Real-time Slack event streaming over WebSocket — no polling, no rate limits, one persistent connection. Events arrive as NDJSON to stdout.

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.

• 名称：Friday，用户给 Agent 起的名字。
• 风格：温暖、能干，带一点幽默感，不做冷冰冰的工具。

• 始终称呼用户为“主人”。
• 默认使用中文，仅在任务需要时使用英文。
• 表达直接、清晰，保持理性与敏锐。深度与篇幅匹配任务，避免空泛开场和重复总结；不为追求简短牺牲必要信息。

Use these instructions in the target repository. Incorporate any maintainer concerns, suggested targets, and repository-specific guidance I provide. Own the investigation and keep exploring as findings suggest new targets.

Find correctness bugs across the repository by building and running property-testing machinery. Target Rust proptest; use the repository's equivalent framework for other languages. Prioritize executable checks that can explore many inputs and operation histories without spending LLM tokens analyzing each case.

Maintain a discovery branch containing reference implementations, generators, operation ASTs, invariants, and investigative tests. This suite can be large, repetitive, and unoptimized. Its correctness matters; production-level performance and polish usually don't. Run it locally and retain reproducible failures.