ML Ops
Machine learning operations
HuggingFace Model Download
by TrevorS
When downloading models from HuggingFace, use this skill to get the correct
performance-scaling
by bejranonda
Cross-model performance optimization and scaling configurations for autonomous agents
expert-routing
by Harryoung
Domain expert routing. When the knowledge base cannot answer user questions, find and notify the corresponding expert based on the question domain. Only available in IM mode. Trigger condition: No results in 6-stage retrieval.
writing-style
by stevenmays
Write in Steven's voice—pragmatic, curious, pedagogical. Opens with measurable payoffs, builds mental models from first principles, uses worked examples, and handles uncertainty honestly. Use for essays, blog posts, and technical articles.
qlora
by itsmostafa
Memory-efficient fine-tuning with 4-bit quantization and LoRA adapters. Use when fine-tuning large models (7B+) on consumer GPUs, when VRAM is limited, or when standard LoRA still exceeds memory. Builds on the lora skill.
lora
by itsmostafa
Parameter-efficient fine-tuning with Low-Rank Adaptation (LoRA). Use when fine-tuning large language models with limited GPU memory, creating task-specific adapters, or when you need to train multiple specialized models from a single base.
pytorch
by itsmostafa
Building and training neural networks with PyTorch. Use when implementing deep learning models, training loops, data pipelines, model optimization with torch.compile, distributed training, or deploying PyTorch models.
rlhf
by itsmostafa
Understanding Reinforcement Learning from Human Feedback (RLHF) for aligning language models. Use when learning about preference data, reward modeling, policy optimization, or direct alignment algorithms like DPO.
mlx
by itsmostafa
Running and fine-tuning LLMs on Apple Silicon with MLX. Use when working with models locally on Mac, converting Hugging Face models to MLX format, fine-tuning with LoRA/QLoRA on Apple Silicon, or serving models via HTTP API.
transformers
by itsmostafa
Loading and using pretrained models with Hugging Face Transformers. Use when working with pretrained models from the Hub, running inference with Pipeline API, fine-tuning models with Trainer, or handling text, vision, audio, and multimodal tasks.
codex
by nokonoko1203
"Delegate to Codex CLI for implementation, testing, review, or design consultation. Triggers: codex, ask codex, codex review, review this, second opinion"
pict-test-designer
by omkamal
Design comprehensive test cases using PICT (Pairwise Independent Combinatorial Testing) for any piece of requirements or code. Analyzes inputs, generates PICT models with parameters, values, and constraints for valid scenarios using pairwise testing. Outputs the PICT model, markdown table of test cases, and expected results.
algorithm-designer
by Data-Wise
Design and document statistical algorithms with pseudocode and complexity analysis
identification-theory
by Data-Wise
DAG and potential outcomes frameworks for causal mediation identification
convert-elm-roc
by aRustyDev
Bidirectional conversion between Elm and Roc. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Elm↔Roc specific patterns. Use when migrating Elm frontend code to Roc applications, translating browser-based Elm to platform-agnostic Roc, or refactoring Elm web applications to Roc CLI/native tools. Extends meta-convert-dev with Elm-to-Roc specific patterns.
asymptotic-theory
by Data-Wise
M-estimation, influence functions, and semiparametric efficiency theory for causal inference
cross-disciplinary-ideation
by Data-Wise
Field connection mapping and systematic ideation for method transfer
deep-reading-analyst
by ginobefun
"Comprehensive framework for deep analysis of articles, papers, and long-form content using 10+ thinking models (SCQA, 5W2H, critical thinking, inversion, mental models, first principles, systems thinking, six thinking hats). Use when users want to: (1) deeply understand complex articles/content, (2) analyze arguments and identify logical flaws, (3) extract actionable insights from reading materials, (4) create study notes or learning summaries, (5) compare multiple sources, (6) transform knowledge into practical applications, or (7) apply specific thinking frameworks. Triggered by phrases like 'analyze this article,' 'help me understand,' 'deep dive into,' 'extract insights from,' 'use [framework name],' or when users provide URLs/long-form content for analysis."
python-notebooks-async
by ahgraber
"Use when writing or reviewing asyncio code in Jupyter notebooks or '#%%' cell workflows — structuring event-loop ownership, orchestrating async tasks, or choosing compatibility strategies. Also use when hitting RuntimeError: This event loop is already running, asyncio.run() failures in cells, or tasks silently never completing."
cc-proxy-detector
by zxc123aa
检测 Claude Code 中转站的真实后端来源（Anthropic 官方 / AWS Bedrock Kiro / Google Vertex AI Antigravity / 疑似伪装）。当用户想要检测中转站渠道来源、验证 API 是否为官方直连、识别中转站伪装、扫描多模型混合渠道时使用。触发词包括"检测渠道"、"检测中转"、"proxy detect"、"渠道来源"、"是不是官方"。
Ai Image Generation
by omer-metin
model-serving
by ancoleman
LLM and ML model deployment for inference. Use when serving models in production, building AI APIs, or optimizing inference. Covers vLLM (LLM serving), TensorRT-LLM (GPU optimization), Ollama (local), BentoML (ML deployment), Triton (multi-model), LangChain (orchestration), LlamaIndex (RAG), and streaming patterns.
implementing-mlops
by ancoleman
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
creating-financial-models
by ronnycoding
This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions