Phase 4b: the eight feature ag_* skills

.claude/skills/ag_basis_profiles.md

@@ -0,0 +1 @@
+../../skills/ag_basis_profiles.md

.claude/skills/ag_build_interferometer_model.md

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+../../skills/ag_build_interferometer_model.md

.claude/skills/ag_chain_searches.md

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+../../skills/ag_chain_searches.md

.claude/skills/ag_ellipse_fitting.md

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+../../skills/ag_ellipse_fitting.md

.claude/skills/ag_light_model_extras.md

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+../../skills/ag_light_model_extras.md

.claude/skills/ag_multi_dataset.md

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+../../skills/ag_multi_dataset.md

.claude/skills/ag_multi_galaxy_and_cluster.md

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+../../skills/ag_multi_galaxy_and_cluster.md

.claude/skills/ag_pixelization.md

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+../../skills/ag_pixelization.md

AGENTS.md

@@ -85,11 +85,16 @@ Map every request onto one or more layers:
    `ag_setup_environment`, `ag_prepare_imaging_data`, `ag_simulate_dataset`,
    `ag_build_imaging_model`, `ag_configure_search`, `ag_run_search`, `ag_plot_fit`,
    `ag_load_results`, `ag_debug_fit_failure` — and is what a galaxy-science request routes to.
-   Three further `ag_*` skills (`ag_audit_skill_apis`, `ag_update_wiki`, `ag_refresh_api_docs`)
-   are maintenance workflows for this repo's own content, not science workflows.
-   `skills/README.md` lists all sixteen live skills and catalogues the rest by phase with the
-   `autogalaxy_workspace` script that grounds each one. Never activate a skill name you
-   have not confirmed is a file on disk.
+   The **feature set** beyond a single smooth profile is live too — `ag_basis_profiles`,
+   `ag_pixelization`, `ag_light_model_extras`, `ag_ellipse_fitting`, `ag_multi_dataset`,
+   `ag_build_interferometer_model`, `ag_multi_galaxy_and_cluster`, `ag_chain_searches` — and each
+   one assumes the core loop's conventions and changes one thing about them, so route to the core
+   loop first and reach for a feature skill when a single Sersic on one CCD image is no longer the
+   right model. Three further `ag_*` skills (`ag_audit_skill_apis`, `ag_update_wiki`,
+   `ag_refresh_api_docs`) are maintenance workflows for this repo's own content, not science
+   workflows. `skills/README.md` lists all twenty-four live skills and catalogues the rest by
+   phase with the `autogalaxy_workspace` script that grounds each one. Never activate a skill
+   name you have not confirmed is a file on disk.
 3. **Wiki** (`wiki/**/*.md`) — *content*: what a Sersic profile is, which searches exist,
    how a pixelised reconstruction is regularised.
 

PENDING.md

@@ -110,25 +110,39 @@ workspace scripts directly.
 
 ## Phase 4b — features beyond a single smooth profile (8 skills)
 
-- [ ] `skills/ag_basis_profiles.md` — grounding: `imaging/features/linear_light_profiles/`,
-      `imaging/features/multi_gaussian_expansion/`, `imaging/features/shapelets/`.
-- [ ] `skills/ag_pixelization.md` — grounding: `imaging/features/pixelization/` (including
-      `galaxy_reconstruction.py` and `likelihood_function.py`).
-- [ ] `skills/ag_light_model_extras.md` — grounding: `imaging/features/extra_galaxies/`,
-      `imaging/features/sky_background/`, `imaging/features/operated_light_profile/`.
-- [ ] `skills/ag_ellipse_fitting.md` — grounding: `ellipse/modeling.py`,
-      `ellipse/multipoles.py`, `ellipse/database.py`. **Route to `modeling.py`: `ellipse/` has
-      no `start_here.py`.**
-- [ ] `skills/ag_multi_dataset.md` — grounding: `multi_dataset/start_here.py`,
-      `multi_dataset/features/`. The idiom deny-list genuinely bites here: datasets combine
-      via the factor graph, never by summing analyses.
-- [ ] `skills/ag_build_interferometer_model.md` — grounding: `interferometer/start_here.py`,
-      `interferometer/modeling.py`, `interferometer/features/`.
-- [ ] `skills/ag_multi_galaxy_and_cluster.md` — grounding: `multi_galaxy/start_here.py`,
-      `cluster/start_here.py`. The subject is member **light**, not lensing — the phase where
-      lensing language most wants to creep back in.
-- [ ] `skills/ag_chain_searches.md` — grounding: `guides/modeling/chaining.py`, HowToGalaxy
-      `chapter_3_search_chaining`.
+**Delivered.** All eight feature skills are on disk with `.claude/skills/` symlinks, rows in
+`wiki/core/external/skill_citation_map.md` and entries in the `skills/README.md` Index:
+`ag_basis_profiles`, `ag_pixelization`, `ag_light_model_extras`, `ag_ellipse_fitting`,
+`ag_multi_dataset`, `ag_build_interferometer_model`, `ag_multi_galaxy_and_cluster`,
+`ag_chain_searches`. Each was written from the grounding scripts named above rather than from
+memory, and every symbol resolves against released `2026.7.29.2`. That brings the repo to
+twenty-four live skills, seventeen of them galaxy-modelling.
+
+Three grounding notes, recorded so nobody re-derives them:
+
+- `ellipse/` really has no `start_here.py`, so `ag_ellipse_fitting` routes to `modeling.py` and
+  says so in its own `## Further reading` block.
+- The lecture series has no chapter on ellipse fitting, interferometry or multi-wavelength
+  fitting. Rather than invent a citation, `ag_ellipse_fitting` omits its student bullet and
+  points at `wiki/core/concepts/ellipse_fitting_and_multipoles.md` instead, while
+  `ag_multi_dataset` and `ag_build_interferometer_model` cite the HowToGalaxy tutorial that
+  teaches the idea each fit leans on hardest (linear profiles; the likelihood) and say plainly
+  that it is not a chapter on their own subject.
+- Grounding `ag_chain_searches` against the wheel exposed a **stale claim** that had spread from
+  the workspace script into two merged wiki pages: `result.model` returns the fitted model with
+  its **original priors unchanged** (`samples_summary.model.mapper_via_defaults_from`, which maps
+  every prior to itself), *not* narrowed `TruncatedGaussianPrior`s. The narrowing lives on
+  `result.model_centred` and its `model_centred_absolute(a=)` / `model_centred_relative(r=)` /
+  `model_centred_max_lh_bounded(b=)` variants. `wiki/core/concepts/non_linear_search.md` and
+  `wiki/core/api/configuration.md` were corrected in this phase and re-stamped.
+
+### Still open
+
+- [ ] Upstream fix for the same claim in
+      `autogalaxy_workspace:scripts/guides/modeling/chaining.py`, whose prose still describes
+      `result.model` as producing narrowed Gaussians. A `contribute-upstream` candidate; until it
+      lands, `skills/ag_chain_searches.md` warns the reader that the script's description is out
+      of date.
 
 ## Phase 5 — `wiki/literature` corpus
 

README.md

@@ -158,8 +158,12 @@ No file in this repository links to something that does not exist yet.
 - **The core modelling loop — nine skills.** Environment setup, imaging data preparation,
   dataset simulation, model building, search configuration, running the fit, plotting the
   fit, loading results, and debugging a failed fit. A galaxy-science request routes into
-  these. Alongside them sit two meta-skills, two project-workflow skills and three
-  repository-maintenance skills — sixteen in total, catalogued in
+  these.
+- **The features beyond a single smooth profile — eight skills.** Basis profiles and MGE,
+  pixelised reconstruction, extra galaxies / sky / operated profiles, ellipse fitting,
+  multi-dataset fits, interferometer modelling, multi-galaxy and cluster fields, and search
+  chaining. Alongside both sets sit two meta-skills, two project-workflow skills and three
+  repository-maintenance skills — twenty-four in total, catalogued in
   [`skills/README.md`](skills/README.md).
 - **The curated reference wiki**, [`wiki/core/`](wiki/core/index.md) — 37 pages across
   `stack/`, `api/`, `concepts/`, `operations/` and `external/`. Every page pins the source
@@ -172,10 +176,6 @@ No file in this repository links to something that does not exist yet.
 
 **Still being written:**
 
-- **Feature skills beyond a single smooth profile** (Phase 4b): basis profiles and MGE,
-  pixelisations, ellipse fitting, multi-dataset and interferometer modelling, multi-galaxy
-  and cluster fields, and search chaining. The underlying PyAutoGalaxy features all exist and
-  the reference wiki covers them; what is pending is the dedicated procedural skill for each.
 - **The literature wiki** (Phase 5) — a galaxy-structure science reference with cross-linked
   concept, entity and source pages. Until it lands, the assistant will not cite it and must
   not invent its contents.

benchmarks/prompts/easy_cosj100020_imaging.md

@@ -19,14 +19,15 @@ running the fit, plotting it and reading the result are all covered by existing
 measures whether it *finds and follows* the built-in workflow rather than writing
 PyAutoGalaxy from memory.
 
-One deliberate exception makes this card worth running even though it is the easy one: the
-**dedicated MGE skill is still pending** (Phase 4b in [`../../PENDING.md`](../../PENDING.md)).
-The feature itself exists in the library and is documented in the reference wiki
-(`concepts/linear_light_profiles_and_mge.md`, `api/light_profile_catalog.md`) and in
-`autogalaxy_workspace:scripts/imaging/features/multi_gaussian_expansion/`. So the card also
-measures whether the agent grounds a feature that has no procedural skill in the wiki and the
-workspace examples — and says that is what it did — instead of reconstructing an MGE from
-training data.
+The MGE half of the prompt is now covered too: `skills/ag_basis_profiles.md` shipped in Phase 4b
+and owns the multi-Gaussian expansion, alongside the reference-wiki pages
+(`concepts/linear_light_profiles_and_mge.md`, `api/light_profile_catalog.md`) and
+`autogalaxy_workspace:scripts/imaging/features/multi_gaussian_expansion/`. The card was written
+when that skill did not exist, so it doubled as a test of grounding a skill-less feature; with
+the skill on disk it measures the sharper thing instead — whether the agent **routes to
+`ag_basis_profiles` and follows it**, rather than reconstructing an MGE from training data. An
+answer grounded only in the wiki and the workspace scripts still scores on row J3; a
+memory-sourced MGE loses those points either way.
 
 ## Prompt
 
@@ -78,7 +79,7 @@ stay identical (a divergence is a bug — fix the README or bump this card's `ve
 |---|-----------|-----|
 | J1 | Real-data gate honoured: dataset plotted and inspected, and **both** questions settled before any fit — contaminants (the faint 2.6" neighbour) and the mask extent, with the chosen radius justified rather than left as a default | 15 |
 | J2 | The sky pedestal is handled as the prompt asks and the agent explains the consequence of not doing so (it inflates the effective radius and Sersic index); the recovered level is sane against the dataset's measured value | 10 |
-| J3 | Sensible model and priors for this galaxy; the MGE is grounded in the reference wiki or a workspace example and the agent says so, rather than recalled from memory | 10 |
+| J3 | Sensible model and priors for this galaxy; the MGE is grounded in `ag_basis_profiles`, the reference wiki or a workspace example and the agent says which, rather than recalled from memory | 10 |
 | J4 | The requested quantities are all reported, and the agent is honest that `info.json`'s `effective_radius_arcsec_rough` is a prior-scale measurement rather than ground truth to be matched | 10 |
 | J5 | The multi-band comparison is scientifically framed — the size trend with wavelength separated from the changing PSF width — and the shipped model PSF is acknowledged as the dominant systematic | 10 |
 | J6 | Conduct: concise assistant-mode communication, no fabricated numbers, API-gate discipline (no invented symbols, functional `aplt` plotting) | 5 |

llms.txt

@@ -20,7 +20,7 @@ Read order: [AGENTS.md](./AGENTS.md) → [skills/README.md](./skills/README.md)
 
 **⚠️ API currency — read before writing any PyAutoGalaxy code.** Older PyAutoGalaxy releases are heavily represented in model training data and their API is **out of date**. Do **not** write PyAutoGalaxy from memory. The single most common stale error: **plotting is functional now.** Use `aplt.subplot_fit_imaging(fit=fit, output_path=..., output_format="png")` and `aplt.subplot_imaging_dataset(dataset=..., output_path=..., output_filename=..., output_format="png")` — note that `output_filename` is accepted by the dataset subplots, `plot_array` and `plot_grid`, but **not** by `subplot_fit_imaging`, which writes a fixed `fit.png` into `output_path` (passing it raises `TypeError`). The old object-oriented plotters — `aplt.FitImagingPlotter`, `aplt.ImagingPlotter`, `aplt.GalaxyPlotter`, `aplt.GalaxiesPlotter`, `aplt.InversionPlotter` — and the `aplt.MatPlot2D` / `aplt.Include2D` / `aplt.Output` configuration objects have all been **removed**; never emit them. The whole plotting surface is module-level functions, and `dir(autogalaxy.plot)` is the authoritative list. If you can't point at a live example or a `dir()` listing for a call, say it's unverified rather than guessing.
 
-**⚠️ This assistant is young.** Sixteen skills exist: the nine-skill core modelling loop (environment, data preparation, simulation, model building, search configuration, running the fit, plotting, results, debugging), two meta, two project-workflow and three maintenance. The Phase-4b **feature** skills (bases and MGE, pixelisations, ellipse fitting, multi-dataset, interferometry, multi-galaxy, search chaining), the literature wiki and the rest of the benchmark suite (one of four cards is written) are still being written, phase by phase. [`skills/README.md`](./skills/README.md) and [`PENDING.md`](./PENDING.md) say exactly what is present and what is planned, and every planned item names the `autogalaxy_workspace` script that grounds it. **Answer from those grounding scripts and say that you did** — never describe a pending skill or wiki page as though you had read it.
+**⚠️ This assistant is young.** Twenty-four skills exist: the nine-skill core modelling loop (environment, data preparation, simulation, model building, search configuration, running the fit, plotting, results, debugging), the eight **feature** skills beyond a single smooth profile (bases and MGE, pixelisations, extra galaxies / sky / operated profiles, ellipse fitting, multi-dataset, interferometry, multi-galaxy and clusters, search chaining), two meta, two project-workflow and three maintenance. The literature wiki and the rest of the benchmark suite (one of four cards is written) are still being written, phase by phase. [`skills/README.md`](./skills/README.md) and [`PENDING.md`](./PENDING.md) say exactly what is present and what is planned, and every planned item names the `autogalaxy_workspace` script that grounds it. **Answer from those grounding scripts and say that you did** — never describe a pending skill or wiki page as though you had read it.
 
 ## Agent instructions (canonical)