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AI Agent Analysis
DS

amElnagdy/delegate-skills

Delegate a coding task to a separate coding agent CLI, review the diff, land the commit yourself — one per implementer.

stars
1.0k
Language
JavaScript
GitHub
Source and compliance noteLast synced: Aug 16, 2026

Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.

Original GitHub sourceMethodologyEditorial Policy

Overview

delegate-skills is a collection of Claude Code skills that let an orchestrating agent delegate coding tasks to separate coding-agent CLIs (e.g., Codex, Claude Code, Cursor) via named lanes, while keeping the human in the loop for review and commit. It provides setup, direct delegation, and safety controls like read-only modes and resume support.

Installation

npx skills add amElnagdy/delegate-skills

Problem solved

It solves the problem of using multiple AI coding agents efficiently by organizing them into a fleet of lanes, preventing context-switching and tool fragmentation. It also addresses safety and control by ensuring the human reviews and commits all changes, and by providing read-only modes and fail-closed project configs.

What you can build

Developers can build workflows where different coding tasks (features, tests, UI) are automatically routed to the best-suited agent CLI, with configurable models and effort. They can delegate one-off tasks directly, resume sessions, and enforce safety gates. The ceiling includes complex multi-agent pipelines with human oversight, but it's limited to tasks that CLIs can perform in a repo.

Community sentiment

Positive

No community feedback yet.

Concerns

No concerns documented yet.

Bottom line

This is for developers who use multiple AI coding agents and want a structured, safe way to delegate tasks while retaining control. Avoid it if you prefer a single-agent workflow or need fully autonomous execution. The trade-off is added setup complexity for better agent selection and human-in-the-loop safety.

Analyzed by Git-Stars - 8/16/2026