Business & Operations

Operate ByteDance Protenix-v2 for open biomolecular structure prediction of proteins, antibodies, nucleic acids, ligands, and complexes using JSON inputs, MSA and template features, constraints, and inference-time sampling. Use when running Protenix locally or through its server, comparing AlphaFold3-style open models, or building reproducible co-folding evaluations.

Operate Microsoft BioEmu to sample approximate equilibrium conformational ensembles for protein monomers from amino-acid sequences or supplied MSAs. Use when studying protein flexibility, alternative conformations, free-energy landscapes, disorder, ensemble generation, physical steering, or downstream side-chain reconstruction and MD relaxation.

--> --- name: source-management description: Manages connected MCP sources for enterprise search. Detects available sources, guides users to connect new ones, handles source priority ordering, and manages rate limiting awareness. keywords: - sources - mcp - connections - priority - rate-limiting measurable_outcome: Accurately detects connected MCP tools and routes queries to appropriate sources based on query intent. allowed-tools: - read_file - run_shell_command --- > If you see unfamiliar p...

--> --- name: bio-simpy description: Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-...

--> --- name: bio-reactome-database description: Query Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis, for systems biology studies. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-qiskit description: IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow...

--> --- name: bio-pytorch-lightning description: Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_c...

--> --- name: bio-medchem description: Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-matchms description: Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within...

--> --- name: bio-market-research-reports description: Generate comprehensive market research reports (50+ pages) in the style of top consulting firms (McKinsey, BCG, Gartner). Features professional LaTeX formatting, extensive visual generation with scientific-schematics and generate-image, deep integration with research-lookup for data gathering, and multi-framework strategic analysis including Porter Five Forces, PESTLE, SWOT, TAM/SAM/SOM, and BCG Matrix. tool_type: mixed primary_tool: Unkn...

--> --- name: bio-labarchive-integration description: Electronic lab notebook API integration. Access notebooks, manage entries/attachments, backup notebooks, integrate with Protocols.io/Jupyter/REDCap, for programmatic ELN workflows. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-iso-13485-certification description: Comprehensive toolkit for preparing ISO 13485 certification documentation for medical device Quality Management Systems. Use when users need help with ISO 13485 QMS documentation, including (1) conducting gap analysis of existing documentation, (2) creating Quality Manuals, (3) developing required procedures and work instructions, (4) preparing Medical Device Files, (5) understanding ISO 13485 requirements, or (6) identifying missing docu...

--> --- name: bio-edgartools description: Python library for accessing, analyzing, and extracting data from SEC EDGAR filings. Use when working with SEC filings, financial statements (income statement, balance sheet, cash flow), XBRL financial data, insider trading (Form 4), institutional holdings (13F), company financials, annual/quarterly reports (10-K, 10-Q), proxy statements (DEF 14A), 8-K current events, company screening by ticker/CIK/industry, multi-period financial analysis, or any SE...

--> --- name: bio-cobrapy description: Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-cirq description: Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output with...

--> <!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE --> --- name: 'dmmr-crc-histopathology-agent' description: 'Predict and validate colorectal cancer dMMR signals from H&E histopathology, including non-tumor and low-magnification WSI regions.' measurable_outcome: 'Execute skill workflow successfully with valid output within 15 minutes.' allowed-tools: - read_file - run_shell_command - web_fetch ---

--> <!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE --> --- name: 'scientific-spectral-vqa-benchmark' description: 'Evaluate MLLMs on scientific spectral images using SpecVQA-style figure extraction, curve-aware sampling, QA design, and scoring workflows.' measurable_outcome: 'Execute skill workflow successfully with valid output within 15 minutes.' allowed-tools: - read_file - run_shell_command - web_fetch ---

Evaluate and operate released Profluent OpenCRISPR gene-editing systems, especially OpenCRISPR-1, for controlled research workflows using its published Cas9-like protein, compatible guide RNA designs, protocols, licensing, specificity testing, and experimental validation. Use when comparing OpenCRISPR-1 with SpCas9, planning nonclinical editing studies, or assessing use in nuclease, nickase, deactivated, base, prime, or epigenome-editing contexts.

--> --- name: bio-genomics-vcf-operations description: 'VCF operations: multi-allelic parsing, variant classification (SNP/MNP/INS/DEL/COMPLEX), Ti/Tv ratio, QUAL/DP filtering, INFO field parsing. Mirrors bcftools stats.' tool_type: mixed primary_tool: genomics measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- VCF manipulation, filtering, merging, and summary statistics. Wraps bcftools and GATK Selec...

--> --- name: bio-genomics-assembly description: 'Genome assembly quality assessment: N50/N90/L50/L90 (QUAST-compatible), GC content, contig length distribution, completeness estimation. Wraps SPAdes, Megahit, Flye, Canu.' tool_type: mixed primary_tool: genomics measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- De novo genome assembly for short and long reads. Wraps SPAdes, Megahit, Flye, and Canu.

Operate Arc Institute Evo 2 for long-context DNA sequence scoring, zero-shot variant effect analysis, genomic embeddings, sequence generation, and model deployment. Use when a task explicitly needs Evo 2, million-base genomic context, DNA likelihood comparisons, genomic foundation-model embeddings, or generated DNA candidates across species.

Use Google DeepMind AlphaGenome to predict tissue-aware regulatory effects of DNA sequence variants across expression, splicing, chromatin, and contact-map outputs. Use when prioritizing noncoding variants, comparing reference and alternate alleles, visualizing predicted regulatory changes, or designing focused AlphaGenome API analyses.

Operate CZI TranscriptFormer cross-species generative single-cell models to produce cell embeddings, contextual gene embeddings, likelihoods, zero-shot classifiers, disease-state representations, and regulatory analyses from raw-count AnnData files. Use when selecting TF-Sapiens, TF-Exemplar, or TF-Metazoa, processing in- or out-of-distribution species, or scaling embedding extraction across GPUs.

--> <!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE --> --- name: 'stack-single-cell-icl-agent' description: 'Apply Arc Institute Stack, a single-cell foundation model that performs in-context learning at inference time without per-task fine-tuning.' measurable_outcome: 'Execute skill workflow successfully with valid output within 15 minutes.' allowed-tools: - read_file - run_shell_command - web_fetch ---