This document provides guidelines for maintaining high-quality Python code. These rules MUST be followed by all AI coding agents and contributors.
All code you write MUST be fully optimized.
"Fully optimized" includes:
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qoomon
/ conventional-commits-cheatsheet.md
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April 24, 2026 00:16
Conventional Commits Cheatsheet
See how a minor change to your commit message style can make a difference.
git commit -m"<type>(<optional scope>): <description>" \ -m"<optional body>" \ -m"<optional footer>"
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.
spilist
/ token-efficiency-analysis-prompt.md
Last active
April 24, 2026 00:07
코딩 에이전트 토큰 효율과 관련된 상태 점검용 프롬프트
다음 레퍼런스를 바탕으로, 내 코딩 에이전트 설정을 토큰 효율 관점에서 점검해줘.
- https://code.claude.com/docs/en/settings.md
- https://code.claude.com/docs/en/env-vars.md
- https://developers.openai.com/codex/config-reference.md
- https://github.com/cnighswonger/claude-code-cache-fix
목표는 성능 저하를 크게 만들지 않으면서 토큰 낭비를 줄이는 거야.
- 직접 절약(자동 주입 문맥, 긴 툴 출력 제한 등)과 간접 절약(검색/IDE/앱 경로 차단 등)을 구분해서 봐줘
- 현재 버전 공식 문서나 현재 설치본에서 확인되지 않은 키는 절대 추천하지 마
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