How do I install the Rail skill?

Run npx skillmds@latest add jfcrenshaw/rail in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Rail?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

jfcrenshaw (@jfcrenshaw) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Rail by jfcrenshaw — Agent Skill

Name: Rail
Author: jfcrenshaw

Rail

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.

jfcrenshaw Updated Sep 22, 2026

File contents

RAIL — DESC photo-z framework

You're helping a user work with RAIL (Redshift Assessment Infrastructure Layers), the DESC photo-z framework. RAIL extends ceci's PipelineStage with a DataStore/DataHandle pattern; for ceci base conventions see the ceci skill. For NERSC job submission, see the nersc skill.

Architecture

RailStage adds self.get_data(tag) / self.add_data(tag, data) on top of the ceci file-tag system. The three modules are rail.creation (simulate catalogs), rail.estimation (run photo-z algorithms), rail.evaluation (quality metrics). RAIL is distributed across many sub-packages (rail_base, rail_astro_tools, rail_som, …) that all populate the rail.* namespace via setuptools entry-points — always import from rail.*, never from rail_base.* directly.

Key DataHandle types: PqHandle (Parquet), Hdf5Handle (HDF5, default group photometry), QPHandle (qp.Ensemble PDFs), ModelHandle (trained models).

Pipeline YAML: same ceci format; modules: must list every sub-package that provides a stage (e.g. rail.creation, rail_astro_tools).

RAIL creation pipeline (catalog simulation)

truth catalog (Parquet/HDF5)
  → QuantityCut              (pre-select columns/redshift range)
  → Reddener                 (SFD Galactic dust reddening, adds A_λ)
  → LSSTErrorModel           (photometric errors, non-detections → NaN)
  → Dereddener               (subtract A_λ = R_λ × EBV correction)
  → QuantityCut              (SNR / magnitude limit cuts on observed photometry)
  → SpecSelection_DESI_Phy   (physics-based DESI spec selection; in rail_astro_tools)

Key config notes:

Reddener / Dereddener: both in rail.tools.photometry_tools (rail_astro_tools); dustmap_dir (required) points to downloaded SFD files; band_a_env maps column names to R_λ (SF11: u=4.145, g=3.237, r=2.273, i=1.684, z=1.323, y=1.088); Reddener adds A_λ×E(B-V), Dereddener subtracts it; they do NOT add an EBV column
LSSTErrorModel: bands: [u,g,r,i,z,y]; magnitude columns named mag_{b}_lsst; NaN = non-detection
ObsCondition is for spatially-varying depth/seeing, NOT for SFD reddening — use Reddener/Dereddener for dust
SpecSelection_DESI_Phy: desi_type = "bgs"/"lrg"/"elg"; threshold_col = physical param (e.g. log_peak_sub_halo_mass); threshold_table = Parquet file with z, thresh columns
Two QuantityCut instances in one pipeline require ceci aliasing (duplicate name raises DuplicateStageName)

References

Topic	Reference
RailStage, DataHandle types, base class hierarchy, gotchas	`references/rail-core-concepts.md`
Full creation pipeline: all degradation stages, spec selectors, YAML skeleton, gotchas	`references/rail-creation.md`
Estimation base classes (CatInformer/Estimator/Summarizer), implementing algorithms, available algorithms, gotchas	`references/rail-estimation.md`
RailPipeline pipeline-as-code, Stage.build() wiring, pre-built pipelines, CatalogTag	`references/rail-pipelines.md`
RailProject multi-flavor project management, YAML config, CLI, shared library, gotchas	`references/rail-projects.md`

---name: raildescription: 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.---# RAIL — DESC photo-z frameworkYou're helping a user work with RAIL (Redshift Assessment Infrastructure Layers), the DESC photo-z framework.RAIL extends ceci's `PipelineStage` with a `DataStore`/`DataHandle` pattern; for ceci base conventions see the `ceci` skill.For NERSC job submission, see the `nersc` skill.## Architecture`RailStage` adds `self.get_data(tag)` / `self.add_data(tag, data)` on top of the ceci file-tag system.The three modules are `rail.creation` (simulate catalogs), `rail.estimation` (run photo-z algorithms), `rail.evaluation` (quality metrics).RAIL is distributed across many sub-packages (`rail_base`, `rail_astro_tools`, `rail_som`, …) that all populate the `rail.*` namespace via setuptools entry-points — always import from `rail.*`, never from `rail_base.*` directly.**Key DataHandle types:** `PqHandle` (Parquet), `Hdf5Handle` (HDF5, default group `photometry`), `QPHandle` (qp.Ensemble PDFs), `ModelHandle` (trained models).**Pipeline YAML:** same ceci format; `modules:` must list every sub-package that provides a stage (e.g. `rail.creation`, `rail_astro_tools`).## RAIL creation pipeline (catalog simulation)```truth catalog (Parquet/HDF5) → QuantityCut (pre-select columns/redshift range) → Reddener (SFD Galactic dust reddening, adds A_λ) → LSSTErrorModel (photometric errors, non-detections → NaN) → Dereddener (subtract A_λ = R_λ × EBV correction) → QuantityCut (SNR / magnitude limit cuts on observed photometry) → SpecSelection_DESI_Phy (physics-based DESI spec selection; in rail_astro_tools)```Key config notes:- `Reddener` / `Dereddener`: both in `rail.tools.photometry_tools` (`rail_astro_tools`); `dustmap_dir` (required) points to downloaded SFD files; `band_a_env` maps column names to R_λ (SF11: u=4.145, g=3.237, r=2.273, i=1.684, z=1.323, y=1.088); `Reddener` adds A_λ×E(B-V), `Dereddener` subtracts it; they do NOT add an EBV column- `LSSTErrorModel`: `bands: [u,g,r,i,z,y]`; magnitude columns named `mag_{b}_lsst`; NaN = non-detection- `ObsCondition` is for spatially-varying depth/seeing, NOT for SFD reddening — use `Reddener`/`Dereddener` for dust- `SpecSelection_DESI_Phy`: `desi_type` = "bgs"/"lrg"/"elg"; `threshold_col` = physical param (e.g. `log_peak_sub_halo_mass`); `threshold_table` = Parquet file with `z`, `thresh` columns- Two `QuantityCut` instances in one pipeline require ceci aliasing (duplicate `name` raises `DuplicateStageName`)## References| Topic | Reference ||---|---|| RailStage, DataHandle types, base class hierarchy, gotchas | [`references/rail-core-concepts.md`](references/rail-core-concepts.md) || Full creation pipeline: all degradation stages, spec selectors, YAML skeleton, gotchas | [`references/rail-creation.md`](references/rail-creation.md) || Estimation base classes (CatInformer/Estimator/Summarizer), implementing algorithms, available algorithms, gotchas | [`references/rail-estimation.md`](references/rail-estimation.md) || RailPipeline pipeline-as-code, Stage.build() wiring, pre-built pipelines, CatalogTag | [`references/rail-pipelines.md`](references/rail-pipelines.md) || RailProject multi-flavor project management, YAML config, CLI, shared library, gotchas | [`references/rail-projects.md`](references/rail-projects.md) |

Frequently asked questions

Rail

File contents

RAIL — DESC photo-z framework

Architecture

RAIL creation pipeline (catalog simulation)

References

Related skills

Frequently asked questions

How do I install the Rail skill?

What does the Rail skill do?

Is Rail safe to use?

Which AI agents work with Rail?

Is Rail free to use?

Who published Rail?