docs: update stale admin_jammy/prompt/ refs to PyAutoPrompt/

autogalaxy/ellipse_no_run.md

@@ -10,7 +10,7 @@
   - `ellipse/database`
 
   They were parked because the ellipse model needs a refactor and JAX support (tracked separately in
-  `admin_jammy/prompt/autogalaxy/ellipse_fitting_jax.md`). In particular, `ellipse/modeling` and
+  `PyAutoPrompt/autogalaxy/ellipse_fitting_jax.md`). In particular, `ellipse/modeling` and
   `ellipse/multipoles` time out under `PYAUTO_TEST_MODE=1` in the mega-run, and
   `ellipse/modeling` additionally raises a `KeyError` on `ellipses.0.centre_0` kwargs after API drift.
 

autolens/pixelization_eager_vs_jit_divergence.md

@@ -4,7 +4,7 @@
 
 Surfaced by the eager-numpy regression assertions added in
 `jax_profiling/imaging/pixelization.py` (PR from
-`admin_jammy/prompt/issued/eager_numpy_regression_assertions.md`).
+`PyAutoPrompt/issued/eager_numpy_regression_assertions.md`).
 
 When the rectangular pixelization script runs, it computes the same
 `log_evidence` via three supposedly-equivalent numpy/JAX paths and gets two

issued/adapt_images_pytree_fix.md

@@ -25,7 +25,7 @@ so the fresh Galaxy doesn't match any key in `adapt_images.galaxy_image_dict`. T
 → `mesh.mesh_weight_map_from(adapt_data=None)` blows up.
 
 The analogous fix on the autolens side that solved a similar dict-keyed-by-instance problem is
-tracked at `@admin_jammy/prompt/autolens/linear_light_profile_intensity_dict_pytree.md`.
+tracked at `@PyAutoPrompt/autolens/linear_light_profile_intensity_dict_pytree.md`.
 
 Note that autolens's jax_likelihood_functions/imaging/rectangular.py currently passes in
 autolens_workspace_test despite apparently having the same Galaxy-identity issue — worth checking

issued/autogalaxy_workspace_test_jax_likelihood_imaging.md

@@ -14,7 +14,7 @@ From @autolens_workspace_test/scripts/jax_likelihood_functions/imaging/:
 - `rectangular_mge.py`
 - `delaunay.py`
 - `delaunay_mge.py` — currently disabled in autolens smoke suite (jax 0.7 regression,
-  see admin_jammy/prompt/build/smoke_workspace_fixes.md). Ship it but disable with the same
+  see PyAutoPrompt/autobuild/smoke_workspace_fixes.md). Ship it but disable with the same
   comment in `smoke_tests.txt`.
 
 **Skip**: `rectangular_dspl.py`, `simulator_dspl.py`.
@@ -38,17 +38,17 @@ with ad-hoc `register_pytree_node` calls inside the workspace script.
 Other known spawn-offs if they surface during porting:
 
 - **Linear light profile** models need `linear_light_profile_intensity_dict_pytree` fixed — see
-  @admin_jammy/prompt/autolens/linear_light_profile_intensity_dict_pytree.md for the lens-side
+  @PyAutoPrompt/autolens/linear_light_profile_intensity_dict_pytree.md for the lens-side
   counterpart. Only blocks scripts that use `ag.lp_linear.*` or MGE bases via
   `fit_for_visualization`, not the scalar `fit_from` round-trip.
 - Any autogalaxy profile that isn't pytree-registered (follow the per-profile pattern in
-  @admin_jammy/prompt/issued/fit_imaging_pytree_*.md).
+  @PyAutoPrompt/autolens/fit_imaging_pytree_*.md).
 
 __Three-step JAX pattern__
 
 Each script mirrors the autolens reference: NumPy baseline → `jax.jit`-wrapped `analysis.fit_from`
 → scalar `log_likelihood` match. The reference file `mge_pytree.py` in autolens is the gold
-standard for this pattern (see @admin_jammy/prompt/issued/fit_imaging_pytree_lp.md for background).
+standard for this pattern (see @PyAutoPrompt/autolens/fit_imaging_pytree_lp.md for background).
 
 __Deliverables__
 

issued/group_20260414.md

@@ -19,7 +19,7 @@ I want you to move the following features from imaging:
 - advanced/sky_background
 - advanced/subhalo (no sensitivity)
 
-For the following features can you put a prompt in admin_jammy/prompt/workspaces with text saying these features
+For the following features can you put a prompt in PyAutoPrompt/workspaces with text saying these features
 ned improving or padding out in imaging,  and then adapting to group when ready:
 
 - scaling relation

z_vault/galaxy_pytree_token.md

@@ -14,7 +14,7 @@ key in the dict (whose keys are still the *original* galaxies attached to `adapt
 which rides as aux/static through the pytree), so the lookup fails.
 
 This is the same class of bug that
-@admin_jammy/prompt/issued/linear_light_profile_intensity_dict_pytree.md fixed for
+@PyAutoPrompt/autolens/linear_light_profile_intensity_dict_pytree.md fixed for
 `LightProfileLinear` via the `pytree_token` pattern: an itertools.count-backed monotonic id
 attached to each instance at `__init__`, exposed as a regular `__dict__` field so it survives
 unflatten, with `__hash__` / `__eq__` overridden to use the token and