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Agent skills
cli-tools

Topic: cli-tools

257 skills in this topic.

nonlinear-solvers Select and configure nonlinear solvers for root-finding f(x)=0, optimization min F(x), and least-squares problems — choose among Newton, Newton-Krylov, quasi-Newton (BFGS, L-BFGS), Broyden, Anderson acceleration, and Levenberg-Marquardt methods, configure line search or trust-region globalization, diagnose convergence rate (quadratic, linear, stagnated), and assess Jacobian quality and conditioning. Use when a Newton solver converges slowly or diverges, choosing between line search and trust region, debugging nonlinear iteration failures in FEM or phase-field codes, or selecting a solver for large-scale unconstrained optimization, even if the user only says "my Newton iterations aren't converging."
HeshamFS/materials-simulation-skills 29
numerical-integration Select and configure time integration methods for ODE and PDE simulations — choose among explicit Runge-Kutta, BDF, Rosenbrock, and Adams families, set relative and absolute error tolerances, implement adaptive step-size control with I/PI/PID controllers, plan IMEX operator splitting for mixed stiff and non-stiff terms, and estimate splitting errors. Use when picking an integrator for a new simulation, diagnosing step rejections or tolerance failures, setting up operator splitting for phase-field or reaction-diffusion problems, or deciding between explicit and implicit time marching, even if the user only says "my solver keeps rejecting steps" or "which ODE method should I use."
HeshamFS/materials-simulation-skills 29
numerical-stability Analyze numerical stability for time-dependent PDE simulations — check CFL and Fourier criteria, perform von Neumann stability analysis, detect stiffness, evaluate matrix conditioning, and recommend explicit vs implicit time-stepping schemes. Use when selecting time steps, diagnosing numerical blow-up or solver divergence, checking convergence criteria, or evaluating scheme stability for advection, diffusion, or reaction problems, even if the user doesn't explicitly mention "stability" or "CFL."
HeshamFS/materials-simulation-skills 29
time-stepping Plan and control time-step policies for transient simulations — couple CFL and physics-based stability limits with adaptive stepping, ramp initial transients through sharp gradients or phase changes, schedule output intervals and checkpoint cadence, and plan restart strategies for long-running jobs. Use when choosing dt for a new simulation, diagnosing adaptive time-step oscillations, deciding checkpoint frequency to minimize lost work, or setting up output schedules aligned with physical time scales, even if the user only says "my run is too slow" or "how often should I save."
HeshamFS/materials-simulation-skills 29
slurm-job-script-generator Generate correct, copy-pasteable SLURM sbatch job scripts and sanity-check HPC resource requests — configure nodes, MPI tasks, OpenMP threads, memory (per-node or per-cpu), GPUs, walltime, partitions, modules, and environment variables, with automatic detection of conflicting directives and oversubscription. Use when preparing a SLURM submission script, deciding between pure MPI and hybrid MPI+OpenMP layouts, standardizing #SBATCH directives across a team, debugging why a job won't launch or gets killed, or setting up GPU-accelerated simulation jobs, even if the user only says "I need to run this on the cluster" or "my job keeps getting killed."
HeshamFS/materials-simulation-skills 29
ontology-explorer 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. Supports the OCDO ecosystem (CMSO, ASMO, CDCO, PODO, PLDO, LDO). 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."
HeshamFS/materials-simulation-skills 29
ontology-mapper Map materials science terms, crystal structures, and sample descriptions to standardized ontology classes and properties — resolve natural-language concepts to ontology entries with confidence scores, translate Bravais lattice types, space groups, and lattice constants into ontology-compliant annotations, and produce full sample metadata from structured descriptions. Supports any ontology in ontology_registry.json (CMSO, ASMO, etc.). Use when annotating simulation inputs with FAIR metadata, translating "BCC iron" or "FCC copper" into formal ontology terms, preparing machine- readable sample descriptions, or bridging between lab vocabulary and ontology vocabulary, even if the user only says "what CMSO terms describe my material" or "annotate this sample for me."
HeshamFS/materials-simulation-skills 29
ontology-validator Validate material sample annotations against ontology constraints — check that class names and property names exist in the ontology, verify domain and range consistency for object property relationships, assess annotation completeness (required, recommended, and optional properties), and flag unknown or misspelled terms. Use when verifying that CMSO or other ontology annotations are correct before publishing, checking whether all required properties are present for a class like Crystal Structure or Unit Cell, auditing relationship triples between instances, or catching annotation errors early in a FAIR data workflow, even if the user only says "is my annotation correct" or "what am I missing."
HeshamFS/materials-simulation-skills 29
parameter-optimization 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."
HeshamFS/materials-simulation-skills 29
performance-profiling 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."
HeshamFS/materials-simulation-skills 29
post-processing 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."
HeshamFS/materials-simulation-skills 29
simulation-orchestrator 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."
HeshamFS/materials-simulation-skills 29
simulation-validator Validate simulations across three stages — run pre-flight checks on configuration files (parameter ranges, required fields, disk space), monitor runtime logs for residual growth, NaN/Inf, and adaptive dt collapse, and perform post-flight validation of results (physical bounds, mass/energy conservation, convergence). Diagnose failed simulations with probable-cause analysis and recommended fixes. Use when preparing to launch a simulation, checking whether a running job is healthy, verifying that finished results are trustworthy, or debugging a crash or blow-up, even if the user only says "my simulation crashed" or "can I trust these results."
HeshamFS/materials-simulation-skills 29
claude-sdk-integration-patterns Expert integration patterns for Claude API and TypeScript SDK covering Messages API, streaming responses, tool use, error handling, token optimization, and production-ready implementations for building AI-powered applications
manutej/luxor-claude-marketplace 47
fastapi-microservices-development Comprehensive guide for building production-ready microservices with FastAPI including REST API patterns, async operations, dependency injection, and deployment strategies
manutej/luxor-claude-marketplace 47
golang-backend-development Complete guide for Go backend development including concurrency patterns, web servers, database integration, microservices, and production deployment
manutej/luxor-claude-marketplace 47
spring-boot-development Comprehensive Spring Boot development skill covering auto-configuration, dependency injection, REST APIs, Spring Data, security, and enterprise Java applications
manutej/luxor-claude-marketplace 47
apache-airflow-orchestration Complete guide for Apache Airflow orchestration including DAGs, operators, sensors, XComs, task dependencies, dynamic workflows, and production deployment
manutej/luxor-claude-marketplace 47
pandas Expert data analysis and manipulation for customer support operations using pandas
manutej/luxor-claude-marketplace 47
PostgreSQL Database Administration Comprehensive PostgreSQL database administration skill for customer support tech enablement, covering database design, optimization, performance tuning, backup/recovery, and advanced query techniques
manutej/luxor-claude-marketplace 47
performance-benchmark-specialist Performance benchmarking expertise for shell tools, covering benchmark design, statistical analysis (min/max/mean/median/stddev), performance targets (<100ms, >90% hit rate), workspace generation, and comprehensive reporting
manutej/luxor-claude-marketplace 47
enterprise-architecture-patterns Complete guide for enterprise architecture patterns including domain-driven design, event sourcing, CQRS, saga patterns, API gateway, service mesh, and scalability
manutej/luxor-claude-marketplace 47
frontend-architecture Component architecture, design patterns, state management strategies, module systems, build tools, and scalable application structure
manutej/luxor-claude-marketplace 47
svelte-development Comprehensive Svelte development skill covering reactivity runes, components, stores, lifecycle, transitions, and modern Svelte 5 patterns
manutej/luxor-claude-marketplace 47