πŸ“‹ PyAutoMind task dashboard

Every task the Mind is holding. Tap a task's πŸ“‹ and its /start_dev command is on your clipboard β€” paste it into a Claude Code chat to route Claude straight to that task.

In flight 1 Β· Parked 3 Β· Planned 6 Β· Backlog 125 Β· markdown version

Start here

Highest priority (filed as high) β€” showing 12 of 23

TRIAGE: needs manual review before routing β€” medium Β· safe Β· high

Decide whether the clipper belongs in the search identifier β€” autofit Β· medium Β· human-required Β· high

The ell_comps trapping was masked, not cleared β€” characterise it β€” autolens_profiling Β· medium Β· supervised Β· high

Cluster package: point-source-default narrative + extended-source follow-up feature β€” workspaces Β· medium Β· supervised Β· high

multi_galaxy package: new regime package in autolens_workspace β€” autolens Β· large Β· supervised Β· high

Profile and speed up JAX likelihood-function compile times (all use β€” autolens_profiling Β· large Β· supervised Β· high

Give the Profiling Agent a compile-time axis β€” the arc β€” profiling Β· large Β· supervised Β· high

Deep research: Can we speed up Delaunay in PyAutoArray? β€” autoarray Β· too-large Β· supervised Β· high

EP hierarchical parent-scale collapse: cure the basin, or document the β€” autofit Β· too-large Β· human-required Β· high

Census of priors and messages β€” confirmed bugs + redesign β€” autofit Β· too-large Β· supervised Β· high

einstein_radius_jit_from: replace static init_guess with a JAX-native seed finder β€” autogalaxy Β· too-large Β· supervised Β· high

Investigate eager FitImaging.figure_of_merit vs JIT/step-by-step divergence in rectangular pixelization β€” autolens Β· too-large Β· supervised Β· high

Quick wins (small enough, and safe enough to run unattended)

add-vincken-2026-wiki-and-cite-in-euclid β€” workspaces Β· small Β· safe Β· normal

add_notebook_quotes mistakes a code string literal's closing delimiter for a β€” hands Β· small Β· safe Β· low

Markdown renderings batch 2a β€” leftovers (ellipse/modeling + PNG size) β€” pyautobuild Β· small Β· safe Β· low

Raw-string the LaTeX docstrings emitting SyntaxWarnings (HowToFit + HowToLens) β€” workspaces Β· small Β· safe Β· low

Refresh the stale .script_sizes.json snapshot in @autolens_workspace β€” workspaces Β· small Β· safe Β· low

In flight

Issued β€” each has an open GitHub issue and usually a branch.

Reconstructing a stored sample raises through ignore_assertions=True β€” issue #1486 β€” library-dev β€” WORKSPACE HALF SHIPPED; the PyAutoFit hardening (#1486) is what remains

Parked

3 task(s)

single-source-density-design β€” issue #1500

prior-message-collapse-design β€” issue #1500

pyautoreduce-slacs1430-acs-comparison

Planned

6 task(s)

isothermal-ell-sph-oversampling-at-the-cusp β€” planned β€” NOT yet a prompt file; file one via /intake before starting

remote-mcp-deployment-tiers β€” issue #20 β€” DESIGN-COMPLETE, build BLOCKED-ON-DEMAND β€” issue #20 holds the full auth/transport/hosting design + Richard/PyAutoMCP…

samples-parameter-paths β€” issue #1327 β€” parked

jax-point-source-point-smoke-sentinel β€” planned

piemass-potential β€” planned

latent-nan-guard-honest-run

Backlog

125 filed prompts, not started β€” sorted most-pickable first (priority, then size).

bug β€” 30

EP hierarchical parent-scale collapse: cure the basin, or document the β€” autofit Β· too-large Β· human-required Β· high

Investigate eager FitImaging.figure_of_merit vs JIT/step-by-step divergence in rectangular pixelization β€” autolens Β· too-large Β· supervised Β· high

Fix Autofit release sampler and database regressions β€” health_fixes Β· too-large Β· supervised Β· high

Fix release JAX runtime compatibility and likelihood parity β€” health_fixes Β· too-large Β· supervised Β· high

Fix JIT quick-update visualization output regressions β€” health_fixes Β· too-large Β· supervised Β· high

Fix release-profile numerical inversion failures β€” health_fixes Β· too-large Β· supervised Β· high

Fix release result/sample parameter-path regressions β€” health_fixes Β· too-large Β· supervised Β· high

pixel_scales given as an int (or np.float64) is never widened β€” autoarray Β· small Β· supervised Β· medium

Heart script_timing baselines are orphaned by path moves and filled β€” pyautoheart Β· small Β· supervised Β· medium

jax_grad scripts fail assertions locally that PASS in CI β€” autolens_workspace_test Β· medium Β· supervised Β· medium

PyNUFFT dev extra is incompatible with current SciPy on Python β€” autoarray Β· small Β· supervised Β· normal

LogGaussianPrior misreports its own support as (-inf, inf) β€” autofit Β· small Β· supervised Β· normal

autofit.plot functions accept **kwargs and silently discard them β€” autofit Β· small Β· supervised Β· normal

TEST_MODE bypass crashes on ordered-parameter assertion ties β€” autofit Β· small Β· supervised Β· normal

point.py JAX-vmap parity assert is non-deterministic under the smoke env β€” autolens Β· small Β· supervised Β· normal

Scripts derive geometry from a hardcoded pixel_scale while the dataset β€” autolens_workspace Β· small Β· supervised Β· normal

HowToGalaxy small API drifts: ellipse kwargs + plot_grid_lines (parked NEEDS_FIX) β€” howtogalaxy Β· small Β· supervised Β· normal

generate.py deletes notebooks/ before rejecting an unknown project β€” pyautohands Β· small Β· supervised Β· normal

aplt.Output stale-API drift in the remaining workspace repos β€” workspaces Β· small Β· supervised Β· normal

Three jax_likelihood pins are stale by ~1.24e-4 and fail the β€” workspaces Β· small Β· supervised Β· normal

PROBE: is Adapt's 4th-power coefficient dependence (double square) intentional? β€” autoarray Β· medium Β· supervised Β· normal

interferometer Delaunay pixelization β€” non-PD FitException in test-mode bypass β€” autolens Β· medium Β· supervised Β· normal

JAX point-source smoke sentinel: point.py returns -1e99 instead of -83.38 β€” autolens Β· medium Β· supervised Β· normal

JIT cache not hit in modeling_visualization delaunay/rectangular scripts β€” autolens Β· medium Β· supervised Β· normal

@PyAutoFit TransformedMessage.logpdf/pdf omit the transform Jacobian β€” priors Β· medium Β· supervised Β· normal

Resolve release-profile timeout scripts deliberately β€” health_fixes Β· too-large Β· supervised Β· normal

@PyAutoFit Refactor: replace hand-rolled AbstractDensityTransform with tfp.bijectors / numpyro.distributions.transforms β€” priors Β· too-large Β· supervised Β· normal

Priors & Messages cleanup β€” tracker β€” priors Β· too-large Β· supervised Β· normal

add_notebook_quotes mistakes a code string literal's closing delimiter for a β€” hands Β· small Β· safe Β· low

interferometer/start_here.py OOM in nightly release-validation integrate leg β€” autolens

feature β€” 28

Decide whether the clipper belongs in the search identifier β€” autofit Β· medium Β· human-required Β· high

Profile and speed up JAX likelihood-function compile times (all use β€” autolens_profiling Β· large Β· supervised Β· high

Give the Profiling Agent a compile-time axis β€” the arc β€” profiling Β· large Β· supervised Β· high

Which other searches need prior-support handling β€” coverage audit after β€” autofit Β· medium Β· supervised Β· medium

Give PyAutoFit searches a seed β€” today no search can β€” autofit Β· medium Β· supervised Β· medium

Can create a list of InversionMatrix objects for each dataset β€” autoarray Β· medium Β· supervised Β· normal

The project @z_projects/ic50_workspace is our IC50 use case which we β€” autofit Β· medium Β· safe Β· normal

Tune cluster-scale JOSS benchmarks toward their 5-minute targets β€” autolens_workspace Β· medium Β· supervised Β· normal

One-tap dashboard pattern: roll out to more organs β€” pyautobrain Β· medium Β· supervised Β· normal

Implement a PyAutoHands build dashboard with one-tap copy-for-Claude commands β€” pyautohands Β· medium Β· supervised Β· normal

Token-light wiki index over the complete/ archive β€” pyautomind Β· medium Β· supervised Β· normal

The imaging features/advanced/los_halos example needs improving and padding out before β€” workspaces Β· medium Β· safe Β· normal

The imaging features/advanced/subhalo/sensitivity example needs improving and padding out before β€” workspaces Β· medium Β· safe Β· normal

Claude Development Prompt: Arcsecond Tick Label Decimal Placement β€” autoarray Β· large Β· supervised Β· normal

EP analytic updates β€” implement the four planned work packages β€” autofit Β· large Β· supervised Β· normal

Remote-MCP deployment tiers (2 + 3) for the results-inspector server β€” autofit_assistant Β· large Β· human-required Β· normal

Search settings-estimation + profiling infrastructure (n_starts / batch_size / n_batch) β€” autolens_profiling Β· large Β· supervised Β· normal

Adopt oversampled PSFs in the start-here dataset chain (option a) β€” autolens_workspace Β· large Β· supervised Β· normal

Follow-up to rectangular_adapt_cdf.md (issue #322) and Path A β€” autoarray Β· too-large Β· supervised Β· normal

PIEMass.potential_2d_from: implement the missing lensing potential β€” autogalaxy Β· too-large Β· supervised Β· normal

autolens_jax_joss benchmark repo + real-data start_here pairing β€” autolens_jax_joss Β· too-large Β· supervised Β· normal

Context: PyAutoLens issue #542 follow-up (Gap 1, deferred during the β€” jax_substructure Β· too-large Β· supervised Β· normal

Context: PyAutoLens issue #542 follow-up (Gap 2, deferred during the β€” jax_substructure Β· too-large Β· supervised Β· normal

Once https://github.com/PyAutoLabs/PyAutoLens/issues/480 is fixed (PointSolver β€” workspaces Β· too-large Β· supervised Β· normal

dPIE: optional central-dispersion (sigma_0) parameterization β€” autogalaxy Β· small Β· supervised Β· low

Gallery runner: add visualization_upper + decide the modeling_visualization_jit tier β€” workspaces Β· small Β· supervised Β· low

Scheduled runs β€” overnight queue passes with a morning report β€” autonomy Β· medium Β· supervised Β· low

Teach repos_sync --write to stamp organ config surfaces β€” pyautomind Β· hard Β· supervised Β· low

research β€” 21

The ell_comps trapping was masked, not cleared β€” characterise it β€” autolens_profiling Β· medium Β· supervised Β· high

Deep research: Can we speed up Delaunay in PyAutoArray? β€” autoarray Β· too-large Β· supervised Β· high

Census of priors and messages β€” confirmed bugs + redesign β€” autofit Β· too-large Β· supervised Β· high

Expectation Propagation Scale-Up β€” Scoping β€” graphical_ep Β· too-large Β· supervised Β· high

Graphical Model Scale-Up β€” Scoping β€” graphical_ep Β· too-large Β· supervised Β· high

Delaunay-family JAX modules never hit the persistent compilation cache β€” autoarray Β· medium Β· supervised Β· medium

Quick-update plotting cost β€” minutes per update, and it is β€” autolens Β· medium Β· supervised Β· medium

Use readthedocs or migrate to GitHub docs β€” autobuild Β· small Β· supervised Β· normal

Explore: dashboardify the Brain's operational surfaces with pasteable conductor prompts β€” pyautobrain Β· small Β· supervised Β· normal

Explore: dashboard-style surfaces for papers and wiki contents? β€” pyautomemory Β· small Β· supervised Β· normal

Re-baseline the slacs0008 acceptance parity after the HAP-dedupe fix β€” pyautoreduce Β· small Β· supervised Β· normal

Kernel-CDF bandwidth defaults β€” config-dependent quality, investigate adaptivity β€” autoarray Β· medium Β· supervised Β· normal

We have lots of examples which profile how long JAX β€” autolens_workspace_developer Β· medium Β· supervised Β· normal

slope_hierarchy: methods write-up (NUTS headline, EP cautionary) β€” graphical_ep Β· medium Β· supervised Β· normal

slope_hierarchy: scale the hierarchical slope recovery to N=25–50 β€” graphical_ep Β· medium Β· supervised Β· normal

Checkerboard PSF-mismatch residual diagnostic β€” research + document + ingest β€” pyautomemory Β· medium Β· supervised Β· normal

Multi-band compile census completion β€” A100/multi-core + hetero GPU rows β€” autolens_profiling Β· small Β· supervised Β· low

Chase the ~6% flux scale between PyAutoReduce and legacy SLACS β€” pyautoreduce Β· medium Β· supervised Β· low

PyAutoArray Delaunay interpolator's pure_callback vs vmap β€” minor efficiency follow-up β€” autoarray Β· too-large Β· supervised Β· low

Cluster-scale gradient-search benchmark (Prodigy vs Nautilus, point-source) β€” autolens_profiling

Adopt Python 3.12 as the PyAuto ecosystem minimum β€” libraries

maintenance β€” 18

autolens_workspace_developer rectangular experiments β€” Gut stash + rename β€” autolens_workspace_developer Β· small Β· supervised Β· normal

Mirror drifted library config keys into the workspace configs β€” workspaces Β· small Β· supervised Β· normal

Un-park imaging/features/scaling_relation/slam once PyAutoArray#431 merges β€” workspaces Β· small Β· supervised Β· normal

autolens_workspace_developer: broad stale-API rot (56 symbols, no CI) β€” autolens_workspace_developer Β· medium Β· supervised Β· normal

run_smoke.py: three runner variants across 10 repos, no sync mechanism β€” ci Β· medium Β· supervised Β· normal

Dependency-cap refresh 2026-08: safe bumps, astropy 8 decision, two dead β€” libraries Β· medium Β· supervised Β· normal

PyAutoMemory canonical-key TODO sweep β€” pyautomemory Β· medium Β· supervised Β· normal

Single-source the "Never rewrite history" policy as a generated AGENTS.md β€” pyautomind Β· medium Β· supervised Β· normal

Capped smoke datasets were committed as if they were real β€” workspaces Β· medium Β· supervised Β· normal

autolens_profiling is now a mature project, with a good separation β€” autolens_profiling Β· large Β· supervised Β· normal

Auto-request GitHub Copilot code review on every PR, org-wide β€” ci Β· large Β· supervised Β· normal

autolens_workspace β€” workspaces Β· too-large Β· supervised Β· normal

dataset/imaging/jwst_lw is untracked because the gitignore was never extended for β€” autolens_profiling Β· small Β· supervised Β· low

cosmos_web_ring stores boolean masks as float64, wasting ~3.4 MB of β€” autolens_workspace Β· small Β· supervised Β· low

LaTeX in non-raw docstrings emits SyntaxWarning: invalid escape sequence β€” autolens_workspace Β· small Β· supervised Β· low

Raw-string the LaTeX docstrings emitting SyntaxWarnings (HowToFit + HowToLens) β€” workspaces Β· small Β· safe Β· low

Regenerate setup_notebook-drifted notebooks in autogalaxy/autofit/HowToFit workspaces β€” workspaces Β· small Β· supervised Β· low

Refresh the stale .script_sizes.json snapshot in @autolens_workspace β€” workspaces Β· small Β· safe Β· low

docs β€” 16

Cluster package: point-source-default narrative + extended-source follow-up feature β€” workspaces Β· medium Β· supervised Β· high

multi_galaxy package: new regime package in autolens_workspace β€” autolens Β· large Β· supervised Β· high

Split lensing regimes: multi_galaxy / group / cluster (epic plan) β€” autolens Β· too-large Β· supervised Β· high

Advanced workspace guide: Preloads (PyAutoArray) β€” workspaces Β· too-large Β· supervised Β· high

Regenerate autolens_workspace markdown/ so the MGE pages show sigma_min β€” autolens_workspace Β· small Β· supervised Β· normal

add-vincken-2026-wiki-and-cite-in-euclid β€” workspaces Β· small Β· safe Β· normal

Propagate the shear_galaxy-at-(0,0) idiom to group/ and cluster/ β€” workspaces Β· small Β· supervised Β· normal

Rectangular mesh Enzi citation β€” user-workspace pixelization examples β€” workspaces Β· small Β· supervised Β· normal

Rewrite PyAutoCTI docs/api β€” 55 of 89 autosummary entries are β€” autocti Β· medium Β· supervised Β· normal

extra_galaxies feature parity: point_source + multi_galaxy (both workspaces) β€” workspaces Β· medium Β· supervised Β· normal

HowToLens ch4 tutorial 3: mask overlay is never actually drawn β€” howtolens Β· small Β· supervised Β· low

Markdown renderings batch 2a β€” leftovers (ellipse/modeling + PNG size) β€” pyautobuild Β· small Β· safe Β· low

Assistants: regime-aware routing for multi_galaxy / group / cluster (follow-up) β€” workspaces Β· medium Β· supervised Β· low

Phase 2 β€” drop the hand-written quick-update sentence from the β€” autolens_workspace

plot coverage β€” follow-ups deferred from plot-coverage-gaps β€” workspaces

Phase 2: Make workspace READMEs assistant-first β€” workspaces

refactor β€” 5

einstein_radius_jit_from: replace static init_guess with a JAX-native seed finder β€” autogalaxy Β· too-large Β· supervised Β· high

Vendor bessel_kve into autoarray and drop the tensorflow-probability dependency β€” autoarray Β· large Β· supervised Β· medium

Split Fitness.batch_size into lh_batch_size and latent_batch_size β€” autofit Β· small Β· supervised Β· normal

Slow imports: autolens 4.3s, autogalaxy 3.4s (hygiene perf tier, >3s β€” libraries Β· medium Β· supervised Β· normal

Remove the dead EDEN packaging tooling from PyAutoFit β€” pyautofit Β· medium Β· supervised Β· normal

test β€” 3

Re-baseline the MGE imaging JIT profiling regression value β€” autolens_workspace_developer Β· too-large Β· supervised Β· high

Restore absolute NumPy likelihood regression baselines in the _workspace_test β€” workspaces Β· too-large Β· supervised Β· high

The new workspace smoke-test GitHub Actions (added via feature/smoke-test-ci) surfaced β€” workspaces Β· too-large Β· supervised Β· normal

triage β€” 3

TRIAGE: needs manual review before routing β€” medium Β· safe Β· high

Triage: Convolver "No blurring_image provided" warning in canonical workspace scripts β€” small Β· supervised Β· normal

Nightly release has been blocked 8 nights running β€” triage β€” medium Β· supervised Β· normal

release β€” 1

CTI release-train wiring β€” first modern autocti release β€” autocti Β· medium Β· human-required Β· normal