pipeline▌

Production-grade ML pipeline infrastructure with experiment tracking, orchestration, feature stores, and automated model lifecycle management. \n \n Covers end-to-end pipeline design: data validation, feature engineering, distributed training orchestration, experiment tracking, and model evaluation gates \n Supports multiple orchestration frameworks (Kubeflow, Airflow, Prefect) and experiment tracking systems (MLflow, Weights & Biases) with code templates and reference guides \n Enforces re

Orchestrate iOS screenshot automation from build through frame composition to App Store Connect upload. \n \n Build and launch apps on simulators with Xcode CLI tools, then drive UI interactions and capture screenshots using AXe plan files \n Frame screenshots deterministically with pinned Koubou 0.14.0, supporting six device types (iPhone Air, iPhone 17 Pro, iPhone 17 Pro Max, iPhone 16e, iPhone 17, Mac) \n Configure pipelines via JSON settings and capture plans; skip framing or upload stages a

ln-1000-pipeline-orchestrator

Comprehensive skill for designing and automating Extract, Transform, Load data pipelines.

ln-100-documents-pipeline

Build RAG pipelines combining web search and LLMs for grounded, sourced AI responses. \n \n Integrates multiple search tools (Tavily, Exa) and LLM providers (Claude, GPT-4, Gemini via OpenRouter) via the inference.sh CLI \n Supports three core patterns: simple search-plus-answer, multi-source research aggregation, and URL content extraction with analysis \n Includes ready-to-use examples for fact-checking, research reports, and iterative deep-dive queries with built-in source attribution \n Best

Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness with exact patch locations. \n \n Analyzes every BigQuery job trigger and external API call to identify cost exposure, loop-driven query multiplication, and missing maximum_bytes_billed limits \n Enforces dry-run and execute modes with explicit prod confirmation, partition filter validation, and scan-size optimization \n Validates idempotent writes using MERGE, staging tables, or dedup logic; flags unsafe a

Multi-step AI content creation pipelines combining image, video, audio, and text generation. \n \n Chains tools like FLUX (image generation), Wan 2.5 (animation), Kokoro TTS (voice), and OmniHuman (talking heads) into complete workflows via the inference.sh CLI \n Includes four ready-to-use pipeline templates: YouTube shorts (script → voiceover → background → animation → merge), talking head videos, product demos, and blog-to-video conversion \n Supports common patterns such as image-to-video-to

Orchestrates video generation across three engines, selecting the best one based on requirements and available resources.

End-to-end MLOps pipeline orchestration from data ingestion through model deployment and monitoring. \n \n Covers five core pipeline stages: data preparation, model training, validation, deployment, and monitoring with DAG orchestration patterns (Airflow, Dagster, Kubeflow) \n Includes data validation, feature engineering, experiment tracking integration, and model versioning strategies across the full ML lifecycle \n Provides deployment automation patterns including canary releases, blue-green