Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/pyautolabs/pyautoarray/agents-mdgit clone --depth 1 https://github.com/PyAutoLabs/PyAutoArrayWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.01452 | $0.01452 |
| Opus 5 | $0.00726 | $0.00726 |
| Sonnet 5 | $0.00290 | $0.00290 |
| Haiku 4.5 | $0.00145 | $0.00145 |
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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 129 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.
Related repos
- Source siblings: PyAutoNerves (upstream). PyAutoGalaxy / PyAutoLens build directly on autoarray.
- No
_workspace,_workspace_test, or HowTo of its own. The JAX/xppath 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/Interferometerdataset 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/
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.
- yesterday First seen · 129 lines · 1,452 tokens per session scan A 5312c6b4fd8d
PyAutoArray AGENTS.md is an instructions file published in the GitHub repository PyAutoLabs/PyAutoArray (10 stars, last pushed 2d ago), licensed MIT. It adds 1,452 tokens to every session, about $0.0073 per session on Opus 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.
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