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PyAutoConf — Agent Instructions

PyAutoConf is the configuration management library for the PyAuto ecosystem. It handles object serialization/deserialization, JSON-based priors, and configuration generation.

Setup

pip install -e ".[dev]"

Running Tests

python -m pytest test_autoconf/

Sandboxed / Codex runs

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

Key Architecture

  • autoconf/conf.py — Core configuration system
  • autoconf/dictable.py — Dictionary/serialization support for YAML
  • autoconf/fitsable.py — FITS file handling
  • autoconf/json_prior/ — JSON-based configuration priors
  • autoconf/tools/ — Utility decorators and helpers
  • autoconf/mock/ — Mock objects for testing

Key Rules

  • All files must use Unix line endings (LF)
  • If changing public API, note it in your PR description — all downstream PyAuto packages depend on this

Working on Issues

  1. Read the issue description and any linked plan.
  2. Identify affected files and write your changes.
  3. Run the full test suite: python -m pytest test_autoconf/
  4. Ensure all tests pass before opening a PR.
  5. If changing public API, clearly document what changed — PyAutoFit, PyAutoArray, PyAutoGalaxy, and PyAutoLens all depend on this package.

Never rewrite history

NEVER perform these operations on any repo with a remote:

  • git init in a directory already tracked by git
  • rm -rf .git && git init
  • Commit with subject "Initial commit", "Fresh start", "Start fresh", "Reset for AI workflow", or any equivalent message on a branch with a remote
  • git push --force to main (or any branch tracked as origin/HEAD)
  • git filter-repo / git filter-branch on shared branches
  • git rebase -i rewriting commits already pushed to a shared branch

If the working tree needs a clean state, the only correct sequence is:

git fetch origin
git reset --hard origin/main
git clean -fd

This applies equally to humans, local Claude Code, cloud Claude agents, Codex, and any other agent. The "Initial commit — fresh start for AI workflow" pattern that appeared independently on origin and local for three workspace repos is exactly what this rule prevents — it costs ~40 commits of redundant local work every time it happens.