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PyAutoMind backlog dashboard

81 filed prompts in the backlog · 335 already dispatched to issues (issued/). Backlog view only — organism health lives with the Heart (/health), not here.

Work-type Prompts
feature 31
bug 27
research 11
maintenance 4
docs 3
test 3
refactor 1
triage 1

feature (31)

Prompt Target Difficulty Autonomy Priority
Claude Development Prompt: Arcsecond Tick Label Decimal Placement autoarray large supervised normal
Can create a list of InversionMatrix objects for each dataset autoarray medium supervised normal
The al.SimulatorInterferometer path that uses al.TransformerNUFFT (nufftax-backed) can't scale to autoarray too-large supervised high
Oversampled PSF: k×s evaluation/convolution coupling + simulator adoption autoarray large supervised normal
Users keep combining RectangularAdaptDensity meshes with ConstantSplit autoarray too-large supervised high
Follow-up to rectangular_adapt_cdf.md (issue #322) and Path A autoarray too-large supervised normal
EP analytic updates — implement the four planned work packages autofit large supervised normal
The project @z_projects/ic50_workspace is our IC50 use case which we autofit medium safe normal
PIEMass.potential_2d_from: implement the missing lensing potential autogalaxy too-large supervised normal
We currently does not have implemented a point source of autogalaxy small safe normal
Surface live visual updates in assistant search guidance autolens_assistant too-large supervised normal
Portable user defaults and environment discovery autolens_assistant large supervised normal
The assistant protects generated code with a PreToolUse hook that autolens_assistant large supervised normal
A science project created by start-new-project is a separate, self-contained autolens_assistant small safe high
wiki/literature/ is the assistant's shared, self-contained base reference for strong-lensing autolens_assistant large supervised high
Now that autolens_assistant generates scripts in the PyAutoLens narrative-docstring autolens_assistant large supervised normal
Adopt oversampled PSFs in the start-here dataset chain (option a) autolens_workspace large supervised normal
Scheduled runs — overnight queue passes with a morning report autonomy medium supervised low
Context: PyAutoLens issue #542 asks for a JIT/vmap-able multi-plane substructure jax_substructure too-large supervised normal
Context: PyAutoLens issue #542, prompt 2 of 4. Prompt 1 jax_substructure too-large supervised normal
Context: PyAutoLens issue #542, prompt 3 of 4. Prompts 1-2 jax_substructure too-large supervised normal
Context: PyAutoLens issue #542, prompt 4 of 4 (stretch goal) jax_substructure too-large supervised normal
Context: PyAutoLens issue #542 follow-up (Gap 1, deferred during the jax_substructure too-large supervised normal
Context: PyAutoLens issue #542 follow-up (Gap 2, deferred during the jax_substructure too-large supervised normal
Profiling agent pyautobrain too-large supervised normal
Rework Heart version_skew for floor semantics (stamps are frozen) pyautoheart small supervised normal
Weak lensing real data example on Abell 2744. Reproduce a weak large supervised high
The imaging features/advanced/los_halos example needs improving and padding out before workspaces medium safe normal
The imaging features/advanced/subhalo/sensitivity example needs improving and padding out before workspaces medium safe normal
Adopt version.minimum_library_version in workspace configs workspaces small supervised high
Once https://github.com/PyAutoLabs/PyAutoLens/issues/480 is fixed (PointSolver workspaces too-large supervised normal

bug (27)

Prompt Target Difficulty Autonomy Priority
The - RectangularMagnification light adaptive pixelization visualization show signs of autoarray small safe normal
Investigate FactorGraphModel instance shape on Python 3.14 autofit too-large supervised normal
Make autofit.messages safe under JAX jit trace (xp.array → xp.stack) autofit large supervised normal
NFWTruncatedSph.potential_2d_from: MGE potential fails grad(psi)=alpha self-consistency autogalaxy too-large supervised high
The following line in @autogalaxy/profiles/geometry_profiles.py is probbaly a bug autogalaxy small safe normal
Investigate eager FitImaging.figure_of_merit vs JIT/step-by-step divergence in rectangular pixelization autolens too-large supervised high
Fix Autofit release sampler and database regressions health_fixes too-large supervised high
Fix release JAX runtime compatibility and likelihood parity health_fixes too-large supervised high
Fix JIT quick-update visualization output regressions health_fixes too-large supervised high
Fix release-profile numerical inversion failures health_fixes too-large supervised high
Resolve release-profile timeout scripts deliberately health_fixes too-large supervised normal
Fix release result/sample parameter-path regressions health_fixes too-large supervised high
@PyAutoFit LogGaussianPrior.with_limits will crash on first call (and so will priors medium safe normal
@PyAutoFit UniformPrior.logpdf does not handle array inputs priors large supervised normal
@PyAutoFit GammaMessage.from_mode produces a Gamma that matches neither the requested priors too-large supervised normal
@PyAutoFit TruncatedNormalMessage pdf does not integrate to 1 via the priors large supervised normal
@PyAutoFit inv_beta_suffstats negative-clamp branch is a no-op priors large supervised normal
@PyAutoFit NormalMessage silently accepts negative sigma; TruncatedNormalMessage rejects it priors large supervised low
@PyAutoFit log_prior_from_value convention is inconsistent across priors priors too-large supervised normal
@PyAutoFit RelativeWidthModifier produces zero / negative sigma for parameters near priors too-large supervised normal
@PyAutoFit Add property-based correctness tests for every Prior subclass priors large supervised normal
@PyAutoFit FixedMessage.logpdf_cache is an unbounded class-level dict priors large supervised high
@PyAutoFit TransformedMessage reversal convention is undocumented foot-gun priors large supervised normal
@PyAutoFit Refactor: each density should live in one place, not priors too-large supervised normal
@PyAutoFit Refactor: collapse the Prior / Message two-layer hierarchy priors too-large supervised normal
@PyAutoFit Refactor: replace hand-rolled AbstractDensityTransform with tfp.bijectors / numpyro.distributions.transforms priors too-large supervised normal
Priors & Messages cleanup — tracker priors too-large supervised normal

research (11)

Prompt Target Difficulty Autonomy Priority
PyAutoArray Delaunay interpolator's pure_callback vs vmap — minor efficiency follow-up autoarray too-large supervised low
Deep research: Can we speed up Delaunay in PyAutoArray? autoarray too-large supervised high
Use readthedocs or migrate to GitHub docs autobuild small supervised normal
Census of priors and messages — confirmed bugs + redesign autofit too-large supervised high
autofit_assistant planning — generic inference wiki + content migration autofit_assistant medium supervised normal
We have lots of examples which profile how long JAX autolens_workspace_developer medium supervised normal
EP framework review — statistics, docs, diagnostics, deterministic variables graphical_ep too-large supervised high
Expectation Propagation Scale-Up — Scoping graphical_ep too-large supervised high
Graphical Model Scale-Up — Scoping graphical_ep too-large supervised high
Hygiene agent decision pyautobrain medium supervised normal
Chase the ~6% flux scale between PyAutoReduce and legacy SLACS pyautoreduce medium supervised low

maintenance (4)

Prompt Target Difficulty Autonomy Priority
autolens_profiling is now a mature project, with a good separation autolens_profiling large supervised normal
polish phase 4 — likelihood breakdown and README dashboard autolens_profiling medium supervised normal
Auto-request GitHub Copilot code review on every PR, org-wide ci large supervised normal
autolens_workspace workspaces too-large supervised normal

docs (3)

Prompt Target Difficulty Autonomy Priority
The refer-back model (see @autolens_assistant/skills/start-new-project.md) means a spun-off autolens_assistant medium supervised normal
add-vincken-2026-wiki-and-cite-in-euclid workspaces small safe normal
Advanced workspace guide: Preloads (PyAutoArray) workspaces too-large supervised high

test (3)

Prompt Target Difficulty Autonomy Priority
Re-baseline the MGE imaging JIT profiling regression value autolens_workspace_developer too-large supervised high
Restore absolute NumPy likelihood regression baselines in the _workspace_test workspaces too-large supervised high
The new workspace smoke-test GitHub Actions (added via feature/smoke-test-ci) surfaced workspaces too-large supervised normal

refactor (1)

Prompt Target Difficulty Autonomy Priority
einstein_radius_jit_from: replace static init_guess with a JAX-native seed finder autogalaxy too-large supervised high

triage (1)

Prompt Target Difficulty Autonomy Priority
<!-- TRIAGE: needs manual review before routing - medium safe high