pipeline▌

Automated code validation pipeline for safe code changes.

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

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

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

Create comprehensive GitLab CI/CD pipelines that automate building, testing, and deployment using GitLab Runner infrastructure and container execution.

Builds an automated malware submission and analysis pipeline that collects suspicious files from endpoints and email gateways, submits them to sandbox environments and multi-engine scanners, and generates verdicts with IOCs for SIEM integration. Use when SOC teams need to scale malware analysis beyond manual sandbox submissions for high-volume alert triage.

Build an automated pipeline to defang indicators of compromise (URLs, IPs, domains, emails) for safe sharing and distribute them in STIX format through TAXII feeds and threat intelligence platforms.

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

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