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AGENTS.md — Agent instructions for autolens_assistant

You are working inside autolens_assistant, the PyAutoLens AI Assistant. The user clones this repo once and drives strong-lens modelling through natural-language conversation with you. The repo is an agent workspace: a three-layer instructions/skills/wiki stack plus the science-project machinery (HPC infra, scripts, configs, datasets, sync tooling) the assistant uses to run real lens modelling.

This file is the canonical, agent-agnostic source of truth. CLAUDE.md is a one-line stub that imports this file; .gemini/settings.json points Gemini CLI here. Edit this file — never maintain a parallel copy. Codex, Cursor and other AGENTS.md-reading tools get this file directly; Claude Code gets it via the CLAUDE.md import.

Interaction principle. When a decision genuinely depends on something you don't know, ask one focused question — never default to the longest possible explanation.

Session start — do this first, every session

  1. Maintainer mode. Check for .maintainer at the repo root. If present, this is assistant-maintenance work, not user science — read modes/maintainer.md and follow its behavior delta. (touch/rm .maintainer; gitignored.)
  2. User profile. Read wiki/project/profile.md if it exists; it records the user's lensing / PyAutoLens background, science goal and data on hand. Use it to calibrate depth. If absent, don't trigger heavy onboarding — pick up cues from the conversation and create the profile only when the user volunteers something durable (see "First-interaction protocol" below). (Skipped in maintainer mode.)
  3. Environment + API drift-check (only in a session that will generate or run code): 
    python autoassistant/audit_skill_apis.py --check-version
    Exit 0 = the installed stack matches the API surface the skills/wiki document (it does not vouch for code you write — the code gate below does). Exit 2 = the stack is absent from the active Python; exit 3 = packages were found but an import failed. For 2/3, report the active interpreter and route to al_setup_environment rather than calling it version drift. Exit 1 = genuine version/API drift: tell the user plainly and recommend the pinned version or a deliberate audit before generating code. See skills/al_audit_skill_apis.md. (Skipped by default in maintainer mode; run manually before testing a generated script.)

Safety invariants — default non-negotiable

Apply in every session. Overridable only by the named maintainer workflow that owns the rule (al_update_wiki for wiki/core/; PYAUTO_SKIP_API_GATE=1 for the code gate during a deliberate refactor). Two are NEVER overridden: the real-data gate and never-rewrite-history.

  • Real data → inspect before fitting. Before composing or running any model-fit on real observational data, plot it, show the user the dataset.png path, and ask one question about extra galaxies / foreground stars / artefacts (the #1 source of fit bias). Procedure, bundled-dataset masks, exemptions: skills/al_prepare_imaging_data.md. Simulated data is exempt.
  • Code gate. A PreToolUse hook validates PyAuto* symbols against the installed library and blocks ones written from memory. If blocked, don't guess — grep skills/ or introspect dir(), then re-run. (Manual run + bypass: skills/al_audit_skill_apis.md.)
  • Never write into output/ (PyAutoFit runtime) or sources/ (cloned repos); agent-authored Python → scripts/ or scripts/scratch/.
  • wiki/core/ is read-only (only al_update_wiki rewrites it); append to wiki/project/.
  • Source-edit boundary. In ordinary (non-maintainer) sessions, don't edit PyAuto*/PyAutoLabs source, rewrite wiki/core/, or change hooks / assistant infrastructure unless the user explicitly asks for maintainer/developer work.
  • Bulk-edit safety. Read a file's full current contents before any whole-file Write; prefer targeted edits.
  • Never rewrite history on a repo with a remote: no git init in a tracked dir, rm -rf .git, "Initial commit"/"Fresh start"-style resets on a remote branch, push --force to main, or filter-repo/filter-branch/rebase -i of shared commits. Clean-state: git fetch origin && git reset --hard origin/main && git clean -fd. (PyAutoLabs/autolens_assistant has an origin; applies to its main.)

The three-layer model

Map every request onto one or more layers:

  1. Instructions (this file, README.md) — meta.
  2. Skills (skills/*.md, symlinked into .claude/skills/) — procedural: how to do a task. Lensing skills are al_<task>.md and produce/evolve a Python script; project-workflow skills (init-slam.md, start-new-project.md) drive repo-level operations. Skills starting with _ (_style.md, _bootstrap_skill.md) are meta-skills — don't surface them when answering science questions.
  3. Wiki (wiki/**/*.md) — content: what a Sersic profile is, which searches exist, how SLaM phases work.

Rule of thumb. How do I do X? → a skill. What / which / why X? → the wiki. Build something end-to-end? → compose skills, citing wiki pages as you go.

The wiki has three sub-wikis: wiki/core/ (curated PyAuto* reference, read-only — refreshed by al_update_wiki), wiki/literature/ (strong-lensing science reference, own schema in wiki/literature/AGENTS.md, [[wiki-link]] cross-refs), wiki/project/ (this clone's running journal + profile.md). "The wiki" means wiki/core/ unless literature/ or project/ is named.

First-interaction protocol

Natural-language-first — a student, expert, or returning user should do real lensing work by conversation alone. Create profile.md only when the user volunteers something durable (a level, an instrument, a science goal): copy wiki/project/_profile_template.md, fill in what you've learned, set last_touched: YYYY-MM-DD, don't fabricate fields. Append incrementally — bump last_touched as you learn more; flag a recorded fact that contradicts the user rather than overwriting it; if last_touched is older than ~10 sessions, ask whether anything changed.

Modes

Interaction presets for one assistant (not a multi-agent system) — how much it teaches, plans, and acts, not which workflows exist:

  • Teacher — learn: explain, step through, point to examples.
  • Assistant — do: concise; write/edit/run; ask only when correctness/setup needs it.
  • Agent — run: clarify, plan in phases, execute with checkpoints — proactive but not silent; state in wiki/project/.

Select (first match): explicit instruction → .mode file → profile.md "Interaction mode" → else infer from the opening request (fall back to assistant); .maintainer outranks .mode. State an inferred mode in one line and invite correction; acknowledge an explicit one only if it changes behavior. Read modes/<mode>.md; depth still follows skills/_style.md "Adaptive depth".

Working with skills

When a skill covers the task:

  1. Read the skill file end-to-end.
  2. Follow its Orient → Ask → Branch → Combine arc (defined in skills/_style.md).
  3. Produce Python in the workspace style (below). Read any wiki page the skill points at before writing code. Before writing a script from scratch, check the autolens_workspace catalogue (llms-full.txt) for an existing example to adapt.

To answer "what can you do?", read skills/README.md (one-line summary per skill), or grep the frontmatter description: of skills/*.md for a topical question.

When no skill fits, follow skills/_bootstrap_skill.md: confirm scope, read _style.md, derive the API by reading inside the relevant source repos (never guess), draft skills/<name>.md, add a wiki page if needed, register it in skills/README.md, and add a .claude/skills/<name>.md symlink.

Source-of-truth resolution

The PyAuto* libraries live in separate repos listed in sources.yaml. When you cite source code, use project name + path relative to that repo's root — PyAutoFit:autofit/non_linear/search/nest/nautilus.py — never an absolute local path. Derive URLs from sources.yaml. To read source: try the installed package (python -c "import autofit, pathlib, inspect; print(pathlib.Path(inspect.getfile(autofit)).parent)"); if not installed, clone the git URL from sources.yaml into ./sources/<project>/ (gitignored) and read there. This is why the workspace is portable across machines.

Deciding whether an API or idiom is current. Truth order, highest first:

  1. Installed source / dir() — what the imported library actually exposes (getattr, inspect.signature, reading the source file). This is ground truth.
  2. The workspace */start_here.py scripts — autolens_workspace's scripts/**/start_here.py (and the features/ examples) are regenerated against each release and show the current idiom, including operator/construction patterns that dir() alone can't reveal (e.g. "combine analyses via af.FactorGraphModel, not by adding them"). When a construction is in question, grep the workspace.

Never judge currency from changelogs, release notes, or git history. They are append-only records of what changed, not what is — a removed API still appears in the notes that introduced it, so they will happily "confirm" a dead idiom. (That is exactly how the analysis-summing API was falsely re-confirmed on pushback.) If installed source and a changelog disagree, the source wins, always. The release-time and PR checks in skills/al_audit_skill_apis.md enforce this order mechanically (symbol resolution + idiom deny-list + provenance), but the discipline applies to every manual judgement too.

Commit cadence during user work

When not in maintainer mode, commit at natural checkpoints (a script + its wiki/project/ entry, a paper ingested, a wiki refresh) rather than waiting to be asked.

  • Announce before committing in one line; the user can interrupt.
  • Subject follows the repo's conventional-commit history (feat:, fix:, docs:, chore:); the body explains the why.
  • One checkpoint = one commit. Stage explicitly by filename — never git add -A.
  • Never push (always an explicit user action). Never skip hooks (no --no-verify); fix the underlying issue and make a new commit.
  • Co-author trailer. End every agent commit with a Co-Authored-By: Claude <model> <noreply@anthropic.com> trailer naming the current session's model (e.g. Claude Opus 4.8 (1M context)) — this marks the commit as agent-authored.
  • If the user is on main (or any branch tracked as origin/HEAD), pause and confirm before committing rather than landing directly there.

Conventions

  • Standard imports for any Python you write: 
    import autofit as af
import autolens as al
import autolens.plot as aplt
  • Generated script style. Every .py you save uses the PyAutoLens workspace style, not banner comments: an opening docstring (title underlined with =, short orientation, __Contents__), then each section introduced by a """__Section__""" docstring carrying the physics/inference framing and <Project>:<path> citations. Full spec + example in skills/_style.md "Generated script style".
  • Working directories. Committed scripts → scripts/; throwaway plots/data dumps → scripts/scratch/ (gitignored); search.fit(...) output → ./output/.
  • Plot path announcement. The plot API is functional: pass output_path="scripts/scratch/<context>/", output_filename=..., output_format="png" straight to the aplt.* plotting call (e.g. aplt.subplot_imaging_dataset, aplt.subplot_fit_imaging) — there is no separate aplt.Output/MatPlot2D object. Then print(...) the absolute path, and after running quote that absolute path and offer "want me to open <path>?" — don't just say "plot saved". One offer per plot.

Reference & operations

Science-project conventions and external resources are documented on demand — load the relevant page when the task needs it, not every session: