orchestrator-worker

Coordinates multi-step coding work through plan issues, delegated workers, and shared knowledge stores.

Updated Aug 25, 2017
One-click install
npx skills add https://github.com/loki495/dotfiles --skill orchestrator-worker-loki495
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: orchestrator-worker
Source: https://github.com/loki495/dotfiles/tree/main/ai/skills/orchestrator-worker
Command: npx skills add https://github.com/loki495/dotfiles --skill orchestrator-worker-loki495

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Multi-step coding work loses context across sessions, tools, and agents: plans live in chat history, parallel agents collide on the same files, and research findings get re-derived from scratch. This Skill defines a default protocol where plans, tasks, claims, research, and lessons all live in Dibs (an MCP-backed task tracker), so any session or tool can resume work cold. ## Core Features & Use Cases - Plan and task tracking in Dibs: Plans are plan-labeled issues with child task issues, claimed via process-verified todo_claim leases so two agents cannot edit the same task concurrently. - Orchestrator/worker delegation with model tiering: Bounded tasks are delegated to the cheapest capable model across Claude Code, opencode, Codex, or agy, with automatic escalation rules and worker launch reporting. - Shared research and lessons stores: research-labeled issues are checksum-versioned against the files they cover, and lesson-labeled issues capture durable non-code gotchas, checked before and updated after any work. - Use Case: You ask for a multi-phase refactor. The orchestrator scaffolds a Dibs plan with child tasks, spawns cheap-model workers for bounded pieces, heartbeats their claims, reviews results against acceptance criteria, and a fresh session next week resumes the plan from Dibs alone. ## Quick Start Ask the agent to plan and execute a multi-step coding task using the orchestrator-worker protocol with a Dibs plan issue.

Frequently Asked Questions about orchestrator-worker

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

FAQPage Schema
How do I delegate coding tasks to cheaper AI models?▼

Use the orchestrator/worker pattern: an orchestrator on a capable model decomposes the plan into bounded tasks, then launches workers on the cheapest capable model per task. Each worker gets a prompt template with the plan issue, its task issue, and acceptance criteria.

How do I resume an AI coding plan in a new session?▼

Store the plan as a labeled issue in an external task tracker rather than chat history. A fresh session calls todo_list filtered by the plan label, reads the plan body and child task states, and resumes from the authoritative record without any local plan files.

How do I prevent two AI agents from editing the same files?▼

Use process-verified task claims: a worker claims its task before starting, and the claim is bound to its OS process (host, pid, start time). Other sessions check claim status first and skip tasks with a live claim instead of editing their files.

Can Claude Code, Codex, and opencode share the same task tracker?▼

Yes, when the tracker is exposed as an MCP server registered in each tool's config, with a CLI fallback for tools that cannot reach MCP directly. Each tool can play orchestrator or worker against the same plan and task issues.

When should I not use an orchestrator-worker protocol?▼

Skip it for single-turn, single-file, quickly-resolved work where scaffolding a plan is pure overhead. Even for planned work, full delegation with workers is only warranted when tasks are independent, context-heavy, or worth the coordination cost.

How do I stop AI agents from repeating the same codebase research?▼

Cache findings as research-labeled issues with a covers list of file paths and content hashes. Before new research, re-hash the listed files: matching hashes mean the cached findings are reusable, and only changed files need narrow re-verification.