Opensource Pipeline

Analyzes DevOps Center test failures and Code Analyzer violations in plain language — failure category, offending file/class/method/line, rule violated, fix direction, and prioritized improvement suggestions (test-code vs production-code) — then optionally creates a tracked fix WorkItem on explicit request. Analysis is pure reasoning; work-item creation is a confirmation-gated write. Use this skill to explain failures or improvement suggestions, translate Code Analyzer violations, or track a fix as a work item. TRIGGER when: a run failed and the user wants root cause; a quality gate failure needs explaining; violations need translating; the user shares a failure payload and asks how to address it; wants to strengthen tests; or wants to create a fix work item, log a remediation, or assign a failure. DO NOT TRIGGER when: the user wants fix code written (use platform-apex-generate) or new test classes authored (use platform-apex-test-generate).

Orchestrate Xcode build optimization by benchmarking first, running the specialist analysis skills, prioritizing findings, requesting explicit approval, delegating approved fixes to xcode-build-fixer, and re-benchmarking after changes. Use when a developer wants an end-to-end build optimization workflow, asks to speed up Xcode builds, wants a full build audit, or needs a recommend-first optimization pass covering compilation, project settings, and packages.

Apply approved Xcode build optimization changes following best practices, then re-benchmark to verify improvement. Use when a developer has an approved optimization plan from xcode-build-orchestrator, wants to apply specific build fixes, needs help implementing build setting changes, script phase guards, source-level compilation fixes, or SPM restructuring that was recommended by an analysis skill.

Deploy applications and infrastructure to Cloudflare using Workers, Pages, and related platform services. Use when the user asks to deploy, host, publish, or set up a project on Cloudflare.

Use this when scaffolding a new Terraform provider.

Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data versioning, tuning hyperparameters, or configuring MLOps tooling like Kubeflow, Airflow, MLflow, or Prefect.