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: Autonomy through discipline. Trust through verification.
This framework is not just a collection 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 the agent takes is deliberate, verifiable, and aligned with senior engineering best practices.
I also have Claude Code prompting for your reference: https://gist.github.com/aashari/1c38e8c7766b5ba81c3a0d4d124a2f58
This framework is built on five foundational principles that the AI agent is expected to embody:
- Research-First, Always: The agent must never act on assumption. Every action is preceded by a thorough investigation of the current system state.
- 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 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.
The framework consists of three main parts: the Doctrine, the Playbooks, and optional Directives.
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, research protocols, safety guardrails, and professional standards.
Installation is the first and most critical step. You must install the core.md
content as the agent's primary system instruction set.
- For Global Use (Recommended): Install
core.md
as 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 the content in a project-specific rule file (e.g., a
.cursor/rules/
directory or a root-levelAGENT.md
). This will override the global setting.
Note: Treat the Doctrine like 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 follows the same rigorous, disciplined workflow. The agent uses the following status markers in its reports:
✅
: Objective completed successfully.⚠️
: A recoverable issue was encountered and fixed autonomously.🚧
: Blocked; awaiting input or a resource.
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 a session to capture learnings and improve the core.md . |
These are smaller, single-purpose rule files that can be appended to a playbook prompt to modify the agent's behavior for a specific task.
Directive | Purpose |
---|---|
05-concise.md |
(Optional) Mandates radically concise, information-dense communication, removing all conversational filler. |
To use an optional directive, simply append its full content to the bottom of a playbook prompt before pasting it into the chat.
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.
- (Optional) If you need a specific behavior, like conciseness, append the content of
05-concise.md
to the end of the prompt. - Paste the entire combined text into the chat.
- Copy the full text of the appropriate playbook (e.g.,
-
Observe Disciplined Execution:
- The agent will announce its operational phase (Reconnaissance, Planning, etc.).
- It will perform non-destructive research first, presenting a digest of its findings.
- It will execute its plan, providing verifiable evidence for its actions and running tests autonomously.
- It will conclude with a mandatory self-audit to prove its work is correct.
-
Review the Final Report:
- The agent will provide a final summary with status markers. All evidence will be transparently available in the chat log, and the workspace will be left clean.
-
Close the Loop with a Retro:
- Once satisfied, paste the contents of
retro.md
into the chat. - The agent will analyze the session and, if a durable lesson was learned, it will propose an update to its own Doctrine.
- Once satisfied, 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.
- Be Specific: In your initial request, clearly state what you want and why it's important.
- Trust the Process: The framework is designed for autonomy. Intervene only when the agent explicitly escalates under its Clarification Threshold.
- End with a Retro: Regularly using
retro.md
is the key to creating a learning agent and keeping the Doctrine evergreen.
Welcome to a more disciplined, reliable, and truly autonomous way of working with AI.
would this work with vscode as well?