ML Ops
Machine learning operations
nemo-curator
by NousResearch
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.
llama-cpp
by NousResearch
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
huggingface-hub
by NousResearch
Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.
lambda-labs-gpu-cloud
by NousResearch
Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
lambda-labs-gpu-cloud
by NousResearch
Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
peft-fine-tuning
by NousResearch
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
clip
by NousResearch
OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
modal-serverless-gpu
by NousResearch
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
livewire-development
by coollabsio
Develops reactive Livewire 3 components. Activates when creating, updating, or modifying Livewire components; working with wire:model, wire:click, wire:loading, or any wire: directives; adding real-time updates, loading states, or reactivity; debugging component behavior; writing Livewire tests; or when the user mentions Livewire, component, counter, or reactive UI.
cortex-recon
by jeremylongshore
ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".
jenkins-pipeline-intro
by jeremylongshore
Manage jenkins pipeline intro operations. Auto-activating skill for DevOps Basics. Triggers on: jenkins pipeline intro, jenkins pipeline intro Part of the DevOps Basics skill category. Use when working with jenkins pipeline intro functionality. Trigger with phrases like "jenkins pipeline intro", "jenkins intro", "jenkins".
python-pipeline
by jamditis
Python data processing pipelines with modular architecture. Use when building content processing workflows, implementing dispatcher patterns, integrating Google Sheets/Drive APIs, or creating batch processing systems. Covers patterns from rosen-scraper, image-analyzer, and social-scraper projects.
running-dbt-commands
by dbt-labs
Use when executing dbt commands via CLI - running models, tests, builds, compiles, or show queries. Use when unsure which dbt executable to use or how to format command parameters.
code-quality
by tursodatabase
General Correctness rules, Rust patterns, comments, avoiding over-engineering. When writing code always take these into account
model-council
by michaelboeding
This skill should be used when the user asks for "model council", "multi-model", "compare models", "ask multiple AIs", "consensus across models", "run on different models", or wants to get solutions from multiple AI providers (Claude, GPT, Gemini, Grok) and compare results. Orchestrates parallel execution across AI models/CLIs and synthesizes the best answer.
llm-cli
by glebis
Process textual and multimedia files with various LLM providers using the llm CLI. Supports both non-interactive and interactive modes with model selection, config persistence, and file input handling.
using-dbt-for-analytics-engineering
by dbt-labs
Use when doing any dbt work - building or modifying models, debugging errors, exploring unfamiliar data sources, writing tests, or evaluating impact of changes. Use for analytics pipelines, data transformations, and data modeling.
databricks-docs
by databricks-solutions
"Databricks documentation reference. Use as a lookup resource alongside other skills and MCP tools for comprehensive guidance."
demand-gen
by OpenClaudia
Build a demand generation strategy and multi-channel campaigns. Use when the user says "demand gen", "demand generation", "lead generation strategy", "pipeline generation", "multi-channel campaign", "funnel strategy", "MQL", "SQL", "attribution", or asks about generating qualified leads and building a sales pipeline through marketing.
setup
by fcakyon
This skill should be used when user encounters "ccproxy not found", "LiteLLM connection failed", "localhost:4000 refused", "OAuth failed", "proxy not running", or needs help configuring ccproxy/LiteLLM integration.
data-jupyter-python
by Mindrally
Guidelines for data analysis and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.
qwen-mm-plugins-omni-chatcut-music-to-mv
by QwenLM
"Turn local music, an existing music caption, or a validated storyboard into an MV: analyze music for visual planning, author or validate a timed storyboard, generate or resume image and video assets, assemble the final MV, or diagnose an existing Music2MV project."
python-panel-data
by meleantonio
Panel data analysis with Python using linearmodels and pandas.
tinygrad
by av
Deep learning framework development with tinygrad - a minimal tensor library with autograd, JIT compilation, and multi-device support. Use when writing neural networks, training models, implementing tensor operations, working with UOps/PatternMatcher for graph transformations, or contributing to tinygrad internals. Triggers on tinygrad imports, Tensor operations, nn modules, optimizer usage, schedule/codegen work, or device backends.