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Found 151 Skills
LLM Wiki — persistent markdown knowledge base that compounds across sessions (Karpathy model)
Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform
Evaluate pricing changes using financial impact analysis - ARPU/ARPA, conversion, churn risk, NRR, and payback. Recommends go/no-go on pricing decisions.
Design and implement straight-through processing and operational automation for securities operations. Use when measuring STP rates and identifying manual touchpoints in an existing process, replacing review-all workflows with exception-based processing, selecting automation patterns for account opening trade processing settlement reconciliation or billing, designing integration between portfolio management custodian CRM and order management systems, building exception queuing categorization and auto-resolution workflows, evaluating RPA vs API-based vs hybrid automation for legacy systems, establishing operational controls and audit trails for automated environments, conducting process mining or root cause analysis on exception volumes, or setting STP rate targets and continuous improvement programs.
Use when "experiment tracking", "MLflow", "Weights & Biases", "wandb", "model registry", "hyperparameter logging", "ML experiments", "training metrics"
Analyze a Karpathy-pattern LLM wiki knowledge base and generate an interactive knowledge graph with entity extraction, implicit relationships, and topic clustering.
Refactor Scikit-learn and machine learning code to improve maintainability, reproducibility, and adherence to best practices. This skill transforms working ML code into production-ready pipelines that prevent data leakage and ensure reproducible results. It addresses preprocessing outside pipelines, missing random_state parameters, improper cross-validation, and custom transformers not following sklearn API conventions. Implements proper Pipeline and ColumnTransformer patterns, systematic hyperparameter tuning, and appropriate evaluation metrics.
Run the canonical NVIDIA AOI three-phase training pipeline — Phase 1 AutoML baseline (HPO), Phase 2 DEFT loop (RCA → SDG → mining → plain-train retrain), Phase 3 AutoML refinement on the DEFT-augmented dataset. This is the default entry point for any "run the AOI workflow", "fine-tune my PCB AOI model end-to-end", "improve my AOI ChangeNet model", or "AOI workflow with AutoML" request — route here instead of tao-run-deft-aoi directly unless the user explicitly asks for the DEFT loop ONLY (e.g. "run JUST the DEFT loop", "skip AutoML, only DEFT"). Also handles the same three-phase pattern for non-AOI DEFT applications — AutoML baseline then DEFT loop warm-started from AutoML's winning HPs then post-DEFT AutoML refinement on the iteration-augmented dataset. Trigger phrases include "run the AOI workflow", "AOI end-to-end", "AutoML + DEFT", "AutoML then DEFT", "tune hyperparameters then DEFT", "DEFT with AutoML at both ends", "warm-start DEFT", "improve my AOI model".
Run AutoML / hyperparameter optimization (HPO) for NVIDIA TAO networks using AutoMLRunner. Handles algorithm selection (bayesian, hyperband, asha, bohb, llm, hybrid, autoresearch), WandB experiment tracking, job execution on any TAO SDK platform, result interpretation, and per-rec custom evaluation hooks. Use when the user mentions TAO AutoML, hyperparameter optimization, HPO, automl, automl_settings, AutoMLRunner, tao_automl, bayesian search, hyperband, ASHA, LLM-guided search, autoresearch, or wants to tune training hyperparameters for any TAO network. Platform-agnostic — runs on any SDK (Lepton, Brev, SLURM, Kubernetes, Docker).
Translate A/B test lift percentages into annualized dollar projections. Shows conservative and optimistic revenue impact, break-even analysis, and opportunity cost of waiting.
Platform abstraction decision-making for Amethyst KMP project. Guides when to abstract vs keep platform-specific, source set placement (commonMain, jvmAndroid, platform-specific), expect/actual patterns. Covers primary targets (Android, JVM/Desktop, iOS) with web/wasm future considerations. Integrates with gradle-expert for dependency issues. Triggers on: abstraction decisions ("should I share this?"), source set placement questions, expect/actual creation, build.gradle.kts work, incorrect placement detection, KMP dependency suggestions.
Use this skill when when asked to read an arxiv paper given an arxiv URL