“Data pipelines”

Automated data quality and transformation capabilities for Dataform/dbt/BigQuery pipelines.

CI/CD pipeline patterns with GitHub Actions, GitLab CI, build automation, deployment strategies, and infrastructure as code.

This skill should be used for book-to-SFT pipelines: ePub extraction, literary segmentation, author-voice dataset construction, style-transfer training, LoRA…

Build deterministic multi-step work with Anvia Pipelines — steps, composition, parallel branches, decision routing, batch runs, agent, extractor, and decision…

Build repeatable data-to-Docs pipelines from Sheets and Drive sources.

Public investment workflow pipeline facade for listing templates, dry-runs, and auditable execution.

Audit data quality across pipelines, warehouses, and stores.

A skill with a dangerous bash pipeline where untrusted network data flows into a shell execution sink, triggering rule 005014.

Route and coordinate an end-to-end design-award workflow across winner research, evidence-based evaluation, award matching, entry-text preparation, and final…

Build multi-agent pipelines with structured data flow between agents.

Pipeline de vendas para hotéis: lead → qualificado → proposta → negociação → fechado/perdido

Build DeepStream GStreamer pipelines interactively.

Quicknode blockchain infrastructure for Solana — RPC endpoints, DAS API (Digital Asset Standard) for NFTs and compressed assets, Yellowstone gRPC streaming…

Comprehensive design guide for web, mobile, and desktop applications.

Create professional architecture, workflow, sequence, data-flow, and lifecycle/state diagrams as standalone animated HTML files with SVG graphics, flowing…

Use the DVC repository skill for data versioning, pipelines, experiments, remotes/cache, metrics/params/plots, Python API usage, and DVC codebase maintenance.

Identify poisoned training data and backdoored models across the ML pipeline.

编写、修改、审查 .cnb.yml 流水线配置，诊断构建失败原因并优化性能。

Check GitLab CI pipeline status and investigate failures.

Portable methodology for semantic (row-truth) data quality — write-time quality attributes, trust ladders, cohort fences, golden entity packs with dual error…

Data engineering workflows for designing reliable pipelines and datasets: ingestion, transforms, orchestration, schema evolution, data contracts, quality…

Deep learning framework (PyTorch Lightning / lightning package).

Design and generate CI/CD pipelines from project stack signals across GitHub Actions, GitLab CI, CircleCI, and Buildkite.

Render Cisco Data Fabric ingest-time routing workflows and Splunk Cloud Platform Ingest Processor setup plans with SPL2 pipelines, source types, destinations…