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Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time -> debris -> singlets -> live -> lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semantics, rare-event/MRD gating, and per-population statistics. Use when building a gating strategy, automating a manual FlowJo scheme across samples, choosing manual vs data-driven gates, or extracting population frequencies.

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Detects acquired antimicrobial-resistance determinants and chromosomal point-mutation resistance in bacterial assemblies using AMRFinderPlus, ResFinder 4.0 (acquired + PointFinder), CARD-RGI, abritAMR, staramr, and species-specific callers (TB-Profiler, Mykrobe). Harmonises cross-tool output via hAMRonization, contextualises determinants with mobile-genetic-element annotation (MOB-suite, PlasmidFinder, MobileElementFinder, ICEberg), predicts phenotype against EUCAST or CLSI breakpoints, and translates calls into WHO GLASS reporting categories. Use when screening clinical or surveillance isolates for AMR, distinguishing acquired vs intrinsic vs point-mutation resistance, calling rpoB / katG / pncA / gyrA / mgrB mutations, reconciling AMRFinderPlus vs RGI vs ResFinder disagreement, contextualising carbapenemases or mcr alleles on plasmids, predicting susceptibility from genotype against the WHO Mtb 2nd-edition catalogue, or building a hAMRonized multi-lab AMR surveillance pipeline.

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Query UniProt's REST API (post-2022 endpoint at rest.uniprot.org) for protein sequences, annotations, GO terms, cross-references, ID mappings, and proteomes. Use when fetching UniProtKB entries, navigating the JSON schema, choosing between UniProtKB/UniRef/UniParc/Proteomes resources, deciding stream vs search endpoint for batch retrieval, running ID-mapping jobs with the async pattern, handling isoform suffixes, or filtering reviewed Swiss-Prot vs auto-annotated TrEMBL. Encodes the legacy URL migration (2022), the new JSON schema layout, and bulk-pull patterns.

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Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.

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Build circular genome visualizations using circlize (R), pyCirclize (Python), or Circos (Perl CLI) with ideogram tracks, multi-data tracks (scatter, histogram, heatmap), chord/link arcs for interactions, and explicit circos.clear() between plots. Covers when circular is appropriate vs when Cartesian wins (Cleveland-McGill 1984), karyograms, and chromosome adjacency in chord diagrams. Use when adjacency on the circle conveys meaning — chromosome-level overview, structural variants, Hi-C interactions, cross-genome comparisons.

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Add p-value brackets, significance asterisks, and effect-size annotations to distribution plots using ggpubr, ggsignif, and statannotations with correct test selection (parametric vs non-parametric vs paired), multiple-testing adjustment, and rendering of negative results. Use when a boxplot/violin/raincloud needs in-figure statistical comparisons between groups.

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Detect positive (diversifying / episodic / pervasive) selection using codon dN/dS frameworks. Implements PAML codeml site models (M0/M1a/M2a/M7/M8/M8a), branch models, branch-site model A (Zhang 2005), and HyPhy methods (BUSTED, BUSTED-S, BUSTED-MH, BUSTED-PH, MEME, FEL, FUBAR, aBSREL, SLAC, RELAX, GARD, FUBAR-MH). Includes McDonald-Kreitman framework (asymptotic alpha, impMKT, polyDFE, DFE-alpha, GRAPES) for within-species + divergence inference, RERconverge for trait-correlated rate shifts, CSUBST for convergent substitution, and PhyloAcc for accelerated noncoding evolution. Use when testing adaptive evolution at codons, branches, or full gene; running GARD recombination pre-screen; controlling alignment-error and gBGC false positives; reconciling PAML vs HyPhy results; or performing genome-scale selection scans.

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Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2020 reference-based annotation transfer), Liftover (UCSC), GeMoMa (Keilwagen 2019 evidence-based projection), and Comparative Annotation Toolkit (CAT). Use when transferring annotations from a well-annotated reference to query genome(s), classifying gene-loss vs gene-intact across many genomes at scale, building Zoonomia-style comparative annotations across hundreds of mammals or birds (Kirilenko 2023), detecting pseudogenization, projecting alternative isoforms, or selecting between WGA-anchored (TOGA) vs ortholog-based (LiftOff) annotation transfer strategies.

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Performs 3D shape-based similarity searching using ROCS (OpenEye), USRCAT (ultra-fast), Open3DAlign (RDKit), ESPSim (electrostatic), and ShaEP with explicit handling of Tanimoto-Combo (shape + color), shape vs ECFP4 complementarity, conformer-ensemble searching, alignment optimization, and scaffold hopping. Use when searching for shape-mimicking compounds with different scaffolds, identifying bioisosteric replacements, prospective scaffold hopping, or expanding hit series beyond 2D similarity.

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Identifies super-enhancers from H3K27ac, MED1, or BRD4 ChIP-seq using ROSE, ROSE2, LILY, HOMER -style super, and ENCODE dELS cross-referencing. Handles peak stitching parameters, ranking choices, hockey-stick inflection, marker choice (H3K27ac vs MED1/BRD4), and cross-condition comparison with spike-in normalization. Constructs core regulatory circuitry (Saint-Andre 2016) from SE-encoded TFs. Use when identifying cell-identity / cancer-associated regulatory domains, comparing super-enhancers between conditions, identifying master transcription factor networks, or predicting BET-inhibitor responsiveness.

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Builds QSAR / QSPR models using chemprop D-MPNN, MolFormer, Uni-Mol, ChemBERTa, random forest baselines, and Gaussian processes with explicit handling of OECD 5 principles, applicability domain (kNN, leverage, conformal prediction, Mahalanobis), scaffold-balanced splits, ensemble uncertainty, calibration (Platt, isotonic), feature importance (SHAP, atomic attribution), and prospective validation. Use when building target-specific predictive models from in-house bioassay data, ADMET endpoints, or selectivity profiles.

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Standardizes molecular structures using ChEMBL chembl_structure_pipeline and RDKit rdMolStandardize covering sanitization, salt/solvent stripping, neutralization, tautomer canonicalization, stereochemistry standardization, mixture handling, and isotope normalization. Explicitly compares ChEMBL pipeline, canSARchem, and PubChem standardization choices. Use when preparing libraries for QSAR training, joining datasets across sources, deduplicating compound collections, or building canonical compound registries.

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Quantifies alternative splicing as PSI (percent spliced in) from RNA-seq using rMATS-turbo (BAM-based event), SUPPA2 (TPM-based event), MAJIQ V3 (LSV-based Bayesian), leafcutter (annotation-free intron clusters), VAST-TOOLS (cross-species with microexon support), Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage), or IRFinder-S (intron retention coverage-aware). Distinguishes the five canonical event classes (SE, A5SS, A3SS, MXE, RI), special classes (microexons, exitrons, AFE/ALE), intron retention subtypes (canonical RI vs detained introns), and applies effective-length normalization. Use when measuring splice-site usage or isoform inclusion ratios from short-read RNA-seq.

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Detects aberrant splicing in single rare-disease patients vs a control panel using FRASER 2.0 (Bioconductor; Beta-binomial autoencoder on Intron Jaccard Index, default delta cutoff 0.1, q hyperparameter), OUTRIDER (gene-level outlier expression via autoencoder denoising), LeafcutterMD (Dirichlet-multinomial outlier mode of LeafCutter for annotation-free junctions), and DROP (Snakemake pipeline integrating FRASER2 + OUTRIDER + monoallelic expression for clinical diagnostics). The statistical model is fundamentally different from differential splicing — single-sample-vs-cohort outlier detection rather than two-group comparison. Standard tool in EU rare-disease (Solve-RD) and NIH UDN programs. Use when applying RNA-seq to undiagnosed Mendelian disease, validating predicted splice variants in clinical samples, or detecting cryptic splicing in disease tissue.

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Build a differential-ready consensus peakset from per-replicate ATAC-seq peaks using iterative overlap removal, fixed-width re-centering, and majority-rule overlap. Use when generating a stable peak coordinate system for downstream differential accessibility, ML feature engineering, cross-sample comparison, or fixed-width peak counts; covers Corces 2018 iterative overlap (501 bp), DiffBind summit re-centering, and ENCODE consistency rules.

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Use when a driver or carrier asks about trucker mental health — depression, isolation, stress, fatigue, suicide risk, addiction, EAP programs, or wellness benefits. Covers evidence-based programs, hotlines, carrier-side EAP design, and how mental health affects safety and retention.

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Use this skill when the user asks about truck purchase financing, leasing options, factoring, fuel surcharge math, lease-purchase agreements, the total-cost-of-ownership (TCO) comparison between buying + leasing, and how owner-operators finance their first truck. Cover Section 179 + Bonus Depreciation tax treatment.

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Use this skill when the user asks about CMV operation types — Dedicated, OTR (Over-the-Road), Regional, Local, and the differences in driver lifestyle, pay, equipment, home time, hours, and customer mix. Reference industry segmentation conventions.

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Use this skill when the user asks how to design or improve a fleet safety scorecard — what KPIs to track, how to weight them, telematics signals vs document compliance vs roadside outcomes, monthly cadence, driver-facing vs admin-facing dashboards, gamification considerations, peer-comparison metrics, and how a scorecard ties to insurance premiums + retention. Reference SMS Methodology v3.20 for industry-standard weights.

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Use when discussing the mobile apps drivers use day-to-day — load boards (DAT, Truckstop), route planners (Trucker Path, CoPilot Truck), expense and per diem apps (Per Diem Plus), parking apps (Park My Truck), maintenance / DVIR apps, and how these stack with the carrier's ELD app.

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Use this skill when the user asks about driver fitness-for-duty programs — pre-trip readiness assessment, recognizing impairment from drugs/alcohol/medication/fatigue/mental health, refusing assignments when unfit, supervisor training on fitness assessment, FMCSA recommendations on duty status determination, and how to handle a driver who appears unfit. Reference 49 CFR 392.3 + 392.4.

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End-to-end Coval accent testing workflow. Creates one persona per accent (each using a distinct accent voice and mirroring your Standard Customer behavior), launches one run per accent against the same voice agent + test set + metrics, polls for completion, builds a per-persona comparison table from the results, and creates the saved multi-run report (grouped by Persona) via the public API. Use when a user wants to follow the Testing Across Accents cookbook (https://docs.coval.dev/guides/testing-across-accents) without doing each step by hand.

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Maps intelligence for local SEO: geo-grid rank tracking, GBP profile auditing via API, review intelligence across Google/Tripadvisor/Trustpilot, cross-platform NAP verification, competitor radius mapping, and LocalBusiness schema generation. Three tiers: free (Overpass + Geoapify), DataForSEO, and DataForSEO + Google. Use when user says "maps", "geo-grid", "rank tracking", "GBP audit", "review velocity", "competitor radius", or "SoLV".

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Builds reusable Convex components with isolated tables and app-facing APIs. Use for new components, reusable backend modules, integrations, or component boundary work.

日本語の概要は準備中です。原文の説明を表示しています。