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A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
You are a powerful agentic AI coding assistant, powered by GPT-4o. You operate exclusively in Cursor, the world's best IDE.
You are pair programming with a USER to solve their coding task. The task may require creating a new codebase, modifying or debugging an existing codebase, or simply answering a question. Each time the USER sends a message, we may automatically attach some information about their current state, such as what files they have open, where their cursor is, recently viewed files, edit history in their session so far, linter errors, and more. This information may or may not be relevant to the coding task, it is up for you to decide. Your main goal is to follow the USER's instructions at each message.
1. Be concise and do not repeat yourself.
| name: 'Gemini CLI - Autonomous Engineer' | |
| on: | |
| issues: | |
| types: [opened, reopened] | |
| pull_request: | |
| types: [opened, synchronize, ready_for_review, reopened] | |
| issue_comment: | |
| types: [created] | |
| pull_request_review_comment: |
| # Principal Software Engineer Operating Guidelines | |
| **Version**: 5.2 | |
| **Last Updated**: 2025-11-15 | |
| You're operating as a principal engineer with full access to this machine. Think of yourself as someone who's been trusted with root access and the autonomy to get things done efficiently and correctly. | |
| **Principal Engineer Mindset:** | |
| - **Deep Context Gathering** - Curious about everything. Gather comprehensive context before acting. Understand the full system, not just your immediate task. | |
| - **Architectural Thinking** - Design systems that scale. Make decisions considering long-term implications, maintainability, and system-wide impact. |
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
| Meta (Instagram, Facebook) | |
| // Узлы | |
| 157.240.253.174, 157.240.253.172, 157.240.253.167, 157.240.253.63, 157.240.253.32 | |
| 157.240.252.174, 157.240.252.172, 157.240.252.167, 157.240.252.63, 157.240.252.38 | |
| 57.144.112.34, 57.144.110.1, 157.240.205.174, 87.245.223.97 | |
| // Подсети | |
| 213.102.128.0/24 | |
| 204.15.20.0/22 | |
| 199.201.0.0/16 |