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Designs QC, corrects signal drift, removes batch effects, filters features, normalizes samples, and imputes missing values for untargeted LC-MS/GC-MS metabolomics, framing each step as a measurement model that can create or erase biological signal. Use when processing a peak/feature table before statistical analysis, choosing a drift-correction or sample-normalization method, deciding QC RSD vs D-ratio filtering, or handling left-censored missing values. The feature table is produced by metabolomics/xcms-preprocessing or metabolomics/msdial-preprocessing; transformation/scaling for modeling defers to metabolomics/statistical-analysis; cross-study design issues link to experimental-design/batch-design.

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Turns untargeted LC-MS/MS features (m/z, RT, MS/MS) into confidence-stratified metabolite annotations using spectral-library matching (matchms), in-silico tools (SIRIUS/CSI:FingerID, MetFrag) and molecular networking, and assigns a defensible MSI/Schymanski confidence level to each. Use when naming detected features, scoring MS/MS against a reference library, running SIRIUS, or deciding what confidence level an evidence set actually supports. For upstream feature extraction see metabolomics/xcms-preprocessing and metabolomics/msdial-preprocessing; for downstream enrichment that must respect these levels see metabolomics/pathway-mapping; for lipid-specific structural annotation see metabolomics/lipidomics.

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Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al 2022/2023 J Chem Inf Model 63:147-158), Pharmer / Pharmit (search), and PharmacoForge (diffusion-based generation, Flynn et al 2025 Front Bioinform), covering ligand-based pharmacophore (from active set alignment) and receptor-based pharmacophore (from binding pocket geometry). Explicit handling of feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.

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Maps metabolomics results to biological pathways via over-representation (ORA), metabolite-set enrichment (MSEA/QEA), mummichog/PSEA on raw m/z peaks, and network-diffusion enrichment (FELLA), with correct background-set construction and honest interpretive ceilings. Use when interpreting differential metabolites or an untargeted LC-MS feature table in pathway context, choosing ORA vs MSEA vs mummichog vs topology, or setting the reference/background set. For annotation confidence levels feeding ORA see metabolomics/metabolite-annotation; for gene-set concepts see pathway-analysis/go-enrichment and pathway-analysis/gsea; for joint gene+metabolite pathways see multi-omics-integration/mofa-integration.

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Calculates molecular fingerprints (ECFP/Morgan, FCFP, MACCS, RDKit, AtomPair, TopologicalTorsion, Avalon, MAP4, MHFP6) and physicochemical descriptors (Lipinski, QED, TPSA, Crippen LogP, 3D shape) with explicit choice tables, bit vs count semantics, and partial-charge model selection. Use when featurizing molecules for similarity, QSAR, virtual screening, or ML, or selecting the correct fingerprint for a chemotype-aware task.

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Designs novel molecules using REINVENT 4 (de novo, scaffold decoration, linker design, R-group, molecular optimization), MolMIM, Diffusion-based generators (DiGress, DiffSMol), and JT-VAE with explicit handling of multi-parameter optimization (MPO), goal-directed scoring functions, transfer/reinforcement/curriculum learning, synthetic accessibility scoring, and chemical space exploration vs exploitation. Use when designing new chemical matter against a target, decorating a scaffold, linking fragments, or optimizing a hit for multiple ADMET / activity properties simultaneously.

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Package a GNOME app as a Flatpak — manifest for the GNOME runtime, sandbox permissions, build/run/test loop, and Flathub submission checklist. Use when packaging, distributing, or preparing a GNOME app for Flathub.

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Make a GTK4/libadwaita app accessible — accessible labels and roles, tooltips on icon-only buttons, keyboard navigation, contrast, and testing with Orca/Accerciser. Use when fixing a11y issues or running the accessibility category of a HIG audit.

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Audit a GTK4/libadwaita app for GNOME HIG compliance across 12 categories (text, icons, layout, shortcuts, accessibility, color, dialogs, responsiveness, CSS, GSettings, i18n), then fix violations category by category. Use when asked to audit, review, polish, or "make HIG-compliant" a GNOME app, or before a release/Flathub submission.

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Implement GNOME's 4-tier destructive-action pattern: undo toasts instead of confirmation dialogs, AdwAlertDialog for irreversible actions. Use when adding delete, remove, clear, or other destructive actions.

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Flat page switching with AdwViewStack + AdwViewSwitcher (3-5 top-level views) and ViewSwitcherBar on narrow widths. Use when an app needs tabs/views in the header bar.

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Add a standard AdwAboutDialog wired to app metadata, plus the AppStream metainfo file it should share data with. Use when adding an About entry, release notes, credits, or preparing app metadata.

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Exclusive-choice UI: CheckButton radio groups in list rows, when to use them vs ComboRow vs ToggleGroup. Use when presenting mutually exclusive options in GNOME preferences or dialogs.

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Scaffold a minimal GNOME-compliant GTK4/libadwaita app from scratch — window, header bar, ViewSwitcher, adaptive breakpoint, menu, preferences, GSettings schema, desktop entry, meson. Use when starting a new GNOME app or when the user asks for a GNOME app template.

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Authoritative resource for building, auditing, and improving GNOME GTK4/libadwaita apps. Design tokens, 42-widget component library, 21 pattern skills, 47 mirrored HIG pages, 34 real-app audits, and lint scripts. Use when building a GNOME app, adding GNOME GUI features, auditing HIG compliance, packaging for Flatpak/Flathub, or when the user mentions GTK, libadwaita, Adw, Blueprint, or GNOME HIG.

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Extract reusable components, product patterns, and design tokens into a clearer design system with shared APIs, documented defaults, and better reuse. Use when the user wants to refactor repeated UI, consolidate buttons/cards/forms/sections, build a component library, or turn one-off values into reusable tokens.

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Add or refine depth through elevation systems, raised and inset surfaces, layered overlap, and meaningful shadow behavior so interfaces feel structured instead of flat. Use when the user mentions depth, elevation, shadows, layering, flat cards, pressed states, drag feedback, or inset controls.

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。

Plan, implement, or improve an internationalization and localization strategy for UI content, formatting, and regional adaptation. Use when the user asks to add i18n, localize, translate, support multiple languages, handle regional formats, manage locale switching, or build a multilingual product.

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。

Design, structure, or improve form interfaces for clarity, completion rates, and user confidence. Use when the user asks to build a form, redesign a form flow, improve form conversion, add multi-step forms, fix form UX, or structure field layouts and validation.

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。

Design focused empty states for zero-data, no-results, permission, and error situations with clear value framing, strong CTAs, and less dead chrome. Use when the user mentions blank pages, zero-data screens, no results, permission states, or dead controls—not broader onboarding strategy.

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。

Implement or refine UI motion, gestures, and transitions. Use when the user asks for animation, hover or press motion, drag or swipe behavior, transition timing, or reduced-motion alternatives. Mentioning a drawer, toast, or hover state alone does not request animation.

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Improve UX writing, marketing copy, labels, microcopy, instructions, and error messages so interfaces and product messaging are easier to understand and act on. Use when the user mentions confusing copy, unclear labels, bad error text, weak CTAs, hard-to-follow instructions, or wanting better UX writing.

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Systematically audit and remediate accessibility issues in UI, focusing on keyboard navigation, screen reader support, color contrast, semantic HTML, ARIA usage, and motion sensitivity. Use when the user wants to improve accessibility, make a component accessible, fix WCAG violations, add keyboard support, or ensure screen reader compatibility.

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。

Create distinctive React and Tailwind interfaces with strong hierarchy and polished implementation. Use when building or redesigning pages, flows, or components, or when another better-react-web-ui skill needs shared design guidance. Pair with add-ui for direct implementation or requested alternatives.

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。