This repository contains a disciplined, evidence-first prompting framework designed to elevate an Agentic AI from a simple command executor to an Autonomous Principal Engineer.
The philosophy is simple: Trust, but verify. Autonomy through discipline.
This framework is not just a set of prompts; it is a complete operational system for managing AI agents. It enforces a rigorous workflow of reconnaissance, planning, safe execution, and self-improvement, ensuring every action taken by the AI is deliberate, verifiable, and aligned with senior engineering best practices.
This framework is built on five foundational principles:
- Research-First, Always: The agent must never act on assumption. Every action is preceded by a thorough investigation of the current system state and established patterns.
- Extreme Ownership: The agent's responsibility extends beyond the immediate task. It owns the end-to-end health and consistency of the entire system it touches.
- Autonomous Problem-Solving: The agent is expected to be self-sufficient, exhausting all research and recovery protocols before escalating for human clarification.
- Unyielding Precision & Safety: The operational environment is treated with the utmost respect. Every command is executed safely, and the user's workspace is kept pristine.
- Metacognitive Self-Improvement: The agent is designed to learn. It reflects on its performance and systematically improves its own core directives to prevent repeat failures.
The framework consists of two main parts: the Operational Doctrine (the agent's "brain") and the Operational Playbooks (the structured workflows).
This is the central "constitution" that governs all of the agent's behavior. It's a universal, technology-agnostic set of principles that defines the agent's identity, its research protocols, its safety guardrails, and its professional standards.
Installation is the first and most critical step. You must install the core.md as the agent's primary system instruction set.
- For Global Use (Recommended): Install
core.mdas a global or user-level rule in your AI environment. This ensures all your projects benefit from this disciplined foundation. - For Project-Specific Use: If a project requires a unique doctrine, you can place
core.mdin a project-specific rule location (e.g., a.cursor/rules/directory or a root-levelAGENT.md). The project-level file will override the global setting.
Note: Treat
core.mdlike a piece of infrastructure-as-code. When updating, replace the entire file to prevent configuration drift.
These are structured "mission briefing" templates that you paste into the chat to initiate a task. They ensure every session, whether for building a feature, fixing a bug, or learning from a session, follows the same rigorous, disciplined workflow.
| Playbook | Purpose | When to Use |
|---|---|---|
request.md |
Standard Operating Procedure for Constructive Work | Use this for building new features, refactoring code, or making any planned change. |
refresh.md |
Root Cause Analysis & Remediation Protocol | Use this when a bug is persistent and previous, simpler attempts have failed. |
retro.md |
Metacognitive Self-Improvement Loop | Use this at the end of any significant work session to capture learnings and improve the core.md. |
Your interaction with the agent becomes a simple, repeatable, and highly effective loop.
-
Initiate with a Playbook:
- Copy the full text of the appropriate playbook (e.g.,
request.md). - Replace the single placeholder line at the top with your specific, high-level goal (e.g.,
{Add a GraphQL endpoint to fetch user profiles.}). - Paste the entire template into the chat.
- Copy the full text of the appropriate playbook (e.g.,
-
Observe Disciplined Execution:
- The agent will announce its phase (Reconnaissance, Planning, etc.).
- It will perform non-destructive research first, presenting a digest of its findings.
- It will then present a clear, incremental plan.
- It will execute the plan, providing evidence of its work and running tests autonomously.
- It will conclude with a mandatory self-audit to verify its own work against the live system state.
-
Review the Final Report:
- The agent will provide a final summary with β
/
β οΈ / π§ markers. All evidence and thought processes will be transparently available in the chat log. The workspace will be left clean.
- The agent will provide a final summary with β
/
-
Close the Loop with a Retro:
- Once you are satisfied with the work, paste the contents of
retro.mdinto the chat. - The agent will analyze the session and, if it finds a durable, universal lesson, it will propose an update to its own
core.mdfile.
- Once you are satisfied with the work, paste the contents of
By following this workflow, you are not just giving the agent tasks; you are actively participating in its training and evolution, ensuring it becomes progressively more aligned and effective over time.
Welcome to a more disciplined, reliable, and truly autonomous way of working with AI.