PyAutoLabs · Jammy2211 · Jul 8, 2026 · Jul 8, 2026 · Jul 8, 2026
diff --git a/AGENTS.md b/AGENTS.md
@@ -36,7 +36,11 @@ deliberate refactor). Two are NEVER overridden: the real-data gate and never-rew
   bundled-dataset masks, exemptions: [`skills/al_prepare_imaging_data.md`](./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`](./skills/al_audit_skill_apis.md).)
+  `dir()`, then re-run. The hook fires only on harnesses with hook support (Claude Code);
+  **on any other harness (Codex, Gemini, OpenCode, Copilot, chat) self-enforce it**: run
+  `python autoassistant/audit_skill_apis.py --code "<snippet>"` (or `--file <script.py>`) on
+  generated PyAuto* code before executing it. (Manual run + bypass:
+  [`skills/al_audit_skill_apis.md`](./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/`.

diff --git a/autoassistant/refresh_api_docs.py b/autoassistant/refresh_api_docs.py
@@ -50,9 +50,9 @@ def main() -> int:
     parser = argparse.ArgumentParser()
     parser.add_argument(
         "--scope",
-        choices=("skills", "wiki", "all"),
+        choices=("skills", "wiki", "scripts", "all"),
         default="all",
-        help="Which maintenance surface to audit.",
+        help="Which maintenance surface to audit (matches audit_skill_apis.py --scope).",
     )
     args = parser.parse_args()
 

diff --git a/skills/al_adaptive_pixelization.md b/skills/al_adaptive_pixelization.md
@@ -60,7 +60,7 @@ image as the adapt source.
   pedagogical adaptive-pixelisation walkthrough.
 - **General reference** — [RTD: overview_3_features](https://pyautolens.readthedocs.io/en/latest/overview/overview_3_features.html):
   pixelisation feature section.
-- **Experienced PyAutoLens user** — [workspace/lens: imaging/features/pixelization/adaptive.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/imaging/features/pixelization/adaptive.py):
+- **Experienced PyAutoLens user** — [workspace/lens: imaging/features/pixelization/adaptive.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/imaging/features/pixelization/adaptive.py):
   the canonical adaptive setup.
 
 See also [`wiki/core/concepts/inversions_and_pixelizations.md`](../wiki/core/concepts/inversions_and_pixelizations.md)

diff --git a/skills/al_aggregator_bulk_analysis.md b/skills/al_aggregator_bulk_analysis.md
@@ -70,7 +70,7 @@ stacks (model image, residuals, source map) and PNG sets per fit.
   lensing physics).
 - **General reference** — [RTD: overview_3_features](https://pyautolens.readthedocs.io/en/latest/overview/overview_3_features.html):
   results / aggregator section.
-- **Experienced PyAutoLens user** — [workspace/lens: guides/results/start_here.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/guides/results/start_here.py):
+- **Experienced PyAutoLens user** — [workspace/lens: guides/results/start_here.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/guides/results/start_here.py):
   the entry point — sub-folders cover aggregator, database, workflow
   outputs.
 

diff --git a/skills/al_build_imaging_model.md b/skills/al_build_imaging_model.md
@@ -162,6 +162,6 @@ Natural next steps:
 - **General reference** — [RTD: Model cookbook](https://pyautolens.readthedocs.io/en/latest/general/model_cookbook.html):
   systematic reference for building lens models with `Model` and `Collection` —
   two-component to multi-galaxy, prior customisation, parameter pairing, MGE.
-- **Experienced PyAutoLens user** — [workspace/lens: imaging/modeling/start_here.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/imaging/modeling/start_here.py):
+- **Experienced PyAutoLens user** — [workspace/lens: imaging/modeling/start_here.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/imaging/modeling/start_here.py):
   the canonical imaging-modeling script — light-profile choice (Sersic vs. MGE),
   non-linear-search configuration, JAX/GPU fitting.
diff --git a/skills/al_build_interferometer_model.md b/skills/al_build_interferometer_model.md
@@ -103,6 +103,6 @@ for a complete example.
 - **General reference** — [RTD: Features overview](https://pyautolens.readthedocs.io/en/latest/overview/overview_3_features.html):
   tour of advanced features including interferometry, where the visibility-plane fit
   fits alongside MGE / pixelization / multi-wavelength capabilities.
-- **Experienced PyAutoLens user** — [workspace/lens: interferometer/start_here.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/interferometer/start_here.py):
+- **Experienced PyAutoLens user** — [workspace/lens: interferometer/start_here.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/interferometer/start_here.py):
   canonical strong-lens fit of radio/mm interferometer data — NUFFT-based forward
   model, scaling to millions of visibilities.
diff --git a/skills/al_chain_searches.md b/skills/al_chain_searches.md
@@ -148,5 +148,5 @@ doesn't match your problem.
 - **General reference** — [RTD: Model cookbook](https://pyautolens.readthedocs.io/en/latest/general/model_cookbook.html):
   systematic reference for inheriting and adjusting priors between models, the
   mechanical core of chaining.
-- **Experienced PyAutoLens user** — [workspace/lens: guides/modeling/slam_start_here.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/guides/modeling/slam_start_here.py):
+- **Experienced PyAutoLens user** — [workspace/lens: guides/modeling/slam_start_here.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/guides/modeling/slam_start_here.py):
   SLaM is the canonical multi-phase chain; this script overviews it stage-by-stage.
diff --git a/skills/al_cluster_csv_api.md b/skills/al_cluster_csv_api.md
@@ -67,7 +67,7 @@ fit.
   lensing is research-grade).
 - **General reference** — [RTD: overview_3_features](https://pyautolens.readthedocs.io/en/latest/overview/overview_3_features.html):
   cluster lensing in the feature tour.
-- **Experienced PyAutoLens user** — [workspace/lens: cluster/csv_api.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/cluster/csv_api.py):
+- **Experienced PyAutoLens user** — [workspace/lens: cluster/csv_api.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/cluster/csv_api.py):
   the canonical CSV schema + round-trip demo.
 
 See also [`wiki/core/api/csv_api.md`](../wiki/core/api/csv_api.md) for the

diff --git a/skills/al_configure_search.md b/skills/al_configure_search.md
@@ -136,6 +136,6 @@ See [`al_load_results`](./al_load_results.md) for the inverse — loading what
 - **General reference** — [RTD: Features overview](https://pyautolens.readthedocs.io/en/latest/overview/overview_3_features.html):
   tour of advanced capabilities; search configuration interacts directly with most
   of them (especially pixelization, MGE, point-source).
-- **Experienced PyAutoLens user** — [workspace/lens: guides/modeling/slam_start_here.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/guides/modeling/slam_start_here.py):
+- **Experienced PyAutoLens user** — [workspace/lens: guides/modeling/slam_start_here.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/guides/modeling/slam_start_here.py):
   the SLaM pipeline pre-configures sensible searches per stage — a reference for
   *which* knobs matter at *which* phase.
diff --git a/skills/al_custom_analysis.md b/skills/al_custom_analysis.md
@@ -91,7 +91,7 @@ external prior on a derived quantity). Source:
 - **Student / new to lensing** — _ (advanced PyAutoFit topic).
 - **General reference** — [RTD: overview_3_features](https://pyautolens.readthedocs.io/en/latest/overview/overview_3_features.html):
   analysis customisation in the feature tour.
-- **Experienced PyAutoLens user** — [workspace/lens: guides/advanced/custom_analysis.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/guides/advanced/custom_analysis.py):
+- **Experienced PyAutoLens user** — [workspace/lens: guides/advanced/custom_analysis.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/guides/advanced/custom_analysis.py):
   the canonical custom-analysis walkthrough.
 
 See also [`wiki/core/api/analysis_objects.md`](../wiki/core/api/analysis_objects.md)

diff --git a/skills/al_custom_profile.md b/skills/al_custom_profile.md
@@ -201,6 +201,6 @@ extends (`ag.lp.Sersic` with the same parameters). For mass profiles do the same
 - **General reference** — [RTD: Model cookbook](https://pyautolens.readthedocs.io/en/latest/general/model_cookbook.html):
   systematic model-composition reference; covers using non-standard profiles
   inside `Model` and `Collection`.
-- **Experienced PyAutoLens user** — [workspace/lens: guides/profiles/light.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/guides/profiles/light.py):
+- **Experienced PyAutoLens user** — [workspace/lens: guides/profiles/light.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/guides/profiles/light.py):
   catalog of the built-in light profiles in `al.lp.*` — the patterns your subclass
   should match.
diff --git a/skills/al_datacube_modeling.md b/skills/al_datacube_modeling.md
@@ -57,7 +57,7 @@ intensities — useful for line-emission morphology.
 - **Student / new to lensing** — _ (specialist workflow).
 - **General reference** — [RTD: overview_3_features](https://pyautolens.readthedocs.io/en/latest/overview/overview_3_features.html):
   interferometer / datacube feature section.
-- **Experienced PyAutoLens user** — [workspace/lens: interferometer/features/datacube/start_here.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/interferometer/features/datacube/start_here.py):
+- **Experienced PyAutoLens user** — [workspace/lens: interferometer/features/datacube/start_here.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/interferometer/features/datacube/start_here.py):
   the canonical datacube walkthrough.
 
 See also [`wiki/core/api/datacube.md`](../wiki/core/api/datacube.md) for

diff --git a/skills/al_debug_fit_failure.md b/skills/al_debug_fit_failure.md
@@ -125,6 +125,6 @@ inputs — [`al_run_search`](./al_run_search.md) — and re-loading the new resu
 - **General reference** — [RTD: Demagnified solutions](https://pyautolens.readthedocs.io/en/latest/general/demagnified_solutions.html):
   how unphysical demagnified-source reconstructions arise in pixelised fits, and how
   `PositionsLH` penalties prevent them.
-- **Experienced PyAutoLens user** — [workspace/lens: guides/tracer.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/guides/tracer.py):
+- **Experienced PyAutoLens user** — [workspace/lens: guides/tracer.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/guides/tracer.py):
   inspecting an inferred model via `Tracer` — the patterns this skill uses to
   pull apart a failed fit.
diff --git a/skills/al_group_lensing.md b/skills/al_group_lensing.md
@@ -61,7 +61,7 @@ the scaling relation. Composed in one model.
   lensing is an advanced topic).
 - **General reference** — [RTD: overview_3_features](https://pyautolens.readthedocs.io/en/latest/overview/overview_3_features.html):
   group lensing in the feature tour.
-- **Experienced PyAutoLens user** — [workspace/lens: group/start_here.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/group/start_here.py):
+- **Experienced PyAutoLens user** — [workspace/lens: group/start_here.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/group/start_here.py):
   the canonical group fit — extra galaxies, scaling relations, group
   SLaM.
 

diff --git a/skills/al_hierarchical_inference.md b/skills/al_hierarchical_inference.md
@@ -55,7 +55,7 @@ Workspace path: `autolens_workspace:scripts/guides/modeling/advanced/hierarchica
 - **Student / new to lensing** — _ (advanced PyAutoFit topic).
 - **General reference** — [RTD: overview_3_features](https://pyautolens.readthedocs.io/en/latest/overview/overview_3_features.html):
   graphical models in the feature tour.
-- **Experienced PyAutoLens user** — [workspace/lens: guides/modeling/advanced/hierarchical.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/guides/modeling/advanced/hierarchical.py):
+- **Experienced PyAutoLens user** — [workspace/lens: guides/modeling/advanced/hierarchical.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/guides/modeling/advanced/hierarchical.py):
   canonical hierarchical setup; sibling files cover graphical models and
   expectation propagation.
 

diff --git a/skills/al_inspect_source_reconstruction.md b/skills/al_inspect_source_reconstruction.md
@@ -49,7 +49,7 @@ function-style plot API is documented in
 > `KNNBarycentric` crash inside `FitImaging` (`'NoneType' object has no attribute
 > 'array'`), and `ConstantSplit` is broken on `RectangularUniform`. Use
 > `al.mesh.RectangularUniform` + `al.reg.Constant` for now; tracking:
-> <https://github.com/Jammy2211/PyAutoArray/issues/332>.
+> <https://github.com/PyAutoLabs/PyAutoArray/issues/332>.
 
 ## Branch — source-plane reconstruction
 
@@ -154,6 +154,6 @@ appear.
 - **General reference** — [RTD: Features overview](https://pyautolens.readthedocs.io/en/latest/overview/overview_3_features.html):
   the pixelization feature sits here alongside MGE / interferometry / shapelets —
   links into deeper docs.
-- **Experienced PyAutoLens user** — [workspace/lens: imaging/features/pixelization/modeling.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/imaging/features/pixelization/modeling.py):
+- **Experienced PyAutoLens user** — [workspace/lens: imaging/features/pixelization/modeling.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/imaging/features/pixelization/modeling.py):
   rectangular-mesh + constant-regularization pixelization model — production
   pattern for extended-arc sources.
diff --git a/skills/al_load_results.md b/skills/al_load_results.md
@@ -198,6 +198,6 @@ Sub-paths vary for interferometer / multi-wavelength fits.
 - **General reference** — [RTD: Likelihood function](https://pyautolens.readthedocs.io/en/latest/general/likelihood_function.html):
   how PyAutoLens computes likelihoods — useful when interpreting a loaded
   `samples.csv` or `model.results`.
-- **Experienced PyAutoLens user** — [workspace/lens: guides/results/start_here.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/guides/results/start_here.py):
+- **Experienced PyAutoLens user** — [workspace/lens: guides/results/start_here.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/guides/results/start_here.py):
   loading single fits from JSON/CSV and the aggregator pattern for hundreds of fits
   at once.
diff --git a/skills/al_mge_decomposition.md b/skills/al_mge_decomposition.md
@@ -70,7 +70,7 @@ useful for stellar-dynamics-style decompositions.
   HowToLens has its own chapter for).
 - **General reference** — [RTD: overview_3_features](https://pyautolens.readthedocs.io/en/latest/overview/overview_3_features.html):
   MGE in the feature tour.
-- **Experienced PyAutoLens user** — [workspace/lens: imaging/features/multi_gaussian_expansion/modeling.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/imaging/features/multi_gaussian_expansion/modeling.py):
+- **Experienced PyAutoLens user** — [workspace/lens: imaging/features/multi_gaussian_expansion/modeling.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/imaging/features/multi_gaussian_expansion/modeling.py):
   the canonical MGE modeling script; sibling files cover SLaM, fit,
   likelihood.
 

diff --git a/skills/al_multi_dataset.md b/skills/al_multi_dataset.md
@@ -132,7 +132,7 @@ set the wavelength-varying parameter from a shared relation (e.g. a linear
 - **Student / new to lensing** — _ (no direct HowToLens chapter).
 - **General reference** — [RTD: overview_3_features](https://pyautolens.readthedocs.io/en/latest/overview/overview_3_features.html):
   multi-dataset section.
-- **Experienced PyAutoLens user** — [workspace/lens: multi/start_here.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/multi/start_here.py):
+- **Experienced PyAutoLens user** — [workspace/lens: multi/start_here.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/multi/start_here.py):
   the canonical multi-dataset walkthrough; features/ folder has
   per-scenario examples.
 

diff --git a/skills/al_plot_fit_residuals.md b/skills/al_plot_fit_residuals.md
@@ -140,5 +140,5 @@ what each residual pattern usually means physically. Common cases:
 - **General reference** — [RTD: Likelihood function](https://pyautolens.readthedocs.io/en/latest/general/likelihood_function.html):
   how PyAutoLens computes the likelihood the residuals contribute to — useful for
   interpreting structured residuals statistically.
-- **Experienced PyAutoLens user** — [workspace/lens: imaging/results/start_here.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/imaging/results/start_here.py):
+- **Experienced PyAutoLens user** — [workspace/lens: imaging/results/start_here.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/imaging/results/start_here.py):
   production-quality fit inspection — residuals, chi-squared, FITS exports.
diff --git a/skills/al_plot_tracer.md b/skills/al_plot_tracer.md
@@ -128,6 +128,6 @@ explanation.
 - **General reference** — [RTD: Start here](https://pyautolens.readthedocs.io/en/latest/overview/overview_1_start_here.html):
   the `Tracer` object in action — building one, evaluating it on a grid, plotting
   the result.
-- **Experienced PyAutoLens user** — [workspace/lens: guides/tracer.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/guides/tracer.py):
+- **Experienced PyAutoLens user** — [workspace/lens: guides/tracer.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/guides/tracer.py):
   full pattern for inspecting an inferred `Tracer` — ray tracing, profiles, numpy
   arrays, visualization.
diff --git a/skills/al_point_source.md b/skills/al_point_source.md
@@ -69,7 +69,7 @@ H0-inference workflow built on top of this.
   positions-only fitting looks like in isolation.
 - **General reference** — [RTD: overview_3_features](https://pyautolens.readthedocs.io/en/latest/overview/overview_3_features.html):
   short PointDataset / AnalysisPoint section in the feature tour.
-- **Experienced PyAutoLens user** — [workspace/lens: point_source/start_here.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/point_source/start_here.py):
+- **Experienced PyAutoLens user** — [workspace/lens: point_source/start_here.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/point_source/start_here.py):
   full point-source fitting walkthrough — dataset, model, analysis, fit
   inspection.
 

diff --git a/skills/al_prepare_imaging_data.md b/skills/al_prepare_imaging_data.md
@@ -197,5 +197,5 @@ If you're new to *what* this dataset represents physically, read
 - **General reference** — [RTD: New user guide](https://pyautolens.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html):
   decision-tree routing by lens scale and data type — orients a new PyAutoLens user
   before they touch FITS data.
-- **Experienced PyAutoLens user** — [workspace/lens: imaging/data_preparation/start_here.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/imaging/data_preparation/start_here.py):
+- **Experienced PyAutoLens user** — [workspace/lens: imaging/data_preparation/start_here.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/imaging/data_preparation/start_here.py):
   canonical reference for getting telescope data analysis-ready.
diff --git a/skills/al_run_search.md b/skills/al_run_search.md
@@ -132,5 +132,5 @@ tail -f output/imaging/<your_lens>/sie_sersic/*/search.log
   managing run times.
 - **General reference** — [RTD: New user guide](https://pyautolens.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html):
   decision-tree routing for a new user about which fit to run first.
-- **Experienced PyAutoLens user** — [workspace/lens: imaging/start_here.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/imaging/start_here.py):
+- **Experienced PyAutoLens user** — [workspace/lens: imaging/start_here.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/imaging/start_here.py):
   the minimal end-to-end imaging fit on GPU — production pattern this skill mirrors.
diff --git a/skills/al_run_slam_pipeline.md b/skills/al_run_slam_pipeline.md
@@ -131,6 +131,6 @@ all 4+ phases will short-circuit, but you'll catch import / config errors fast.
 - **General reference** — [RTD: Features overview](https://pyautolens.readthedocs.io/en/latest/overview/overview_3_features.html):
   SLaM sits alongside pixelization, MGE, subhalo detection and multi-wavelength on
   the feature tour.
-- **Experienced PyAutoLens user** — [workspace/lens: guides/modeling/slam_start_here.py](https://github.com/Jammy2211/autolens_workspace/blob/main/scripts/guides/modeling/slam_start_here.py):
+- **Experienced PyAutoLens user** — [workspace/lens: guides/modeling/slam_start_here.py](https://github.com/PyAutoLabs/autolens_workspace/blob/main/scripts/guides/modeling/slam_start_here.py):
   the canonical SLaM invocation script — all other SLaM scripts in the workspace are
   documented relative to it.