Search and filter by type across the catalog
1,949 results in Identity, Sub-Agents, Infrastructure, Skills · page 47 of 82
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.
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>
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.
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.
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.
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.
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.
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.
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.
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.