Performs theoretical translations, vocabulary generation, and origin synthesis for ancient cultures (including specific substrates like Cucuteni-Trypillia) using multi-disciplinary data. Supports speculative and non-speculative modes, strictly adhering to user-defined exclusions such as avoiding specific admixtures (e.g., Yamnaya).
Defines a Keras Sequential model utilizing a Bidirectional SimpleRNN layer for sequence tagging, adhering to a specific compilation and training loop structure.
Transcribes DNA sequences into complementary mRNA sequences, adhering to base pairing rules (A-U, T-A, C-G, G-C) and handling specific output directionality requests (5' to 3' or 3' to 5').
Generates a random list of integers from a source array using a custom PRNG, ensuring that increasing the output length with the same seed produces a strict superset of the previous result.
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.
Guidance for managing R package lifecycle according to tidyverse principles using the lifecycle package. Use when: (1) Setting up lifecycle infrastructure in a package, (2) Deprecating functions or arguments, (3) Renaming functions or arguments, (4) Superseding functions, (5) Marking functions as experimental, (6) Understanding lifecycle stages (stable, experimental, deprecated, superseded), or (7) Writing deprecation helpers for complex scenarios.
Identify outdated docstrings in the $1 library
Deep qualitative analysis of high-signal sessions. Spawns subagents with v2 template, synthesizes patterns, compares against known findings. Use after /session-scan.
Create publication-quality scientific diagrams using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.
Query STRING API for protein-protein interactions (59M proteins, 20B interactions). Network analysis, GO/KEGG enrichment, interaction discovery, 5000+ species, for systems biology.
Access BRENDA enzyme database via SOAP API. Retrieve kinetic parameters (Km, kcat), reaction equations, organism data, and substrate-specific enzyme information for biochemical research and metabolic pathway analysis.
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
Trigger Pattern SEP-41 token patterns detected (approve/transfer/transfer_from/allowance/balance) - Inject Into Breadth agents, depth-token-flow, depth-edge-case
OpenClaw bioinformatics skill library for genomics pipelines
Apply NLP and text mining techniques to research text data
Query AlphaFold protein structure predictions by UniProt accession
Access nucleotide sequence data from the European Nucleotide Archive
Perform gene set enrichment analysis using the Enrichr API
