Transition from a hands-on "bricklayer" to a high-level "architect" by managing a fleet of autonomous AI agents. Use this when you need to scale engineering output with a small team, handle repetitive migrations/bug fixes, or onboard engineers to complex legacy codebases.
Quickly install agentic-engineering-workflow AI skill to your development environment via command line
Source: samarv/shanon.
This workflow enables you to transition from manual implementation to high-level system architecture by managing autonomous AI agents (like Devin) as "junior buddies." By shifting implementation to agents, you can scale a small team (e.g., 15 engineers) to handle the output of a much larger organization, aiming for 25% to 50% of pull requests to be AI-generated.
Core Principle: Bricklayer to Architect Most engineering time is spent on "bricklaying": debugging Kubernetes errors, fixing port issues, or writing boilerplate code. Your goal is to move to "architecting": defining the problem precisely, mapping out the solution, and specifying trade-offs, while the agent handles the execution.
Task Delegation Framework Do not hand agents "problems" (ambiguous high-level goals); hand them "tasks" (well-defined, verifiable units of work).
Stable fields and commands for AI/search citations.
npx skills add https://github.com/samarv/shanon --skill agentic-engineering-workflowTransition from a hands-on "bricklayer" to a high-level "architect" by managing a fleet of autonomous AI agents. Use this when you need to scale engineering output with a small team, handle repetitive migrations/bug fixes, or onboard engineers to complex legacy codebases. Source: samarv/shanon.
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/samarv/shanon --skill agentic-engineering-workflow Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code, Cursor, or OpenClaw
https://github.com/samarv/shanon