PantheonOS

Skills for opening and driving agent-controllable visualization components in the Pantheon UI sidebar — interactive viewers the agent can open, control, and read back. Viewers: Vitessce (spatial / single- cell omics), Viv (bioimage / microscopy), volume3d (3D image volumes — MIP/ISO), spatial3d (3D spatial transcriptomics), Mol*, IGV, Gosling, Cytoscape, MSA, RDKit, phylotree, plus agent-generated apps.

General-purpose skills for data analysis infrastructure: workspace file organization, environment management, parallel computing, and performance.

Skills for single-cell and spatial omics data analysis. Best practices, code snippets, and workflows for the scverse ecosystem.

Aesthetic guidelines and output-type recipes for scientific figure production. Supports lightweight default-agent use through SKILL.md + one outputType recipe, with optional venue-specific style guides when requested.

Query the Virtual Embryo knowledge graph (mouse/human developmental biology: genes, anatomy, Theiler/Carnegie stages, gene expression, diseases, papers) and its 3D atlas catalog (anatomical OPT/light-sheet volumes + 3D spatial- transcriptomics datasets), and visualise those datasets in 3D with the volume3d / spatial3d live-view viewers. Public read-only HTTP API at https://kg.virtualembryo.ai — no auth, no key needed for reads. Use when the user asks about mouse/human embryo development, where a gene is expressed, an anatomical structure, a developmental stage, or wants to see/visualise the Virtual Embryo atlas or spatial-transcriptomics data.

Skills for rare disease case support: ontology-first normalization/retrieval and the clinical genetics consult report format contract (structure + theme). Load the relevant skill file when performing the matching task.

Scenario router for paper-writing tasks. Use after root triage to select paper submission, journal article, conference paper, grant proposal, lab report, group report, talk/workshop, or revision-response behavior.

Routing and workflow skill family for paper-writing tasks. Covers manuscript drafting, journal and conference papers, grant proposals, lab reports, group-meeting reports, talks, workshop notes, reviewer rebuttals, academic HTML/PDF/LaTeX output with editable-block contracts, citation grounding, evidence checking, and pre-submission quality gates.

Workflow phases for paper-writing tasks: triage, material inventory, research question, literature review, paper outline, data analysis summary, figure storyline, reader testing, and finalize packet. Each phase is a short contract — read the relevant rows for the current task only.

Section-level writing skill for evidence-bound academic prose: IMRaD papers, grants, reports, talks, and response letters. Indexes section-specific templates (abstract, introduction, method, results, discussion) and pre-submission quality protocols (claim-evidence, reviewer rubric, response letter).

Skills for querying and downloading data from genomic, transcriptomic, 3D-genome, and cancer-genomics databases. Covers programmatic access to public repositories, gene annotation, sequence retrieval, processed functional-genomics tracks, Hi-C / Micro-C contact matrices, TCGA-style cohorts, and large-scale single-cell data.

Obtain and predict protein 3D structures — fetch AlphaFold predicted models from the AlphaFold DB, experimental structures from the RCSB PDB, or predict a novel sequence with ColabFold — and visualise them in the Mol* LiveView.

Skills for creating presentations, slides, and visual documentation.

Cell and nucleus segmentation tools for microscopy images. Covers Cellpose, SAM-based methods, StarDist, InstanSeg, and Mesmer.

Skills for biological image analysis: cell/nucleus segmentation, image restoration, and spatial data processing.

Skills for spatial transcriptomics analysis including single-cell to spatial mapping (MOSCOT), 3D visualization (PyVista), and related spatial workflows.

Workflow guidance and model reference for single-cell foundation models (scGPT, Geneformer, UCE, scBERT, etc.). Covers model selection, validation-first workflow, and per-model I/O contracts.

Core skills for single-cell RNA-seq analysis: quality control, cell type annotation, and trajectory inference. These are high-priority actionable workflows — load them first for common single-cell tasks.

End-to-end workflow for gene panel design in scRNA-seq and spatial transcriptomics, that should be **STRICTLY** followed: dataset understanding + smart downsampling + train/test splits, algorithmic selection (HVG/DE/RF/scGeneFit/SpaPROS), optimal sub-panel discovery (ARI vs size), biological completion with a stability gate (Completion Rule), consensus scoring and completion (only if there is still room), and benchmarking on test splits (ARI/NMI/Silhouette + UMAP similarity).

Skills for using nf-core community pipelines to process omics data, from installation and configuration to running specific analysis pipelines.

Skills for Open-ST spatial transcriptomics data processing, from raw BCL files to spatially-resolved single-cell h5ad objects.

Skills for upstream data processing in single-cell and spatial omics, covering raw data generation, barcode processing, alignment, spatial registration, and technology-specific preprocessing pipelines.

Skills derived from the Single-cell Best Practices book (sc-best-practices.org). Comprehensive workflows and guidelines for single-cell and spatial omics analysis.