openmodelingfoundation
- 8 skills
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- 5 hours ago last updated
- ▌ Hpc · openmodelingfoundation bundleGenerate Slurm job scripts, job arrays, and resource allocation templates for running computational models on HPC (High-Performance Computing) clusters. Use this skill when you need multi-node execution, high-memory jobs, GPU/accelerator access, or direct Slurm cluster submission. Triggers: "run on HPC", "generate Slurm script", "set up batch array job", "submit to cluster", "create Slurm job array". Expected output: Slurm batch scripts (.slurm), job array configurations, resource allocation templates, and submission validation checklist.
- ▌ Fair · openmodelingfoundation bundleUse this skill when planning or reviewing FAIR stewardship of digital research objects, including research software, datasets, computational models, and workflows. Use it for FAIR metadata, reproducibility assessment, object/workflow provenance, persistent identifiers, citation, repository organization, dependency and environment management, packaging, portability, archival preparation, preservation, stewardship artifacts, and management planning. FAIR owns stewardship metadata, reproducibility assessment, object/workflow provenance, packaging, and pointers to scientific artifacts; it does not author or canonicalize model-card content. Triggers include making research objects FAIR, reproducible, citable, reusable, publication- or archive-ready; creating FAIR metadata (e.g. codemeta.json, CITATION.cff, RO-Crate); packaging or archiving a repository, dataset, or model; and developing FAIR, data, software, or maintenance plans. Expected output: a FAIR Management Plan as the sole canonical stewardship document, w
- ▌ Omfa · openmodelingfoundation bundleGuide modelers in applying good modeling practice across the full computational modeling lifecycle, from problem framing through evaluation, uncertainty disclosure, governance, and readiness for handoff to implementation. Use this skill when users want lifecycle guidance, quality self-assessment, required modeling deliverables, or protocol-specific checks for ABM, uncertainty, ethics, participatory modeling, deep uncertainty, or immediate lifecycle triage and handoff. Expected output: staged modeling guidance, identified deficiencies against required practices, handoff-readiness assessment, and a concrete set of required artifacts and review checks.
- ▌ Omfb · openmodelingfoundation bundleUse this skill when planning or reviewing the implementation of a computational model as research software. Helps translate scientific models into maintainable implementations, preserve traceability to the conceptual model, identify implementation risks, and delegate platform-specific practices to specialized guidance. Expected output: implementation guidance, implementation review, implementation planning, and routing to platform-specific implementation guidance.
- ▌ Ospool · openmodelingfoundation bundleGenerate HTCondor job submission scripts, parameter sweep configurations, and batch job templates for running computational models on the Open Science Grid (OSPool). Use this skill when you need to run parameter sweeps, large ensembles, or distributed sensitivity analysis on OSPool infrastructure. Triggers: "run on OSPool", "generate HTCondor script", "set up batch parameter sweep", "submit to OSG", "create HTCondor DAG". Expected output: HTCondor submit files (.submit), DAG specification files, parameter sweep configuration, and submission checklist.
- ▌ Document · openmodelingfoundation bundleGenerate narrative documentation for computational models and research software, and select the appropriate documentation framework for a given model type. Use this skill when a user wants to: - document a computational model - generate documentation from source code - write an ODD or ODD+2 narrative for an agent-based model - create model narratives for publication or reuse - draft narrative workflow documentation The skill classifies the model type, selects a framework, extracts model structure from supplied materials, and drafts documentation. It does not assess or score existing documentation — use the document-review skill for completeness assessment, gap analysis, or structured review output. For model-card requests or scientific model specifications, route to omfa. Inputs may include source code, pseudocode, READMEs, publications, architecture descriptions, or model metadata.
- ▌ Peer Review · openmodelingfoundation bundleEvaluate computational model submissions for OMF peer review readiness using reproducibility, documentation, code quality, and research software engineering criteria. Use this skill whenever a user asks to review a computational model, codebase, model release, or submission package for publication, reuse, or peer review. Trigger on phrases like: "peer review my model", "is this model submission ready", "review codebase quality", "check reproducibility", "review ODD documentation", "assess FAIR/research software quality". Expected output: structured peer review report with a binary recommendation limited to named baseline submission criteria, criterion-by-criterion findings, prioritized fixes, and an evidence-based checklist mapped to those criteria and key EVERSE research software quality indicators.
- ▌ Update Skill · openmodelingfoundation bundleRepository-local maintainer workflow for refreshing skill assets and references when upstream standards, rubrics, or guidance change. Use when updating compressed artifacts, checklists, and linked policy text across this repository in a consistent PR.