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更新日2026年5月18日 16:12

Ecosystem-level guide to the DESC (Dark Energy Science Collaboration) Python stack. Covers which tools exist, how they fit together, general DESC coding conventions, and how to set up the shared DESC Python environment and Jupyter kernels at NERSC. Use this skill when the user is new to DESC or asks what DESC software exists; asks how DESC packages fit together; asks about cross-package data flow (e.g. TXPipe → sacc → Firecrown, or RAIL inside TXPipe); asks about general DESC coding conventions; needs to choose the right tool for a DESC analysis task; asks how to activate or install the DESC Python environment; asks about desc-python, desc-python-dev, desc-python-bleed, or other DESC Jupyter kernels; or asks a question spanning multiple DESC packages with no single obvious skill to invoke. For questions specific to a single package (ceci, TXPipe, RAIL, pyccl/CCL, pymaster/NaMaster, firecrown, photerr), prefer that package's dedicated skill instead.

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jfcrenshaw

jfcrenshaw/claude-desc-skills

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LSST DESC Python ecosystem

You're helping a user write DESC science code. DESC (Dark Energy Science Collaboration) is the primary LSST weak-lensing / large-scale-structure science collaboration. Most work happens at NERSC (see the nersc skill for cluster conventions); this skill focuses on the DESC software stack itself.

For package-specific work, invoke the dedicated skill.

Package skills

Package	Purpose	Skill
`ceci`	Pipeline framework: stage definition, YAML pipelines, MPI, file I/O	`/ceci`
`TXPipe`	Main DESC 3×2pt pipeline: selection → maps → 2pt → covariance → theory	`/txpipe`
`RAIL`	Photo-z framework: creation (catalog simulation), estimation, evaluation	`/rail`
`pyccl` / CCL	Core Cosmology Library: theory predictions, tracers, angular/3D power spectra	`/ccl`
`pymaster` / NaMaster	Pseudo-Cl estimator: NmtField/Workspace, mode-coupling, Gaussian covariance	`/namaster`
`firecrown`	Likelihood framework: statistics, systematics, sacc I/O, CosmoSIS/NumCosmo connectors	`/firecrown`
`photerr`	Photometric error modeling for LSST, Euclid, and Roman; LsstErrorModel/EuclidErrorModel/RomanErrorModel	`/photerr`

How the packages fit together

TXPipe and RAIL are both built on top of ceci — all their stages are ceci PipelineStage subclasses. RAIL extends ceci with a DataStore/DataHandle pattern for managing in-memory data across stages. TXPipe uses RAIL stages for its photo-z steps (PZEstimatorLens/Source, PZRailSummarizeLens/Source).

TXPipe uses NaMaster (pymaster) for Fourier-space power spectrum estimation and CCL (pyccl) for theory predictions in its theory stages. TXPipe outputs sacc files that Firecrown consumes for cosmological parameter inference; some TXPipe theory stages call Firecrown's load_likelihood_from_script directly.

A typical DESC analysis pipeline flows: truth simulation → RAIL creation (degrade photometry with photerr) → RAIL estimation (photo-z) → TXPipe (2pt statistics) → Firecrown (parameter inference).

General DESC coding conventions

DESC packages are Python 3; most require Python ≥ 3.9.
Prefer the official DESC I/O helpers over rolling your own FITS/HDF5 reads — they handle unit conventions and multi-file layouts correctly.
When in doubt about units, check the package docs: DESC packages are not always consistent with each other on angular units (radians vs arcmin vs degrees).
For catalog-scale work (millions of objects), use numpy / fitsio / h5py over astropy table reads — they're dramatically faster on NERSC Lustre.
DESC repos live on GitHub under the LSSTDESC organization: https://github.com/LSSTDESC.

DESC Python environment at NERSC

See desc-python-env.md for the full reference. Key facts:

Activate (command line): source /global/common/software/lsst/common/miniconda/setup_current_python.sh
Add Jupyter kernels (one-time): source /global/common/software/lsst/common/miniconda/kernels/setup.sh
Requires membership in the NERSC lsst group; see desc-python-env.md for access instructions.
Default kernel for notebooks: desc-python (current production release).
Other kernels: desc-python-dev, desc-python-bleed, desc-python-old, desc-python-gpu, plus stack and specialized envs.

このリポジトリの他の Skills

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photerr

jfcrenshaw/claude-desc-skills

Guide to photerr, a Python library for photometric error modeling in astronomical imaging surveys (LSST/Rubin, Euclid, Roman). Covers ErrorModel/ErrorParams architecture, survey-specific models (LsstErrorModel, EuclidErrorModel, RomanErrorModel and variants), parameter customization (nYrObs, nVisYr, m5, theta, gamma, scale, sigmaSys), non-detection handling (ndMode, sigLim, ndFlag), extended-source aperture corrections (extendedType="auto"/"gaap"), and getLimitingMags. Use this skill whenever the user imports photerr; works with LsstErrorModel, EuclidErrorModel, RomanErrorModel, or ErrorModel; calls getLimitingMags; simulates photometric errors or survey depth; applies photometric noise to a magnitude catalog; handles non-detections or limiting magnitudes; models extended-source (galaxy) photometry; compares survey depths or error budgets across LSST, Euclid, or Roman; or asks how to add realistic photometric scatter to a simulated catalog. For RAIL pipeline stages that wrap photerr (LSSTErrorModel degradatio

2026-05-183

ceci

jfcrenshaw/claude-desc-skills

How to write DESC pipeline stages and pipelines with ceci. Covers PipelineStage subclassing, inputs/outputs declarations, config_options, HDFFile/FitsFile/TextFile/YamlFile I/O, open_input/open_output, parallel iteration helpers, MPI patterns, pipeline YAML structure (modules/launcher/site/stages/inputs), stage aliasing, CLI usage, resume mode, nprocess, and NERSC/parsl deployment. Use this skill whenever the user imports ceci; subclasses PipelineStage; defines inputs, outputs, or config_options on a stage; calls open_input or open_output; writes or debugs a pipeline YAML; runs ceci from the CLI; asks about stage aliasing, DuplicateStageName, resume mode, nprocess/MPI, parsl launchers, or ceci file types.

2026-05-083

namaster

jfcrenshaw/claude-desc-skills

Guide to NaMaster (pymaster), the DESC pseudo-Cl angular power spectrum estimator. Use this skill whenever the user imports pymaster; works with NmtField, NmtBin, NmtWorkspace, or NmtCovarianceWorkspace; calls compute_full_master, compute_coupled_cell, decouple_cell, or mask_apodization; computes pseudo-Cl power spectra for spin-0 or spin-2 (shear) fields; handles masks, mode-coupling matrices, or workspace serialization; asks about bandpower binning, EE/BB/EB spectrum ordering, or apodization; writes TXPipe Fourier-space stages (TXTwoPointFourier, TXFourierNamasterCovariance); or needs Gaussian covariance estimates.

2026-05-083

rail

jfcrenshaw/claude-desc-skills

How to work with RAIL (Redshift Assessment Infrastructure Layers), the DESC photo-z framework built on ceci. Covers RailStage/DataStore/DataHandle patterns, namespace architecture (rail.* entry-points), DataHandle types (QPHandle, PqHandle, Hdf5Handle, ModelHandle), three modules (rail.creation/estimation/evaluation), CatInformer/CatEstimator/PZEstimator base classes, RailPipeline/RailProject, pipeline YAML format, and the catalog simulation workflow (QuantityCut/Reddener/LSSTErrorModel/Dereddener/SpecSelection). Use this skill whenever the user imports rail; works with RailStage, DataStore, or any DataHandle type; does photo-z training, estimation, or evaluation; uses any rail.creation degradation stage (QuantityCut, Reddener, LSSTErrorModel, Dereddener, ObsCondition, SpecSelection_DESI_Phy); works with qp.Ensemble PDFs; sets up a RailPipeline or RailProject; asks about photo-z algorithms (FlexZBoost, BPZ, KNN, GPz, DNF, etc.); or asks about the rail.* namespace and entry-point architecture.

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skill-design

jfcrenshaw/claude-desc-skills

Best-practices guide for designing and auditing Claude Code skills and global configuration. Covers CLAUDE.md length and content fitness, skill naming and description quality, SKILL.md structure and progressive disclosure, trigger condition robustness, settings.json permissions, and directory hygiene. Use whenever asked to write, update, audit, or improve a SKILL.md file; evaluate trigger condition quality; review or suggest improvements to a CLAUDE.md or settings.json; create a new skill from scratch; improve an existing skill; or assess whether skills in a set cross-reference each other correctly.

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txpipe

jfcrenshaw/claude-desc-skills

How to work with TXPipe, the DESC 3x2pt weak-lensing pipeline. Covers all pipeline phases (ingestion → photo-z → selection → calibration → masks → maps → noise maps → 2pt → covariance → theory → diagnostics), key stage names, file tags, pipeline YAML structure, and critical gotchas. Use this skill whenever the user imports txpipe; works with any TXPipe stage (TXTwoPoint, TXTwoPointFourier, TXSourceMaps, TXLensMaps, TXSourceSelector*, TXShearCalibration, TXTracerMetadata, TXSimpleMask, TXCustomMask, TXIngest*, TXBlinding, TXFourierNamasterCovariance, TXRealNamasterCovariance, TXJackknifeCenters, TXPSFDiagnostics, TXRoweStatistics, TXTauStatistics, etc.); writes a TXPipe pipeline YAML; asks about shape or shear catalogs, metacalibration or metadetect, source or lens selection, tomographic binning, 3x2pt two-point functions (shear-shear, galaxy-galaxy lensing, angular clustering), map generation, noise maps, jackknife covariance, PSF diagnostics, or theory predictions.

2026-05-083

name

desc

description

LSST DESC Python ecosystem

For package-specific work, invoke the dedicated skill.

Package skills

Package	Purpose	Skill
`ceci`	Pipeline framework: stage definition, YAML pipelines, MPI, file I/O	`/ceci`
`TXPipe`	Main DESC 3×2pt pipeline: selection → maps → 2pt → covariance → theory	`/txpipe`
`RAIL`	Photo-z framework: creation (catalog simulation), estimation, evaluation	`/rail`
`pyccl` / CCL	Core Cosmology Library: theory predictions, tracers, angular/3D power spectra	`/ccl`
`pymaster` / NaMaster	Pseudo-Cl estimator: NmtField/Workspace, mode-coupling, Gaussian covariance	`/namaster`
`firecrown`	Likelihood framework: statistics, systematics, sacc I/O, CosmoSIS/NumCosmo connectors	`/firecrown`
`photerr`	Photometric error modeling for LSST, Euclid, and Roman; LsstErrorModel/EuclidErrorModel/RomanErrorModel	`/photerr`

How the packages fit together

General DESC coding conventions

DESC packages are Python 3; most require Python ≥ 3.9.
Prefer the official DESC I/O helpers over rolling your own FITS/HDF5 reads — they handle unit conventions and multi-file layouts correctly.
When in doubt about units, check the package docs: DESC packages are not always consistent with each other on angular units (radians vs arcmin vs degrees).
For catalog-scale work (millions of objects), use numpy / fitsio / h5py over astropy table reads — they're dramatically faster on NERSC Lustre.
DESC repos live on GitHub under the LSSTDESC organization: https://github.com/LSSTDESC.

DESC Python environment at NERSC

See desc-python-env.md for the full reference. Key facts:

Activate (command line): source /global/common/software/lsst/common/miniconda/setup_current_python.sh
Add Jupyter kernels (one-time): source /global/common/software/lsst/common/miniconda/kernels/setup.sh
Requires membership in the NERSC lsst group; see desc-python-env.md for access instructions.
Default kernel for notebooks: desc-python (current production release).
Other kernels: desc-python-dev, desc-python-bleed, desc-python-old, desc-python-gpu, plus stack and specialized envs.