert-advisorssubagents

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moe-training

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.

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emerging-techniques-moe-trainingskills

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molfeat

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

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molfeatskills

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monday-bug-fixer

Elite bug-fixing agent that enriches task context from Monday.com platform data. Gathers related items, docs, comments, epics, and requirements to deliver production-quality fixes with comprehensive PRs.

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data-aisubagents

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mongodb-performance-advisor

Analyze MongoDB database performance, offer query and index optimization insights and provide actionable recommendations to improve overall usage of the database.

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programming-languagessubagents

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monitoring-specialist

Monitoring and observability infrastructure specialist. Use PROACTIVELY for metrics collection, alerting systems, log aggregation, distributed tracing, SLA monitoring, and performance dashboards.

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devops-infrastructuresubagents

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monorepo-architect

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,

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monorepo-architectskills

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monorepo-architect

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.

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architecturefrontendperformancesubagent

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moodle-external-api-development

Create custom external web service APIs for Moodle LMS. Use when implementing web services for course management, user tracking, quiz operations, or custom plugin functionality. Covers parameter validation, database operations, error handling, service registration, and Moodle coding standards.

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moodle-external-api-developmentskills

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motion-advanced

Advanced motion patterns for React / Next.js — drag & drop, gestures, text animations, SVG path drawing, custom hooks, imperative sequences (useAnimate), loaders, and the full API decision tree. Requires motion-foundations.

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skill

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motion-canvas

Complete production-ready guide for Motion Canvas with ESM/CommonJS workarounds, full setup templates, and troubleshooting for programmatic video creation using TypeScript

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motion-canvasskills

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motion-foundations

Motion tokens, spring presets, performance rules, device adaptation, accessibility enforcement, and SSR safety for React / Next.js using motion/react. Foundation layer — all other motion skills depend on this.

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skill

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motion-patterns

Production-ready animation patterns for React / Next.js — button, modal, toast, stagger, page transitions, exit animations, scroll, and layout — built on motion-foundations tokens and springs.

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skill

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motion-ui

"Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns."

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skill

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move-code-quality

Analyzes Move language packages against the official Move Book Code Quality Checklist. Use this skill when reviewing Move code, checking Move 2024 Edition compliance, or analyzing Move packages for best practices. Activates automatically when working with .move files or Move.toml manifests.

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move-code-qualityskills

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ms-sql-dba

Work with Microsoft SQL Server databases using the MS SQL extension.

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data-aisubagents

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mui

Material-UI v7 component library patterns including sx prop styling, theme integration, responsive design, and MUI-specific hooks. Use when working with MUI components, styling with sx prop, theme customization, or MUI utilities.

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muiskills

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multi-agent-coordinator

Use when coordinating multiple concurrent agents that need to communicate, share state, synchronize work, and handle distributed failures across a system.

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meta-orchestrationsubagent

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