184/300 resolved (61.33%), using GPT-5.6 Luna with high reasoning, Notebook execution, and Remote context management. Estimated token cost: $0.0362 per rollout, $10.87 total.
| Harness | Resolved | Estimated cost per rollout |
|---|
This is living internal context, not a feature specification or user contract. It records why pi-codex exists and how to judge future changes. Source, settings, README, and changelog own current behaviour. Update this file when the intent changes.
GPT coding models are trained around Codex prompts, tools, and transport. Pi is more extensible, but a mismatched harness can make GPT less effective while consuming more subscription quota.
pi-codex removes the choice between an efficient model-native environment and Pi's extension model, sessions, project context, skills, and UI. It gives GPT the Codex-shaped environment it knows without carrying Codex's fixed client or context overhead.
A slightly more personalised version of this skill helped me migrate three machines from Omarchy 3 to Omarchy 4 (Quattro), so I thought the generic version might help someone else.
This is an agent skill, not a migration script. It gives an AI coding agent a cautious workflow and practical heuristics for preserving personal customisations across Quattro's major changes. It deliberately does not choose your terminal, editor, coding agents, package manager, or preferred desktop
This is a command-backed custom tool definition for the
@howaboua/pi-codex-conversion
extension. It is intended to feel close to the native Codex web__run tool:
operations use ref_id, numbered element ids, lineno, find, click,
and batched operation arrays.
It drives a logged-in Chromium browser through the Chrome DevTools Protocol, with optional routing to another workstation over SSH. The implementation is
Three repository-exploration tasks, run sequentially with normal Pi skills, extensions, prompt templates, and project instructions enabled. Each run used an ephemeral session and the existing explorer system prompt.
The first comparison covered tasks 1 and 2:
Data snapshot: 11 July 2026
Primary source: Artificial Analysis GPT‑5.6 launch analysis and its live LLM leaderboard
Scope: all 15 combinations of Sol, Terra, and Luna at low, medium, high, xhigh, and max reasoning effort.
Short answer: Default to Luna for volume work and Sol when quality matters. Treat Terra as a dominated middle tier, not a default. Raise reasoning effort before moving to a larger model when the next effort remains on the better price/intelligence frontier. Use Sol
maxonly where the last few quality points can change the outcome.
| What Responses Lite actually is | |
| It is not a smaller response format. It is a different request contract for the same Responses endpoint, selected by: | |
| - HTTP/SSE header: x-openai-internal-codex-responses-lite: true | |
| - WebSocket requests: equivalent flag inside client_metadata | |
| Official Codex enables it for all three GPT‑5.6 models: Luna, Terra, and Sol. | |
| Main differences |