Perform spatial and temporal convergence analysis for solution verification — compute observed convergence orders from grid or timestep refinement studies, apply Richardson extrapolation to estimate discretization error, and calculate the Grid Convergence Index (GCI) per ASME V&V 20 standards. Use when verifying that a numerical solution converges at the expected rate, estimating the error on the finest mesh, checking whether grids are in the asymptotic range, or preparing formal verification reports, even if the user only asks "is my mesh fine enough" or "how accurate is my solution."
Rigorously evaluate an Agent Skill end-to-end across ANY coding-agent CLI — verify its scripts emit the documented numbers (deterministic checks), test whether its description triggers on the right prompts, and measure whether an agent following the SKILL.md beats a no-skill baseline (with/without pass-rate delta, mean ± stddev, benchmarked). Use whenever you need to test, benchmark, validate, grade, or quantify a skill's quality, check if a skill "actually works," compare two skill versions, optimize a skill's triggering, or set up an eval suite — even if the user just says "is this skill any good," "does my skill work," or "benchmark this skill." Drives Claude Code, OpenAI Codex, Antigravity (agy), Cursor, GitHub Copilot, Amp, opencode, or Grok in headless mode.
Parse, navigate, and query materials science ontology structures — browse class hierarchies, inspect individual classes and their properties, look up object and data property definitions with domain/range, search for ontology terms by keyword, and parse or summarize raw OWL/XML files. Currently supports CMSO and ASMO; the broader OCDO ecosystem (CDCO, PODO, PLDO, LDO) is planned. Use when exploring what classes or properties an ontology provides, finding the right CMSO term for a crystal structure or simulation concept, understanding parent-child class relationships, or onboarding to an unfamiliar materials ontology, even if the user only says "what ontology terms describe my FCC copper simulation" or "show me the CMSO class hierarchy."
First-response triage for cross-code (LAMMPS/VASP/QE/MOOSE) simulation failures: classifies the failure signature (nonconvergence, NaN/Inf, exploding energy, unstable timestep, pressure blow-up, missing potentials/pseudopotentials, memory exhaustion, process crash/segfault, corrupted output, incomplete runs) and proposes a SAFE one-change-at-a-time RETRY LADDER with explicit STOP CONDITIONS, prioritizing evidence preservation. Use as the immediate first response to a failed or suspicious run. For deep per-stage validation, conservation/physical-bounds checks, and "can I trust these results" analysis, defer to the simulation-validator skill.
Explore and optimize simulation parameters via design of experiments (DOE), sensitivity analysis, and optimizer selection — generate Latin Hypercube, quasi-random, or factorial sample plans, rank parameter influence with sensitivity scores, recommend Bayesian optimization, CMA-ES, or gradient- based methods based on dimension and budget, and fit surrogate models for expensive evaluations. Use when calibrating material properties against experimental data, planning a parameter sweep, performing uncertainty quantification, or choosing an optimization strategy for a simulation with a limited evaluation budget, even if the user only says "which parameters matter most" or "how do I calibrate my model."
Identify computational bottlenecks, analyze parallel scaling, estimate memory requirements, and generate optimization recommendations for materials simulations — parse timing logs to find dominant phases (solver, assembly, I/O), evaluate strong and weak scaling efficiency, profile memory from mesh and field parameters, and detect bottlenecks with actionable fix suggestions. Use when a simulation is running slower than expected, investigating MPI scaling efficiency, planning HPC resource allocation, deciding whether to tune the preconditioner or reduce I/O frequency, or estimating if a problem fits in available RAM, even if the user only says "my simulation is too slow" or "how many nodes do I need."
Extract, analyze, and summarize simulation output data — pull spatial fields at specific timesteps, compute time-series trends and detect steady state, extract line profiles through the domain, generate statistical summaries and distributions, calculate derived quantities (gradients, fluxes, volume fractions, interface area), compare results against analytical solutions or experimental data, and produce automated analysis reports. Use when interpreting finished simulation results, checking mass or energy conservation, comparing two runs or meshes, extracting interface profiles from phase-field output, or preparing publication-quality analysis, even if the user only says "what do my results look like" or "did my simulation reach steady state."
Orchestrate multi-simulation campaigns — generate parameter sweep configurations (grid, linspace, or Latin Hypercube sampling), initialize and track batch job campaigns, monitor job completion status, and aggregate results with summary statistics across all runs. Use when running a parameter study across dt, kappa, or other simulation inputs, managing dozens or hundreds of simulation configurations, combining outputs from completed batch runs to find the best result, or automating the generate-run-collect workflow for systematic studies, even if the user only says "I need to try many parameter combinations" or "how do I organize a sweep."