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Use when migrating a directory of local ROOT files to BNL-OSG2_LOCALGROUPDISK via Rucio on BNL SDCC nodes, or for any sub-step of that workflow: pre-flight checks (Rucio account, grid proxy, quotas, RSE names, pnfs mount), monitoring a Rucio replication rule until it reaches OK/STUCK, or building a symlink farm from an already-replicated dataset for transparent proxy-free access.

Use when working with the UChicago ATLAS Analysis Facility, submitting HTCondor batch jobs at UChicago, accessing JupyterLab at af.uchicago.edu, using XCache or Rucio for ATLAS data at UChicago, deploying ML models on Triton at UChicago AF, setting up ServiceX or Coffea Casa, or troubleshooting SSH access to login.af.uchicago.edu

Use when running or writing ART (ATLAS Release Tester) validation tests for ATLAS nightly builds, using art.py to run, list, or compare tests, debugging ART test failures in nightly CI, or understanding ATLAS release validation infrastructure.

Use when creating ATLAS publication-quality plots with the official ATLAS ROOT style macros (atlasrootstyle), applying SetAtlasStyle, placing ATLAS labels (Internal, Preliminary, Simulation), using ATLASLabel or myText helper functions, or following ATLAS publication plotting conventions in ROOT or PyROOT.

Use when visualizing ATLAS detector events with the Atlantis event display: launching the Atlantis GUI, opening JiveXML event files, producing JiveXML output from Athena reconstruction, or troubleshooting Java runtime issues in containerized environments.

Use when a question involves ATLAS software concepts: Athena framework, event data model, CP algorithms, derivation formats (DAOD), CMake build system, CVMFS software releases, analysis releases, ASG tools, or any ATLAS-internal software infrastructure. This skill routes to the right resource or subagent; for deep lookup delegate to atlas-docs-expert.

Use when searching for ATLAS Monte Carlo or data samples: querying the PMG central sample database for MC datasets by process, generator, or campaign, looking up cross-sections and filter efficiencies, or identifying which centrally produced samples are available for a given physics process.

Use when writing a columnar ATLAS analysis with coffea: defining a NanoEvents or custom processor, running over ROOT files, accumulating histograms with hist, applying scale factors and systematic weights, or migrating a for-loop event analysis to a coffea processor pattern.

Use when writing Python bindings for C++ HEP code with nanobind or pybind11: exposing a C++ class or function to Python, binding STL containers (vector, map), handling numpy/awkward array interop, choosing between nanobind and pybind11, or setting up a CMake build that produces a Python extension module.

Use when working with particle decay chains in Python: parsing EvtGen or DecFiles `.dec` decay descriptors with DecFileParser, building DecayChain and DecayMode objects programmatically, visualizing decay trees with DecayChainViewer, computing visible branching fractions, or converting AmpGen amplitude models to GooFit format. Also use when looking up LHCb or Belle II bundled decay tables, handling charge-conjugate decays, or filtering decay chains by minimum effective branching fraction.

Use when working with the ATLAS Event Index: looking up which dataset contains a specific run/event number, picking events from a list of run/event pairs, tracing event provenance through processing stages (RAW to AOD to DAOD), or listing all events in a dataset.

Use when you need a supported RDataFrame-based framework to process CP-algorithm NTuples or TopCPToolkit outputs into analysis histograms.

Use when running jet clustering in Python with the Scikit-HEP fastjet package (or migrating from the deprecated pyjet): calling anti-kt or Cambridge/Aachen algorithms on particle four-vectors, accessing jet constituents, computing jet substructure variables (N-subjettiness, soft drop), clustering jets from generator-level events read with pyhepmc or pylhe, or processing many events over awkward arrays with the array-oriented (columnar) interface to avoid slow Python loops.

Use when creating, filling, slicing, or plotting histograms with the scikit-hep hist library: defining axes (Regular, Variable, StrCategory), filling with weighted data, using UHI indexing (loc, rebin, sum), applying mplhep ATLAS style, producing ratio panels for data/MC comparisons, or projecting multi-dimensional histograms.

Use when setting up, running, or debugging a HistFitter statistical analysis: writing config scripts, defining channels/samples/systematics, choosing systematic types, running workspace generation and fits, producing yields or systematics tables, making pull/ranking plots, running hypothesis tests or upper limits, producing exclusion contours, debugging failed fits, using the built-in pyhf backend, or migrating a HistFitter analysis to pyhf.

Use when submitting ATLAS grid jobs with prun or pathena, monitoring tasks with pbook or BigPanDA, troubleshooting failed grid jobs, choosing between prun and pathena for distributed analysis, or configuring container-based or GPU grid submissions.

Use when looking up particle properties (mass, charge, width, PDG ID, lifetime, spin, parity) from the PDG tables in Python: converting between particle names and PDG IDs, filtering decay modes, checking if a particle is stable, querying quantum numbers (J, P, C, G, I), working with MC generator output where particle codes need to be identified or converted (Geant3, Pythia, Corsika7, EvtGen), or using particle/PDGID literals in analysis code.

Use when reading or writing HepMC3 event records in Python: opening HepMC2, HepMC3, LHEF, or HEPEVT files from a Monte Carlo generator, iterating over events, accessing truth particles and vertices, filtering by status code or PDG ID, traversing decay trees via particle parents/children, using the NumPy API for fast vectorized processing, visualizing events in Jupyter notebooks, or writing modified events back to a HepMC file.

Use when you need HistFactory statistical modeling, profile-likelihood fits, CLs limits, or workspace manipulation in Python.

Use when reading, writing, or validating binned and/or unbinned statistical models in the HS3 (HEP Statistics Serialization Standard) JSON format: loading an HS3 workspace, validating schema compliance, building and evaluating a model (logpdf), serialising a workspace for publication, or working with the standardized JSON format for ATLAS statistical models.

Use when reading or writing Les Houches Event (LHE) files in Python: reading parton-level events from MadGraph, Powheg, Sherpa, Pythia, or Whizard generators, iterating over initial- and final-state parton four-vectors, accessing event weights and scale/PDF reweighting blocks, counting events efficiently, writing modified LHE files back to disk, visualising event topologies with graphviz, or converting events to awkward arrays for vectorized analysis. Also use when the user mentions `LHEFile`, `LHEEvent`, `LHEParticle`, or parsing `.lhe` / `.lhe.gz` files.

Use when performing statistical unfolding for an ATLAS cross-section measurement: building a response matrix from MC, applying Bayesian iterative unfolding (RooUnfoldBayes), SVD unfolding (RooUnfoldSvd), bin-by-bin correction factors, or any other RooUnfold algorithm; handling detector-level fakes or backgrounds (response.Fake, SetBkg); propagating statistical and systematic uncertainties through the unfolding; comparing unfolding methods for stability; or performing closure tests.

Use when querying ATLAS data remotely via ServiceX: fetching branches from ROOT NTuples with the Uproot backend, writing FuncADL queries against DAOD_PHYS or DAOD_PHYSLITE, selecting datasets by Rucio name or EOS path, delivering data as awkward arrays, using servicex_analysis_utils helpers, debugging ServiceX cache or backend issues, or initializing ServiceX with servicex init.

Use when setting up the ATLAS software environment with setupATLAS or ATLASLocalRootBase, running asetup to configure an Athena or StatAnalysis release, using lsetup to set up ROOT, rucio, panda, scikit-hep, LCG views, or other ATLAS tools, using acm (AtlasACM) for cmake/git-based package management and compilation, managing ATLAS grid middleware (rucio, panda), or finding help contacts for ATLAS software support.

Use when using TopCPToolkit (TCT) to produce ATLAS analysis NTuples from DAOD files: setting up the YAML-based analysis configuration (Jets, Electrons, Muons, OverlapRemoval, EventSelection, Output blocks), understanding the single-TTree output format with systematic variations and object selection flags, installing and compiling TCT from source, running locally with runTop_el.py or on the Grid via submitToGrid.py, configuring detector-level and particle/parton-level analyses, writing custom analysis algorithms or config blocks, or reading TCT NTuple output with uproot.

Use when building a Python command-line interface with Typer: adding arguments and options to a script, creating subcommands, wiring an entry point into pyproject.toml, validating CLI inputs with callbacks, or adding a CLI layer to an existing analysis script or HEP workflow.

Use when setting up code quality tooling for a HEP Python project: configuring pre-commit hooks, adding ruff for linting and auto-formatting, setting up mypy for static type checking, or debugging linter failures in CI or a pre-commit run.

Use when a pytest suite is slow and needs speeding up: profiling slow tests, reducing collection time, parallelizing with pytest-xdist or pytest-split, selecting a subset of tests to run, blocking inadvertent network or large-file disk access in HEP tests, or optimizing CI test run time.

Use when creating a new Python package, adding pyproject.toml to an existing project, choosing a build backend, setting up src layout, configuring VCS-based versioning, or managing a development environment with pixi or uv for a HEP Python project.

Use when writing or configuring tests for a HEP Python project with pytest: setting up pyproject.toml test configuration, writing fixtures and parametrized tests, handling numerical tolerances for physics quantities, setting up code coverage, or choosing between unit and integration tests.

Use when generating a self-contained Python script with PEP 723 inline dependency metadata: writing the script block header, making a script runnable with uv run --script, combining with a Typer CLI for argument handling, or creating a one-off analysis tool that should not live inside a package and must declare its own dependencies inline.

Use when building an ATLAS statistical analysis with cabinetry: writing a cabinetry config file, building histogram templates from ROOT NTuples, constructing a pyhf workspace, running a profile likelihood fit, visualising pre/post-fit data-MC comparisons, producing pull plots and NP rankings, or computing CLs exclusion limits or discovery significance via cabinetry's high-level API.

Use when accessing ROOT files on EOS, WLCG grid storage, or any XRootD endpoint from Python: mounting an XRootD path as an fsspec filesystem, passing root:// URIs to uproot or awkward, listing or writing remote directories, multi-site replica access, or troubleshooting XRootD authentication and proxy issues.

Use when working with jagged or variable-length arrays in Python HEP analysis: building event records with ak.zip, filtering nested arrays, computing combinatorics (cartesian products or combinations), flattening ragged arrays, using argmin/argmax with keepdims, broadcasting per-event weights to per-object, or debugging OptionType/None-padding issues in Awkward 2.x workflows.

Use when writing unit-safe HEP code in Python: converting between MeV and GeV, expressing cross-sections or luminosities with explicit units, computing ctau or decay lengths from particle lifetimes, checking that cut thresholds or histogram ranges carry correct units, or bridging hepunits values with Pint quantities.

Use when building ATLAS software with acm (AtlasACM): setting up a cmake build environment with acmSetup, compiling packages, cloning or sparse-cloning athena, adding or excluding packages from the build, creating skeleton analysis packages, running ctests, or troubleshooting cmake configuration issues.

Use when running ATLAS software inside containers via setupATLAS -c, choosing between Apptainer/Docker/Podman/Shifter, setting up containers on macOS or without CVMFS, mounting directories, passing GPU flags, submitting batch jobs from containers, using custom or standalone container images, joining Docker sessions, or debugging container-related issues like timezone, symlink, or SSH-agent problems.

Use when setting up standalone LCG (CERN SFT) software packages via lcgenv or views: listing available LCG releases and platforms, configuring individual packages from a specific LCG release, setting up an entire LCG release at once with views, or combining LCG packages with other ATLAS tools like ROOT or rucio.

Use when installing, updating, or removing ATLAS software on a local Tier-3 cluster, laptop, or desktop with manageTier3SW, configuring ATLASLocalRootBase for a non-CVMFS site, running local ATLAS software diagnostics, setting up local post-user scripts, or troubleshooting Tier-3 ATLAS software installations.

Use when creating ATLAS publication-quality plots with matplotlib using mplhep: applying the ATLAS experiment style, placing ATLAS labels (Internal, Preliminary, Simulation), plotting 1D or 2D histograms from hist/uproot/numpy, making data/MC comparison plots with ratio panels, using mplhep comparison functions (ratio, pull, split_ratio), or customizing ATLAS label positions and luminosity text on matplotlib figures.