How does jules orchestrator prevent the messy pull requests typical of standard AI coding agents?

It prevents 'hallucinated progress' by separating the coding agent from the reviewing agent. By enforcing a mandatory plan-gating step and auditing diffs against a strict rubric, it ensures code isn't merged until it actually meets your spec.

What specific platforms and tools are required to run this orchestrator?

This skill is designed specifically for environments using the Jules MCP server for session management and either the GitHub MCP server or the GitHub CLI (gh) for repository actions. It integrates seamlessly into workflows that support these Model Context Protocol standards.

What exactly is included in the purchase of this skill?

You receive the complete orchestration logic, including the task-scoping templates, the plan-gating workflow, and the automated diff-review rubric. It includes everything needed to set up the senior-reviewer-to-junior-coder loop.

What happens if the implementation agent fails to fix a code review issue after multiple attempts?

The orchestrator includes an iteration cap to prevent infinite loops; if the implementation agent cannot resolve the diff issues within the set limit, the skill will pause and prompt for manual developer handover.

Can this skill handle multiple repository maintenance tasks simultaneously?

Yes, the skill is highly effective for high-volume maintenance, as it can autonomously scope, execute, and merge bug fixes or refactors while ensuring every change is verified against your architectural boundaries.

Google Jules Orchestrator

A full-cycle development orchestrator that gates plans, reviews PR diffs, and iterates until code is merge-ready.

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Google Jules Orchestrator

Example session with this skill installed

Run the Jules development loop to fix the retry logic in the sync service. The backoff counter isn't resetting on success. Follow the iteration cap of 3.

Read your context and instructions
Compiled the google jules orchestrator

I have initialized the orchestration loop for the sync service fix. The task is scoped, and the session is active.

Orchestration State: Task-SYNC-992

Stage	Status	Details
Scoping	Completed	Defined fix for `sync.py::push()` retry loop.
Session	Created	Session ID: `sess_88291-jules`
Plan	Approved	Plan ID: `plan_001` - includes test case for counter reset.
Implementation	In-Progress	Jules is currently modifying `sync.py`.
Review	Pending	Awaiting PR publication.

Plan Summary

Locate push() function in sync.py.
Add self.backoff_counter = 0 inside the successful request block.
Verify with a mock success after three failed attempts.

Next steps

Poll get_session to monitor implementation progress.
Once status is COMPLETED, I will call publish_pr and begin the diff review.
Check references/code-review-rubric.md to ensure the fix doesn't introduce side effects in the sync loop.

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About this skill

Development agents often drift from the original spec or submit messy pull requests that require manual fixing. This skill implements a strict orchestration loop that separates scoping and review from raw code implementation. It prevents implementation models from marking their own homework, ensuring every line of code is verified against a defined spec before merging.

What it does

Task scoping creates a closed spec with explicit acceptance criteria to prevent scope creep.
Session management handles the lifecycle of Jules implementation sessions via MCP tools.
Plan gating enforces a mandatory approval step where the orchestrator reviews implementation plans before execution.
Automated PR publishing triggers the native publish_pr flow once coding tasks are completed.
Loop-based iteration analyzes diffs against a rubric and generates structured fix-it prompts for re-implementation.
Automated merging handles the final GitHub merge flow via CLI or MCP once code passes all checks.

How it works

Scope the task using the provided template to define files, goals, and checkable criteria.
Execute the Jules session by creating the task, approving the plan, and polling for completion.
Audit the resulting pull request diff using a logic-dense code review rubric.
Resolve issues by sending targeted fix-it prompts back to the session or merging the clean PR via GitHub.

Frameworks & tools

This skill requires the Jules MCP server for session control and the GitHub MCP server or gh CLI for repository management. It uses structured Markdown templates for state tracking and scoping.

Why this beats prompting it yourself

Manual prompting often leads to "hallucinated progress" where an agent claims success without verification. This skill forces a rigorous check-and-balance system that treats the implementation agent as a junior dev and the orchestrator as a senior reviewer.

Use cases

Automating the end-to-end fix for reported GitHub issues or bug tickets.
Implementing new features across specific modules while maintaining strict architectural boundaries.
Refactoring legacy codebases where every change must be gated by a plan review.
Managing high-volume repository maintenance tasks that require PR creation and merging.

Known limitations

Requires an authenticated Jules MCP server and GitHub access. Merging logic defaults to squash but follows repo conventions. Handover is mandatory if iteration caps are reached.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Google Jules Orchestrator