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    Fleet Scale Migration Orchestrator Human Supervised

    by John Barros

    2

    Orchestrate human-supervised code migrations across repository fleets with verifier loops and judge review gates.

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    See it in action

    You say

    Plan a human-supervised migration from LegacyLogger to StandardCloudLogger for our Java 17 fleet. Use Maven for verification and cap retries at 10.

    Your agent does

    Migration Plan: LegacyLogger to StandardCloudLogger Status: PILOT_READY Scope: Java 17 services only Verifier: mvn test Tool Policy: Restricted to local search and git diff. Batch Plan: 1 pilot repo, then batches of 5. Receipt: Included. Ready for review.

    What you get

    Design a multi-stage rollout plan for library upgrades across many services.Create a verifier loop for automated code fixes with strict retry limits.Generate judge review checklists to catch scope creep in agent-generated diffs.Standardize migration receipts for auditable platform maintenance.

    About this skill

    The problem

    Large-scale code migrations often fail due to scope creep, missing test coverage, and lack of human oversight. Manual tracking across hundreds of repositories or monorepo paths is prone to error and creates massive review bottlenecks.

    What it does

    • Standardizes migration directives into auditable, scoped execution plans for repository fleets.
    • Establishes a permission-bound tool map to restrict agent access to specific git commands and local search.
    • Defines a verifier loop with explicit retry budgets and failure conditions using your existing CI or test commands.
    • Generates judge review criteria to audit diffs for logic errors and house-style compliance before human approval.
    • Segments repositories into pilot and batch groups to manage rollout risk.

    Frameworks & tools

    Compatible with any build system including Maven, Gradle, Jest, pytest, Go test, and Bazel. Works with monorepo structures and distributed repository fleets.

    Why this beats prompting it yourself

    Basic prompts lack the rigorous safety gates and multi-role architecture required for production codebases. This skill enforces a structured verification loop and judge-led review process that prevents unsupervised merges and quiet logic failures.

    Use cases

    • Migrating legacy logging frameworks to standard cloud-native observers across dozens of services.
    • Executing fleet-wide API deprecations and breaking change remediations.
    • Enforcing new lint rules or house-style patterns across a monorepo.
    • Managing dependency version upgrades with localized verifier feedback loops.

    Known limitations

    Requires existing test suites or verifier scripts to be effective. Does not handle credential management or production deployment operations.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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