ML Ops
Machine learning operations
multi-model-research
by krishagel
Orchestrate multiple frontier LLMs (Claude, GPT-5.1, Gemini 3.0 Pro, Perplexity Sonar, Grok 4.1) for comprehensive research using LLM Council pattern with peer review and synthesis
hytale-custom-blocks
by MnkyArts
Create custom block types for Hytale with textures, physics, states, farming, and interactions. Use when asked to "add a custom block", "create a new block type", "make blocks farmable", "add block interactions", or "configure block physics".
tracking-marketing-metrics
by amogha-dalvi
Use when marketing lacks a metrics hierarchy connecting activity to business outcomes, when attribution is last-click or nonexistent, when marketing and sales report different numbers, when dashboards are ignored or full of vanity metrics, or when leading indicators are not tracked. Use when decisions are made on gut feel instead of data.
analyze-prose
by bdmorin
You are an expert writer and editor and you excel at evaluating the quality of writing and other content and providing various ratings and recommendations about how to improve it from a novelty, cl...
manim-video-teacher
by lispking
专注于使用 Manim 生成动画教学视频的完整流程与专业建议。适用于用户用中文提示语让 Codex 生成脚本、分镜、Manim 代码、渲染命令或优化教学视频质量与节奏，并输出 MP4。
call-cursor-agent
by dotneet
Call cursor-agent to perform a task.
ac-complexity-assessor
by adaptationio
Assess feature and project complexity. Use when estimating effort, determining spec pipeline type, calculating cost estimates, or planning resource allocation.
x-algo-ml
by CloudAI-X
Explain the Phoenix ML model architecture for X recommendations. Use when users ask about embeddings, transformers, how predictions work, or ML model details.
agent-cost-optimizer
by adaptationio
Real-time cost tracking, budget enforcement, and ROI measurement for AI agent operations. Track token usage, predict costs, enforce budget caps ($50-70/month typical), optimize model selection, cache results, measure cost-to-value. Use when tracking AI costs, preventing budget overruns, optimizing spend, measuring ROI, or ensuring cost-effective AI operations.
video-agent
by founderjourney
AI content generation suite with 35+ models. Image generation, video creation, audio processing via FAL AI, Google Vertex AI, ElevenLabs. Pipeline orchestration and cost management.
notion-mastery
by founderjourney
Sistema completo de productividad y CRM en Notion con integracion n8n. Usar cuando el usuario necesite gestionar tareas, proyectos, metas, pipeline de ventas, CRM de clientes, prospeccion con Apollo.io, o automatizar workflows entre Notion y otras herramientas. Activa con palabras como Notion, tareas, proyectos, CRM, pipeline, leads, Apollo, prospeccion, follow-up, deals, clientes.
debug:scikit-learn
by SnakeO
Debug Scikit-learn issues systematically. Use when encountering model errors like NotFittedError, shape mismatches between train and test data, NaN/infinity value errors, pipeline configuration issues, convergence warnings from optimizers, cross-validation failures due to class imbalance, data leakage causing suspiciously high scores, or preprocessing errors with ColumnTransformer and feature alignment.
speckit-tasks-zh
by forztf
基于speckit工作流的任务生成技能，用于根据可用设计文档生成可操作的、依赖有序的tasks.md。当需要基于spec.md、plan.md、data-model.md、contracts/等设计文档为功能开发生成详细任务列表时使用此技能。触发词包括"speckit tasks"、"生成任务"、"任务规划"、"功能任务分解"、"创建tasks.md"等。
debug:tensorflow
by SnakeO
Debug TensorFlow and Keras issues systematically. This skill helps diagnose and resolve machine learning problems including tensor shape mismatches, GPU/CUDA detection failures, out-of-memory errors, NaN/Inf values in loss functions, vanishing/exploding gradients, SavedModel loading errors, and data pipeline bottlenecks. Provides tf.debugging assertions, TensorBoard profiling, eager execution debugging, and version compatibility guidance.
refactor:scikit-learn
by SnakeO
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.
debug:pytorch
by SnakeO
Debug PyTorch issues systematically. Use when encountering tensor errors, CUDA out of memory errors, gradient problems like NaN loss or exploding gradients, shape mismatches between layers, device conflicts between CPU and GPU, autograd graph issues, DataLoader problems, dtype mismatches, or training instabilities in deep learning workflows.
refactor:pytorch
by SnakeO
Refactor PyTorch code to improve maintainability, readability, and adherence to best practices. Identifies and fixes DRY violations, long functions, deep nesting, SRP violations, and opportunities for modular components. Applies PyTorch 2.x patterns including torch.compile optimization, Automatic Mixed Precision (AMP), optimized DataLoader configuration, modular nn.Module design, gradient checkpointing, CUDA memory management, PyTorch Lightning integration, custom Dataset classes, model factory patterns, weight initialization, and reproducibility patterns.
skills
by atxinsky
Execute plan in batches with review checkpoints
pyside6-mvc
by DS-codi
"Use this skill when building Python desktop applications using PySide6 with strict MVC architecture where all UI is defined by .ui files. Covers architecture patterns, controller/model/view separation, signal handling, and .ui file workflows."
nuxt-enums
by leeovery
TypeScript enum pattern with Castable interface for model integration. Use when creating enums with behavior methods (colors, labels), defining fixed value sets, or integrating enums with the model casting system.
model-usage
by Heldinhow
Use CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON.
machine-learning
by pluginagentmarketplace
Supervised & unsupervised learning, scikit-learn, XGBoost, model evaluation, feature engineering for production ML
project-development
by ken-cavanagh-glean
Design and build LLM-powered projects from ideation through deployment. Use when starting new agent projects, choosing between LLM and traditional approaches, or structuring batch processing pipelines.
computer-vision
by pluginagentmarketplace
Image processing, object detection, segmentation, and vision models. Use for image classification, object detection, or visual analysis tasks.