Every task the Mind is holding. Tap a task's π 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
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
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
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
single-source-density-design β issue #1500
prior-message-collapse-design β issue #1500
pyautoreduce-slacs1430-acs-comparison
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/PyAutoMCPβ¦
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
125 filed prompts, not started β sorted most-pickable first (priority, then size).
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
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
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=25β50 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
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
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
Phase 2: Make workspace READMEs assistant-first β workspaces
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
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: 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
CTI release-train wiring β first modern autocti release β autocti Β· medium Β· human-required Β· normal