「screen」の検索結果

Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski rule of five, Veber, Oprea, CNS, lead-like, rule of three), structural alert catalogs (PAINS a/b/c, NIBR screening-deck severity, Brenk, BMS, Glaxo, Dundee, ChEMBL common alerts), ZINC-15 percentile complexity metrics (Bertz, SAscore, QED, Whitlock, Barone), chemical-group detection, Lilly demerits, and the medchem query language (MATCHRULE, HASALERT, HASPROP, HASGROUP) for filtering a library at scale. Also trigger on medchem, `import medchem as mc`, RuleFilters, NIBRFilters, CommonAlertsFilters, NamedCatalogs, QueryFilter, PAINS filtering, or structural alerts.

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 small interfering RNA and antisense oligonucleotide sequences against a transcript, and screen them for the failure modes specific to nucleic-acid drugs. Use this skill to tile a target transcript, apply positional and thermodynamic selection rules including duplex asymmetry and nearest-neighbour melting temperature, scan candidates for seed-region complementarity to off-target transcripts, and lay out a chemical modification pattern — gapmer architecture, 2'-O-methyl and 2'-MOE wings, locked nucleic acid, and phosphorothioate placement. Also trigger on siRNA, antisense oligonucleotide, ASO, gapmer, RNase H, seed region, duplex asymmetry, 2'-MOE, locked nucleic acid, phosphorothioate, or GalNAc conjugate.

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。

How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

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。

Molecular featurization hub with one consistent interface over 100+ featurizers. Fingerprints (ECFP/Morgan, MACCS, atom pair, topological torsion, Avalon, RDKit, ERG), RDKit and Mordred descriptor sets, pharmacophore and 3D shape descriptors, scaffold keys, and pretrained embeddings (ChemBERTa, ChemGPT, MolT5, GIN, Graphormer) through a common transformer API with caching and parallelism. Use this skill to convert SMILES into model-ready feature matrices for QSAR, virtual screening, and molecular ML, and to choose between featurizer families. Also trigger on molfeat, MoleculeTransformer, FPVecTransformer, PretrainedHFTransformer, molfeat model store, or featurizer selection.

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。

Structure-based docking with AutoDock Vina, Vinardo, and AutoDock4 through the Meeko toolchain. Use this skill to define a docking box, prepare receptors and ligands as PDBQT, run single or batch docking, rescore, and interpret affinities, poses, and ligand efficiency. Covers box definition from a reference ligand or pocket residues, protonation and tautomer decisions, flexible side chains, exhaustiveness and seeds, redocking validation, and virtual screening over compound libraries. Also trigger on vina, smina, gnina, mk_prepare_ligand, mk_prepare_receptor, mk_export, scrub.py, PDBQT, autogrid4, docking box, or binding-pose prediction.

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。

Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.

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。

DiffDock and DiffDock-L diffusion-based molecular docking. Use for blind protein-small-molecule pose prediction from a PDB file or sequence plus SMILES/SDF/MOL2, batch docking over a CSV of complexes, virtual screening triage, sampling multiple poses per complex, and reading the confidence score correctly. Also trigger on DiffDock, DiffDock-L, inference.py, confidence_model, samples_per_complex, ESM embedding preparation for docking, or blind docking without a defined box. Not for binding affinity prediction — the confidence score ranks pose plausibility, not potency.

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。

Cofold protein-ligand, protein-protein, and nucleic-acid complexes with Boltz-2, and predict binding affinity with its trained affinity head. Use this skill to build Boltz input YAML, run structure prediction with MSAs, pocket constraints, templates, and modified residues, screen compound libraries by cofolding, and interpret confidence scores (pLDDT, pTM, ipTM, PDE) and affinity output (binder probability and log10 IC50). Also trigger on Boltz, Boltz-1, Boltz-2, cofolding, boltz predict, affinity_pred_value, affinity_probability_binary, ipTM, or open-weights AlphaFold3 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。

Boot and drive the tbh-meter app + reader locally to see a change working — what runs on macOS (UI only; the reader is win32-only) and how to feed the overlay/list fake data. Use when asked to run/start the meter, verify a change in the running app, screenshot the overlay, or reproduce a bug locally.

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。

Standardized commit + PR workflow for tbh-meter. Analyzes changes, makes conventional commits (subjects feed the release changelog), and for any overlay/UI change captures a seed-driven screenshot to attach to the PR. Use when asked to commit, open a PR, or "commit this".

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。

Writes and reviews Swift App Intents code that exposes app actions and data to Siri, Shortcuts, Spotlight, widgets, Control Center, and Apple Intelligence. Use when adding AppIntent, AppEntity, OpenIntent, AppShortcutsProvider, EntityQuery, Focus Filters, AssistantEntity/AssistantIntent schemas, on-screen awareness, LongRunningIntent/CancellableIntent, EntityCollection, SyncableEntity, AppIntentsTesting, or when wiring SwiftData/networked data into intents.

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。

Timebox and coordinate a live three-lane iOS launch sprint from audience idea to tested SwiftUI app, landing page, ASO copy, screenshots, promo video, and privacy report. Use for a 50-minute talk, demo rehearsal, fallback drill, or any request to finish a tiny local iOS product under a hard deadline.

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。

Render a deterministic 15-second vertical promo video from real Simulator screenshots with a three-beat viewer challenge and no Remotion initialization. Use in a twelve-minute parallel launch lane, as a reliable fallback to a richer Claude Design animation, or whenever ffmpeg and ImageMagick are available.

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。

Pressure-test and freeze a tiny iOS MVP in seven minutes using one persona, one explicit devil's-advocate round, scope cuts, and a three-screen flow. Use when an audience supplies an app idea, when requirements must be settled live, or when a concept needs a fast implementation-risk check before Claude Design or Claude Code.

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。

Generate or review a Claude Design brief and convert its output into a deterministic SwiftUI implementation handoff with native iOS layout, tokens, states, transitions, and accessibility rules. Use for Claude Design mobile prototypes, three-screen iOS concepts, Design-to-Claude-Code handoffs, or when a visual prototype must be buildable without interpretation.

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。

Route a 50-minute live iOS launch sprint across the lightweight requirements, Claude Design handoff, SwiftUI implementation, landing page, ASO, Simulator screenshot, promo-video, and privacy-scan skills bundled in this plugin. Use as the main entry point when the whole talk workflow should be planned or executed.

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。

Automate App Store screenshot capture across devices and languages

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。

Review user experience and interface design using Nielsen's heuristics, WCAG 2.1 accessibility guidelines, keyboard and screen reader audit, user flow analysis, cognitive load assessment, and form usability evaluation. Use when conducting a usability review before release, assessing WCAG 2.1 accessibility compliance, evaluating user flows for efficiency, reviewing form design, or performing a heuristic evaluation of an existing interface.

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。

Grade a trading card using PSA, BGS, or CGC standards. Covers observation-first assessment (adapted from meditate's unbiased observation), centering measurement, surface analysis, edge and corner evaluation, and final grade assignment with confidence interval. Supports Pokemon, MTG, Flesh and Blood, and Kayou cards. Use when evaluating a card before professional grading submission, pre-screening a collection for high-grade candidates, settling condition disputes between buyers and sellers, or estimating the grade-dependent value spread for a card.

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 Shiny application UIs using bslib for theming, layout_columns for responsive grids, value boxes, cards, and custom CSS/SCSS. Covers page layouts, accessibility, and brand consistency. Use when building a new Shiny app UI from scratch, modernizing an existing app from fluidPage to bslib, applying brand theming, making a Shiny app responsive across screen sizes, or improving accessibility of a Shiny application.

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。

Trademark filing procedures covering EUIPO (EU), USPTO (US), and WIPO Madrid Protocol (international). Walks through pre-filing conflict checks, Nice classification, descriptiveness assessment, mark type decisions, filing basis strategy, office-specific e-filing procedures, Madrid Protocol extension, post- filing monitoring, and open-source trademark policy drafting. Use after running screen-trademark to confirm the mark is clear, when ready to secure trademark rights in one or more jurisdictions.

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。

Appraise gemstone value using the four Cs (color, clarity, cut, carat), origin assessment, treatment detection, and market factor analysis. Advisory educational guidance only — not a certified appraisal. Use when understanding factors that determine a gemstone's value, pre-screening stones before a professional appraisal, evaluating whether a seller's asking price is reasonable, learning gemstone grading methodology, or understanding how treatment status affects value.

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。

Map the intellectual property landscape for a technology domain or product area. Covers patent cluster analysis, white space identification, competitor IP portfolio assessment, freedom-to-operate preliminary screening, and strategic IP positioning recommendations. Use before starting R&D in a new technology area, when evaluating market entry against incumbents with strong patent portfolios, preparing for investment due diligence, informing a patent filing strategy, or assessing freedom-to-operate risk for a new 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。

State management and storage choice in React Native. State libs work mostly the same as web (Zustand, Jotai, Redux Toolkit, TanStack Query, Context); storage choice is RN-specific — AsyncStorage, MMKV, SecureStore, Keychain. Hydration patterns, splash screen handling, secrets discipline. Use this skill to: - Pick state management lib (matches web React intuition). - Pick storage by sensitivity (AsyncStorage / MMKV / SecureStore / Keychain). - Hydrate state on app start without UI flicker. - Persist Zustand store across app restarts. - Avoid storing secrets in plaintext storage. Do NOT use this skill for: - General project conventions (see rn-conventions). - Platform-specific code (see rn-platform-specific). - Navigation (see rn-navigation). - Testing state/storage (see rn-testing).

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。