Claude Code Status Line - Complete Guide: all fields, config, ready-to-use scripts

A persistent, customizable bar at the bottom of Claude Code that shows real-time session data.

Replace 0F B6 51 0C 83 F2 01 with C6 41 0C 01 31 D2 90

py -c "open('C:\\Program Files\\Sublime Text\\sublime_text.exe', 'rb+').write(open('C:\\Program Files\\Sublime Text\\sublime_text.exe', 'rb').read().replace(b'\x0F\xB6\x51\x0C\x83\xF2\x01', b'\xC6\x41\x0C\x01\x31\xD2\x90'))"

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.

Tested with:

• Ubuntu Desktop 22.04.2
• Ubuntu Server 22.04.2

Both are VirtualBox VMs on my Void Linux host. Both have been installed using the following options:

• Normal installation (not minimal), to ensure it's fully bloated
• With third party drivers

After the install, sudo apt update and sudo apt full-upgrade have been applied. A reboot was done next.

Add the following section to ~/.codex/AGENTS.md and save the companion luna_worker.toml as ~/.codex/agents/luna_worker.toml.

• Primary agents: proactively delegate bounded, self-contained jobs to luna_worker when Luna can complete and verify them independently and delegation saves work or supervision. Suitable jobs include authorized releases through established procedures, focused web research, long-running scripts, batch processing, and straightforward implementation. Handle small operations directly by default, unless there are long waits, useful independent parallel work, or an explicit user request to delegate.
• Delegate preparation, execution, and verification as one complete job. Build a concise brief from available context and file references; avoid extra parent tool calls solely to prepare the delegation when that information is sufficient. Leave input inspection, artifact preparation, and routine validation to Luna, except where the parent n

53 things from ~4 months of running an agent harness in production.

iii, the engine that powers the harness, is really cool too: https://iii.dev/docs/install

• crash mid tool call left a dangling call with no result that poisoned every later request workers#507 workers#630
• resumed turn raced the worker booting and called a function that wasn't registered yet workers#507
• no durable queue for turns so wedged ones sat in running forever workers#464
• cut stream parsed as null args and got reported as a successful empty turn workers#878
• seven providers treating any connection close as done workers#878
• cached tokens double billed on six providers workers#740