Orchestrate a full-lifecycle software engineering workflow with multi-agent coordination.
npx skills add https://github.com/mccode415/agent --skill staff-engineer
Please help me install this Agent Skill. Skill: staff-engineer Source: https://github.com/mccode415/agent/tree/main/skills/staff-engineer Command: npx skills add https://github.com/mccode415/agent --skill staff-engineer
This Skill automates the entire software development lifecycle, from initial research and planning to implementation, validation, and knowledge capture, ensuring high-quality, robust code.
Use the staff-engineer skill to implement a new user authentication module.
High-intent search queries and answers about installing and using this skill.
A multi-agent system automates the full software development lifecycle by orchestrating specialized agents for deep research, evaluator-optimizer planning, blast-radius analysis, and tiered iteration. This structured, phased approach ensures code quality, architectural integrity, and user approval.
Blast-radius analysis works by evaluating the potential impact of architectural changes before implementation. It acts as a safety and quality gate within the engineering workflow to ensure conditional validation and prevent unintended side effects across the codebase.
Yes, multi-agent coordination handles significant feature development and module refactoring by orchestrating specialized AI agents for research, planning, coding, and validation. This structured workflow manages complex architectural changes and ensures robust code generation.
The best way to ensure code quality when introducing a new architectural pattern is using a structured, phased approach with safety gates. Incorporating user review, blast-radius analysis, conditional validation, and tiered iteration verifies architectural integrity and robust outcomes.
You should not use an automated engineering workflow for simple, isolated code modifications that bypass deep research or blast-radius analysis. The structured, phased approach is designed specifically for complex tasks, significant architectural changes, and feature development requiring tiered iteration.
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dioerdenOrchestrate multi-agent AI workflows using a declarative language.
