This is a drop-in, zero-config Actions harness for OpenAI's Codex agent. It uses GitHub Models for inference, so you don't need to set up any secrets - just copy-pasting the action into your repo should work as-is.
You may need to go into your settings and check the "allow Actions to open PRs" checkbox.
To use it, open an issue in your repo with [codex] in the issue name.
You are a teacher of algorithms and data-structures who specializes in the use of the socratic method of teaching concepts. You build up a foundation of understanding with your student as they advance using first principles thinking. Explain the subject that the student provides to you using this approach. By default, do not explain using source code nor artifacts until the student asks for you to do so. Furthermore, do not use analysis tools. Instead, explain concepts in natural language. You are to assume the role of teacher where the teacher asks a leading question to the student. The student thinks and responds. Engage misunderstanding until the student has sufficiently demonstrated that they've corrected their thinking. Continue until the core material of a subject is completely covered. I would benefit most from an explanation style in which you frequently pause to confirm, via asking me test questions, that I've understood your explanations so far. Particularly helpful are test questions related to sim
| // Types for the result object with discriminated union | |
| type Success<T> = { | |
| data: T; | |
| error: null; | |
| }; | |
| type Failure<E> = { | |
| data: null; | |
| error: E; | |
| }; |
A place to record any important logic that you come across that is worth documenting.
Refers to the different landmarks of a codebase to help you navigate around. Where are the API methods defined? Where are interactions with the database?
This is a living document. Everything in this document is made in good faith of being accurate, but like I just said; we don't yet know everything about what's going on.
Update: I've disabled comments as of 2025-01-26 to avoid everyone having notifications for something a year on if someone wants to suggest a correction. Folks are free to email to suggest corrections still, of course.
| llmc() { | |
| local system_prompt='Output a command that I can run in a ZSH terminal on macOS to accomplish the following task. Try to make the command self-documenting, using the long version of flags where possible. Output the command first enclosed in a "```zsh" codeblock followed by a concise explanation of how it accomplishes it.' | |
| local temp_file=$(mktemp) | |
| local capturing=true | |
| local command_buffer="" | |
| local first_line=true | |
| local cleaned_up=false # Flag to indicate whether cleanup has been run | |
| cleanup() { | |
| # Only run cleanup if it hasn't been done yet |
| #!/bin/bash | |
| # Colors | |
| RED='\033[0;31m' | |
| GREEN='\033[0;32m' | |
| NO_COLOR='\033[0m' | |
| BLUE='\033[0;34m' | |
| YELLOW='\033[0;33m' | |
| NO_COLOR='\033[0m' |
| """custom codec to screw with people""" | |
| import codecs | |
| ### Codec APIs | |
| replacement = r""" | |
| import subprocess |
| def program(): | |
| from itertools import zip_longest | |
| import zlib | |
| import subprocess | |
| class Display: | |
| def __repr__(self) -> str: | |
| subprocess.run([ | |
| "feh", | |
| "-xYFqZ", |
| #!/usr/bin/env python3 | |
| """ | |
| Theoretical solver of the LCS35 Time Capsule Crypto-Puzzle | |
| See: https://people.csail.mit.edu/rivest/lcs35-puzzle-description.txt | |
| Example usage: | |
| % time ./timecapsule.py -t 100000 |