ML Ops
Machine learning operations
MLOps Industrialization
by fmind
Guide to transform prototypes into robust, distributable Python packages using the src layout, hybrid paradigm, and strict configuration management.
MLOps Initialization
by fmind
Guide to initialize a new MLOps project with standard tools (uv, git, VS Code) and best practices.
MLOps Observability
by fmind
Guide to implement full stack observability including reproducibility, lineage, monitoring, alerting, and explainability.
MLOps Prototyping
by fmind
Guide to create structured, reproducible Jupyter notebooks for MLOps prototyping, emphasizing configuration management and pipeline integrity.
MLOps Validation
by fmind
Guide to implement rigorous validation layers including static analysis, automated testing, structured logging, and security scanning.
backend-data-model
by parhumm
Generate data model docs with tables, constraints, indexes, retention, and migration notes. Use when designing database schemas from entities.
dart-io
by dartsim
DART model loading - URDF, SDF, MJCF, SKEL parsers and dart::io unified API
dart-io
by dartsim
DART model loading - URDF, SDF, MJCF, SKEL parsers and dart::io unified API
django-patterns
by xu-xiang
Django 架构模式、使用 DRF 的 REST API 设计、ORM 最佳实践、缓存、信号（Signals）、中间件（Middleware）以及生产级 Django 应用。
local-llm-router
by hoodini
Route AI coding queries to local LLMs in air-gapped networks. Integrates Serena MCP for semantic code understanding. Use when working offline, with local models (Ollama, LM Studio, Jan, OpenWebUI), or in secure/closed environments. Triggers on local LLM, Ollama, LM Studio, Jan, air-gapped, offline AI, Serena, local inference, closed network, model routing, defense network, secure coding.
excel-mcp
by sbroenne
Automate Microsoft Excel on Windows via COM interop. Use when creating, reading, or modifying Excel workbooks. Supports Power Query (M code), Data Model (DAX measures), PivotTables, Tables, Ranges, Charts, Slicers, Formatting, VBA macros, connections, and calculation mode control. Triggers: Excel, spreadsheet, workbook, xlsx, Power Query, DAX, PivotTable, VBA.
cellpose-cell-segmentation
by jaechang-hits
"Deep learning cell and nucleus segmentation from fluorescence and brightfield microscopy images. Uses pre-trained models (cyto3, nuclei, tissuenet) and a generalist flow-based algorithm that segments cells without requiring retraining on new image types. Outputs label masks for downstream morphology measurement and tracking. Use scikit-image watershed for rule-based segmentation; use Cellpose when deep learning generalization across staining conditions is needed."
agentic-kaggle-skill
by FrankS-IntelLab
Kaggle-first end-to-end competition workflow for scored submissions. Use when Codex must run Kaggle or competitive ML workflows through scored submission, including code competitions, validation, metrics, policy-safe public notebook/discussion intel, tabular/text/image modeling, tuning, ensembling/stacking, proactive multi-notebook architectures, producer notebooks that train models and export private Kaggle artifact datasets, downstream consumer notebooks, Kaggle GPU offload, kagglehub access, hidden-test debugging, and public score retrieval.
numerai-model-implementation
by numerai
Add a new Numerai model type to the agents training pipeline. Use when you need to register a model in agents/code/modeling/utils/model_factory.py, handle fit/predict quirks in agents/code/modeling/utils/numerai_cv.py, and update configs so the model can run via python -m agents.code.modeling.
numerai-model-upload
by numerai
Create Numerai Tournament model upload pickles (.pkl) with a self-contained predict() function. Use when preparing upload artifacts, debugging numerai_predict import errors, or documenting model-upload requirements and testing steps.
numerai-research
by numerai
"End-to-end Numerai research workflow for trying a new idea: design experiments, implement new model types if needed, run scout→scale experiments, write a full experiment.md report with standard plots, and optionally package/upload a Numerai pickle. Use when a user asks to “try/test a new idea”, “run an experiment”, “sweep configs”, “compare model variants”, or otherwise do new Numerai research."
numerai-experiment-design
by numerai
Design and manage Numerai experiments in this repo for any model idea.
multimodal-llm
by yonatangross
Vision, audio, and multimodal LLM integration patterns. Use when processing images, transcribing audio, generating speech, or building multimodal AI pipelines.
checkpoint-resume
by yonatangross
Rate-limit-resilient pipeline with checkpoint/resume for long multi-phase sessions. Saves progress to .claude/pipeline-state.json after each phase. Use when starting a complex multi-phase task that risks hitting rate limits, when resuming an interrupted session, or when orchestrating work spanning commits, GitHub issues, and large file changes.
llm-integration
by yonatangross
LLM integration patterns for function calling, streaming responses, local inference with Ollama, and fine-tuning customization. Use when implementing tool use, SSE streaming, local model deployment, LoRA/QLoRA fine-tuning, or multi-provider LLM APIs.
concise
by forcedotcom
when creating/modifying md files in skills/rules for AI
model-registry-maintainer
by massgen
Guide for maintaining the MassGen model and backend registry. This skill should be used when adding new models, updating model information (release dates, pricing, context windows), or ensuring the registry stays current with provider releases. Covers both the capabilities registry and the pricing/token manager.
image-generate
by bytedance
Generate images using Seedream models. Invoke when user wants to create images from text prompts or reference images.
aws-sdk-java-v2-bedrock
by giuseppe-trisciuoglio
Provides Amazon Bedrock patterns using AWS SDK for Java 2.x. Use when working with foundation models (listing, invoking), text generation, image generation, embeddings, streaming responses, or integrating generative AI with Spring Boot applications.