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PyAutoArray: Instructions file for Codex

AGENTS.md

PyAutoArray AGENTS.md is an instructions file for Codex, OpenCode from PyAutoLabs/PyAutoArray. It costs 1,896 tokens per session, scanned A, original, MIT.

A repository guide for PyAutoArray, a Python package that provides data structures and numerical utilities for scientific imaging and related projects.

In plain words
What is it for?
Use it when modifying PyAutoArray code, especially arrays, grids, masks, imaging or interferometer data, inversions, convolution, documentation, or JAX-related paths.
Why use it?
It gives coding agents the project’s architecture, dependency boundaries, related repositories, and common commands so they can make changes without breaking how the packages fit together.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions CLAUDE.md.

This is PyAutoLabs/PyAutoArray's own configuration. It tells Codex and OpenCode how to work on PyAutoArray itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything PyAutoArray configures →

not rated 11repo changed 1mo ago A scan 1,896 tokens original MIT
View source ↗ PyAutoLabs/PyAutoArray
Reuse

Borrowing it

Nothing to install: this file belongs to PyAutoLabs/PyAutoArray. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/PyAutoLabs/PyAutoArray/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/PyAutoLabs/PyAutoArray

Made for: Codex, OpenCode.

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Per session 1,896 This file is loaded in full into every session.
When invoked 1,896 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.01896 $0.01896
Opus 5.5 $0.00758 $0.00758
Sonnet 5.5 $0.00379 $0.00379
Haiku 4.5 $0.00190 $0.00190

Measured 1mo ago against content hash a80594b37567, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

PyAutoArray AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

AGENTS.md · 164 lines

How it starts

The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PyAutoArray — Agent Instructions

Canonical, agent-agnostic instructions for this repo. CLAUDE.md imports this file; any tool that does not process @-imports should read this directly.

What this repo is

PyAutoArray (package autoarray) is the low-level data-structure and numerical-utility layer: masks, arrays, (y,x) grids, imaging/interferometer datasets, inversions/pixelizations, convolution/over-sampling operators, and the grid decorators used throughout PyAutoGalaxy and PyAutoLens.

Dependency direction: autoarray depends on autonerves only. It does not import autofit, autogalaxy, or autolens — never add such an import. Shared utilities (e.g. test_mode, jax_wrapper) belong in autonerves.

  • Source siblings: PyAutoNerves (upstream). PyAutoGalaxy / PyAutoLens build directly on autoarray.
  • No _workspace, _workspace_test, or HowTo of its own. The JAX/xp path is exercised by the parity scripts in autogalaxy_workspace_test and autolens_workspace_test.
  • docs/ — Sphinx source; published to ReadTheDocs.

Architecture

  • autoarray/structures/ — Array2D, Grid2D, Grid2DIrregular, VectorYX2D, and the grid decorators.
  • autoarray/dataset/ — Imaging / Interferometer dataset containers.
  • autoarray/inversion/ — pixelizations and linear inversion machinery.
  • autoarray/operators/ — Convolver, over-sampling, and related operators.
  • test_autoarray/ — test suite.

Quick commands

pip install -e ".[dev]"                                  # install with dev/test extras
python -m pytest test_autoarray/                         # full test suite
python -m pytest test_autoarray/structures/test_arrays.py   # one focused test (add -s for output)
black autoarray/                                         # formatter (advisory — not gated)

In a sandboxed / restricted environment, point numba and matplotlib at writable caches:

NUMBA_CACHE_DIR=/tmp/numba_cache MPLCONFIGDIR=/tmp/matplotlib python -m pytest test_autoarray/

Read the full file on GitHub · 164 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · +9 lines · +79 tokens per session a80594b37567
  2. 1mo ago Changed · +14 lines · +183 tokens per session 706d213c6dce
  3. 2mo ago Changed · +12 lines · +182 tokens per session e98f8f5103ac
  4. 2mo ago First seen · 129 lines · 1,452 tokens per session scan A 5312c6b4fd8d

Subscribe to this mod's changes

PyAutoArray AGENTS.md is an instructions file published in the GitHub repository PyAutoLabs/PyAutoArray (11 stars, last pushed yesterday), licensed MIT. It adds 1,896 tokens to every session, about $0.0076 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

PlanningPythonastronomynumpyscientific-computing
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