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Found 98 Skills
LLM Wiki — persistent markdown knowledge base that compounds across sessions (Karpathy model)
Use when "experiment tracking", "MLflow", "Weights & Biases", "wandb", "model registry", "hyperparameter logging", "ML experiments", "training metrics"
Design experiment plans with progressive stages — initial implementation, baseline tuning, creative research, and ablation studies. Plan baselines, datasets, hyperparameter sweeps, and evaluation metrics. Use when planning experiments for a research paper.
Guides structured 4-stage experiment execution with attempt budgets and gate conditions: Stage 1 initial implementation (reproduce baseline), Stage 2 hyperparameter tuning, Stage 3 proposed method validation, Stage 4 ablation study. Integrates with evo-memory (load prior strategies, trigger IVE/ESE) and experiment-craft (5-step diagnostic on failure). Use when: user has a planned experiment, needs to reproduce baselines, organize experiment workflow, or systematically validate a method. Do NOT use for debugging a specific experiment failure (use experiment-craft) or designing which experiments to run (use paper-planning).
Implements the Syncfusion WPF ColorPickerPalette control for color selection from themed and standard color palettes. Use this when adding color pickers with predefined palettes, customizing color options, or handling color selection events in WPF applications. Covers setup, color management, appearance customization, and interaction patterns.
Guide implementation of the Syncfusion WinUI Color Palette control (SfColorPalette) for swatch-based color selection in Windows desktop applications. Use this skill when working with theme colors, standard colors, custom color palettes, or the More Colors dialog. Covers color palette setup, theme color support, standard color configurations, UI customization, and best practices.
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.
Run any question, idea, or decision through a council of 5 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict. Based on Karpathy's LLM Council methodology. MANDATORY TRIGGERS: 'council this', 'run the council', 'war room this', 'pressure-test this', 'stress-test this', 'debate this'. STRONG TRIGGERS (use when combined with a real decision or tradeoff): 'should I X or Y', 'which option', 'what would you do', 'is this the right move', 'validate this', 'get multiple perspectives', 'I can't decide', 'I'm torn between'. Do NOT trigger on simple yes/no questions, factual lookups, or casual 'should I' without a meaningful tradeoff (e.g. 'should I use markdown' is not a council question). DO trigger when the user presents a genuine decision with stakes, multiple options, and context that suggests they want it pressure-tested from multiple angles.
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.
Autonomous Goal-directed Iteration. Apply Karpathy's autoresearch principles to ANY task. Loops autonomously — modify, verify, keep/discard, repeat. Supports optional loop count via Claude Code's /loop command.
Use this skill when when asked to read an arxiv paper given an arxiv URL
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.