'scaffold' 검색 결과

Interactively create a new Grok skill (SKILL.md + optional scripts/references). Use when the user wants to create a skill, scaffold a skill, or runs /create-skill.

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Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.

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Create data-driven presentation slides using React, Vite, and Recharts with Sentry branding. Use when asked to "create a presentation", "build slides", "make a deck", "create a data presentation", "build a Sentry presentation". Scaffolds a complete slide-based app with charts, animations, and single-file HTML output.

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Orchestrates an end-to-end de novo genome assembly project, routing each step to the right genome-assembly skill rather than restating it. Profiles the genome first (k-mer spectrum -> size, heterozygosity, ploidy), QCs reads, chooses an assembly path by data type (SPAdes for Illumina, Flye for noisy long reads, hifiasm for HiFi, metaFlye for communities), polishes only when needed, decontaminates, scaffolds with Hi-C, and finishes with three-axis QC (contiguity + completeness + correctness). Use when assembling a genome from raw reads and deciding which assembler, whether to polish, and how to prove the result is good.

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Searches molecular libraries for substructure matches using SMARTS patterns with explicit handling of recursive SMARTS, ring membership, aromaticity dialect, vector binding, atom map indices, and reactive/PAINS/REOS/Brenk/Aldridge filter catalogs. Use when filtering compounds by pharmacophore features, functional groups, scaffold matches, or screening for assay-interference / structural alerts.

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Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.

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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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Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.

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Publication-quality molecular visualization. 2D structure drawings (PNG/SVG), molecule grids with property annotations, scaffold highlighting, protein-ligand interaction diagrams, and interactive 3D views.

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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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Performs molecular similarity searching using Tanimoto, Tversky, Dice, and cosine coefficients on bit/count fingerprints with explicit choice rules for symmetric vs asymmetric measures, scaffold-hopping vs lead-optimization regimes, activity-cliff diagnosis, and large-library nearest-neighbor methods (BulkTanimoto, Annoy MHFP6, USRCAT). Use when ranking compounds by structural resemblance to a query, clustering libraries, finding analogs, or diagnosing activity cliffs.

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De novo molecule generation for drug discovery. Scaffold-based analog enumeration, fragment growing/linking, structure-based design, multi-objective optimization, and drug-likeness filtering.

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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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Continues Charted Coding work for a PR by routing to scaffold, red, or green based on design doc progress. Use when resuming work on a PR, continuing TDD after a break, or when the user invokes charted continue with a design doc and PR number.

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Writes work-in-progress code and tests based on design doc

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Android Jetpack Compose (Kotlin) の androidx Material3 コンポーネントリファレンス。 Button, IconButton, FloatingActionButton, Checkbox, Switch, Slider, TextField, SearchBar, DropdownMenu, Chip, DatePicker, TimePicker, Card, Scaffold, Dialog, BottomSheet, TopAppBar, NavigationBar, NavigationRail, Tab, Snackbar, ProgressIndicator, Badge, Icon, MaterialTheme, ColorScheme, Typography, Shapes。

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Use when building PHP applications with modern PHP 8.3+ features, Laravel, or Symfony frameworks. Invokes strict typing, PHPStan level 9, async patterns with Swoole, and PSR standards. Creates controllers, configures middleware, generates migrations, writes PHPUnit/Pest tests, defines typed DTOs and value objects, sets up dependency injection, and scaffolds REST/GraphQL APIs. Use when working with Eloquent, Doctrine, Composer, Psalm, ReactPHP, or any PHP API development.

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Write or fix a test in the Lyrion Dashboard's suite. Use this whenever a task adds a test, changes one, or asks why the suite fails — anything touching `tests/`. Covers the standalone-per-file scaffolding, the env-before-import rule, where to patch (the route/service the code under test imports), the cache-and-clock testing pattern, and how to run one test vs the whole suite. Reach for it any time you touch `tests/` so new tests match the existing shape instead of re-inventing setup.

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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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.

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Searches molecular libraries for substructure matches using SMARTS patterns with explicit handling of recursive SMARTS, ring membership, aromaticity dialect, vector binding, atom map indices, and reactive/PAINS/REOS/Brenk/Aldridge filter catalogs. Use when filtering compounds by pharmacophore features, functional groups, scaffold matches, or screening for assay-interference / structural alerts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Performs molecular similarity searching using Tanimoto, Tversky, Dice, and cosine coefficients on bit/count fingerprints with explicit choice rules for symmetric vs asymmetric measures, scaffold-hopping vs lead-optimization regimes, activity-cliff diagnosis, and large-library nearest-neighbor methods (BulkTanimoto, Annoy MHFP6, USRCAT). Use when ranking compounds by structural resemblance to a query, clustering libraries, finding analogs, or diagnosing activity cliffs.

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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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Create new OpenCode skills with the standard scaffold.

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