pipeline▌

This skill enables building document processing pipelines - chain multiple operations (extract, transform, convert) into reusable workflows with data flowing between stages.

Enterprise-grade Jenkins pipelines with declarative and scripted syntax for multi-stage CI/CD automation. \n \n Supports both declarative and scripted pipeline approaches, with multi-branch pipeline and parameterized build capabilities \n Includes environment variables, credentials management, approval gates, and artifact archiving for production-safe deployments \n Covers agent configuration, stage orchestration, test reporting (JUnit), and Docker registry integration \n Best practices emphasiz

Multi-agent DAG execution framework for decomposing large features into independently verifiable work units with quality gates. \n \n Structures complex features as directed acyclic graphs with explicit dependencies, complexity tiers (isolated edits to schema/security changes), and per-unit acceptance criteria \n Enforces a seven-stage pipeline: RFC intake, DAG decomposition, unit assignment, implementation, validation, merge queue processing, and final system verification \n Implements merge qu

Multi-stage CI/CD pipelines with approval gates and deployment orchestration. \n \n Covers four deployment strategies: rolling updates, blue-green, canary, and feature flags, each with trade-offs for downtime, rollback speed, and infrastructure cost \n Includes approval gate patterns for manual review, time-based delays, and multi-approver workflows across GitHub Actions, GitLab CI, and Azure Pipelines \n Provides automated rollback mechanisms triggered by health checks and failure detection, pl

ML pipeline automation orchestrates the entire machine learning workflow from data ingestion through model deployment, ensuring reproducibility, scalability, and reliability.

Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.

Architecture patterns for multi-stage CI/CD pipelines with approval gates and deployment strategies.

Blender Web Pipeline skill provides workflows for exporting 3D models and animations from Blender to web-optimized formats (primarily glTF 2.0). It covers Python scripting for batch processing, optimization techniques for web performance, and integration with web 3D libraries like Three.js and Babylon.js.

Data cleaning pipelines transform raw, messy data into clean, standardized formats suitable for analysis and modeling through systematic handling of missing values, outliers, and data quality issues.

Build automated continuous integration and deployment pipelines that test code, build artifacts, run security checks, and deploy to multiple environments with minimal manual intervention.