Business & Operations

AI-native accounting for UK micro-businesses. Use when the user wants to track transactions, manage VAT, check deadlines, or do any bookkeeping for a UK limited company.

File-based workflow coordinator for Greek accounting. Defines processing pipelines, validation rules, and routine templates. No external APIs needed.

Deploy smart contracts and bridge assets to Abstract (ZK Stack L2). Use when an agent needs to deploy contracts on Abstract, bridge ETH/tokens to Abstract, trade/swap tokens, place predictions on Myriad Markets, check balances, transfer assets, or interact with Abstract mainnet. Covers zksolc compilation, Hardhat deployment, Relay bridging, DEX trading (Kona, Aborean), Myriad prediction markets, and key contract addresses.

Calculate ROI for AI-as-a-Service (managed AI agents). Estimates cost savings, efficiency gains, and payback period for deploying AI agents across business operations. Use when evaluating whether managed AI agents make financial sense for a company.

Unified issue discovery and creation. Create issues from GitHub/text, discover issues via multi-perspective analysis, or prompt-driven iterative exploration. Triggers on \"issue:new\", \"issue:discover\", \"issue:discover-by-prompt\", \"create issue\", \"discover issues\", \"find issues\".

Unified brainstorming skill with dual-mode operation — auto mode (framework generation, parallel multi-role analysis, cross-role synthesis) and single role analysis. Triggers on "brainstorm", "头脑风暴".

RNA velocity and cellular dynamics analysis for spatial transcriptomics using scVelo stochastic / deterministic / dynamical models or VELOVI, with method-aware preprocessing, graph, training controls, and a standardized OmicsClaw gallery + figure_data output contract.

Run scVelo on a velocity-ready h5ad using stochastic, dynamical, or steady-state modes. Use `sc-velocity-prep` first if spliced/unspliced layers are missing.

Single-cell pseudotime and lineage inference after clustering, with DPT, Palantir, VIA, CellRank, or Slingshot plus post-hoc trajectory gene ranking.

Prepare perturbation-ready scRNA AnnData objects by merging barcode-to-guide assignments into expression data and exporting a downstream-safe h5ad for `sc-perturb`.

Discover de novo gene programs and per-cell usage scores from scRNA-seq data using cNMF-compatible or NMF workflows.

Statistical enrichment analysis for single-cell RNA-seq using ORA or preranked GSEA on marker or differential-expression rankings. This skill is for GO/KEGG/Reactome/Hallmark term significance, not per-cell pathway activity scoring.

Annotate putative doublets in single-cell RNA-seq data using Scrublet, DoubletDetection, DoubletFinder, scDblFinder, or scds. The wrapper preserves the current AnnData matrix semantics, standardizes output columns in `obs`, and exports a reusable figure/table gallery.

Integrate multi-sample scRNA-seq data with Harmony, scVI, scANVI, BBKNN, Scanorama, or supported R-backed integration methods.

Metabolite annotation and structural identification using SIRIUS, CSI:FingerID, GNPS, or MetFrag.

VCF operations: multi-allelic parsing, variant classification (SNP/MNP/INS/DEL/COMPLEX), Ti/Tv ratio, QUAL/DP filtering, INFO field parsing. Mirrors bcftools stats.

Genome assembly quality assessment: N50/N90/L50/L90 (QUAST-compatible), GC content, contig length distribution, completeness estimation. Wraps SPAdes, Megahit, Flye, Canu.

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...