🧠 Activate Yann LeCun's cognitive framework — Father of Convolutional Neural Networks, Meta Chief AI Scientist, one of the three pioneers of deep learning. Applicable scenarios: Computer vision design, convolutional network architecture, self-supervised learning, solving open-ended problems, balancing engineering and theory. Core paradigm: Convolutional inductive bias + self-supervised learning + energy models + engineering pragmatism.

yfyang860

Machine Learning

Plan Small Patch

Use esta skill quando for necessário propor uma correção localizada, conservadora e revisável.

matheus-ndc0

Machine Learning

Doc Visual Png

Deprecated skill kept only for legacy reference. Do not use for new doc visuals; prefer `nano-banana-2`, then `nano-banana`, then `ai-image-generation`, with flow/diagram fallback when image-first is not the right fit.

mthang18010

Machine Learning

Deep Research Ldr Automem

Use when the task is deep-research or hybrid, when prior internal decisions may matter, when the user asks for comparison or architecture investigation, or when LDR-style research with conditional AutoMem recall is required.

mud-the-developer0

Machine Learning

Tmw Helix Backend Dual Track

Use this skill when a feature requires coordinated changes in teachmewow-helix and teachmewow-agent. It enforces parallel track planning, per-repo checklists, and final convergence validation.

MatheusOliveiraSilva0

Machine Learning

Payment Validator

Use this skill when modifying payment states, implementing verification flows, or calculating late statuses in the Aegis platform.

muhammadcaeed0

Machine Learning

Continuous Learning V2

Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents.

unju-ai0

Machine Learning

Continuous Learning

Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.

tomevault-io0

Machine Learning

Fixture Golden Tests

Maintain fixture-based regression testing for deterministic extraction and generation outputs.

OnePunchForAll0

Machine Learning

Lmms Eval Guide

Guides AI coding agents through the lmms-eval codebase - a unified evaluation framework for Large Multimodal Models (LMMs). Use when integrating new models, adding evaluation tasks/benchmarks, using the HTTP eval server, or navigating the evaluation pipeline architecture.

onlyairnopods0

Machine Learning

Nemo Curator

GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

tomevault-io0

Machine Learning

Ml Strategy

Machine-learning predictive strategy based on sklearn walk-forward training, feature engineering, and signal generation. Suitable for any OHLCV data.

JacobHsu0

Machine Learning

Ax Agent Optimize

This skill helps an LLM generate correct AxAgent tuning and evaluation code using @ax-llm/ax. Use when the user asks about agent.optimize(...), judgeOptions, eval datasets, optimization targets, saved optimizedProgram artifacts, or recursive optimization guidance.

jadecli0

Machine Learning

Ax Learn

This skill helps an LLM generate correct AxLearn code using @ax-llm/ax. Use when the user asks about self-improving agents, trace-backed learning, feedback-aware updates, or AxLearn modes.

jadecli0

Machine Learning

Ax Gen

This skill helps an LLM generate correct AxGen code using @ax-llm/ax. Use when the user asks about ax(), AxGen, generators, forward(), streamingForward(), assertions, field processors, step hooks, self-tuning, or structured outputs.

jadecli0

Machine Learning

Aeon Time Series Machine Learning

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

tomevault-io0

Machine Learning

Ray Train

Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.

tomevault-io0

Machine Learning

Knowledge Distillation: Compressing LLMs

Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.

tomevault-io0

Machine Learning

Model Merging: Combining Pre-trained Models

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.

tomevault-io0

Machine Learning

Regression Modeling

Build predictive models using linear regression, polynomial regression, and regularized regression for continuous prediction, trend forecasting, and relationship quantification

Underwater-AI0

Machine Learning

Technoir Aesthetic System

Skill for implementing the unified RHIVE Tech-Noir design system including Glassmorphism and Circuitry logic.

jamesgimena0

Machine Learning

Feature Engineering

Create and transform features using encoding, scaling, polynomial features, and domain-specific transformations for improved model performance and interpretability

Underwater-AI0

Machine Learning

Recommendation Engine

Build recommendation systems using collaborative filtering, content-based filtering, matrix factorization, and neural network approaches

Underwater-AI0

Machine Learning

Gear Math

Deterministic mechanical gear math for spur, helical, bevel, and worm geometries with CAD-ready dimension outputs.

JamesNapoletano0

Machine Learning

Cad Geometry

CAD-oriented dimensioning guidance and validation patterns for mechanical parts, with emphasis on gear geometry outputs.

JamesNapoletano0

Machine Learning

Speckit Clarify

Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.

peancharoen0

Machine Learning Skills | Skills Pool

Machine Learning

Machine Learning

Angular

Model Router

Voice Cloner Tts

Source Fix Remediation Workflow

Yann Le Cun

Plan Small Patch

Doc Visual Png

Deep Research Ldr Automem

Tmw Helix Backend Dual Track

Payment Validator

Continuous Learning V2

Continuous Learning

Fixture Golden Tests

Lmms Eval Guide

Nemo Curator

Ml Strategy

Ax Agent Optimize

Ax Learn

Ax Gen

Aeon Time Series Machine Learning

Ray Train

Knowledge Distillation: Compressing LLMs

Model Merging: Combining Pre-trained Models

Regression Modeling

Technoir Aesthetic System

Feature Engineering

Recommendation Engine

Gear Math

Cad Geometry

Speckit Clarify