plan-execute-orchestrator

Orchestrates Codex planning and execution agents across four model quadrants with verified routing.

Updated Jul 3, 2026
One-click install
npx skills add https://github.com/CHENHUI-X/toolbox --skill plan-execute-orchestrator-chenhui-x
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: plan-execute-orchestrator
Source: https://github.com/CHENHUI-X/toolbox/tree/main/codex-skill/codex/plan-execute-orchestrator
Command: npx skills add https://github.com/CHENHUI-X/toolbox --skill plan-execute-orchestrator-chenhui-x

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Coordinating multi-agent coding work often hides which model actually plans or executes a task, leading to silent model substitutions, wasted cost, and unverified results. This Skill enforces a transparent plan-execute pipeline in Codex where every delegation is announced, confirmed, and verified before work begins. ## Core Features & Use Cases - Four-Quadrant Model Routing: Assigns planning to Sol/high (upgradable to Sol/xhigh for complex scopes), complex implementation to Terra/xhigh, small deterministic changes to Luna/medium, and mechanical read-only operations to Luna/low. - Third-Party Provider Registration: Optionally registers Responses API providers as CLI profiles with secure credential storage (macOS Keychain, environment variables, or auth helpers) and validates each profile before routing tasks to it. - Depth-Adaptive Planning: Escalates from initial planning to a single deep-planning pass when evidence shows high-risk signals like security boundaries, data migrations, or concurrency concerns. - Use Case: A user asks to refactor a legacy module across multiple files. The Skill first presents the model routing plan for confirmation, runs a Sol planning agent, detects cross-subsystem impact, adds one Sol/xhigh deep-planning pass, then hands the final plan to a Terra/xhigh executor and reports verified changes. ## Quick Start Ask the agent to plan and implement your coding task using the plan-execute-orchestrator, then confirm the proposed four-quadrant model routing before execution begins.

Frequently Asked Questions about plan-execute-orchestrator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate planning and execution agents in Codex?

Use this Skill to separate planning from execution: a Sol planning agent produces a structured plan with evidence and verification steps, then a Terra or Luna executor implements only that approved plan. The routing is announced and confirmed before any delegation starts.

How do I register a third-party model provider for Codex CLI?

The Skill collects provider details (base URL, model IDs, reasoning effort) and creates a user-level profile file at ~/.codex/<profile-name>.config.toml using the Responses wire API. Credentials are stored in macOS Keychain, environment variables, or an auth helper, and each profile is validated before use.

What happens when the Luna model is not available in native sub-agent delegation?

The Skill falls back to invoking Luna through the official OpenAI CLI with an isolated configuration, preserving the requested model and reasoning effort. It only offers a Terra fallback after both paths fail and the user explicitly approves the change.

When does the planning depth upgrade from high to xhigh?

Planning escalates to Sol/xhigh when evidence shows high-risk signals like security boundaries, data migrations, concurrency, or cross-repository coordination, or two ordinary signals such as multi-subsystem impact or unclear dependency chains. Only one deep-planning pass is added before execution starts.

Can third-party models appear in the Codex desktop model menu?

No. Third-party profiles only work through the Codex CLI via codex --profile and never modify the desktop app or its model selection menu. The Skill explicitly avoids patching the desktop application or replacing the official OpenAI provider.

When should I not use this orchestration workflow?

Avoid it for pure read-only Q&A tasks where no code changes are needed, unless you explicitly want to test the Skill. It also stops and defers to the user when a task requires product decisions that only the user can make.