「forms」の検索結果

Performs retrosynthetic planning using AiZynthFinder (MCTS, template-based), Chemformer (template-free transformer), ASKCOS, and emerging RetroSynFormer with explicit handling of route scoring, building-block availability (eMolecules, Enamine, Mcule), forward prediction validation (Molecular Transformer), and disconnection-aware multi-objective search (MO-MCTS). Use when assessing synthetic feasibility of generated or selected molecules, planning multi-step syntheses, building synthesis-aware design pipelines, or screening libraries for retro-route feasibility.

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。

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.

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。

Performs gene-level association from GWAS summary statistics via genetically predicted tissue expression using FUSION, PrediXcan, S-PrediXcan, S-MultiXcan, UTMOST, MOSTWAS, kTWAS, EpiXcan, TIGAR-V2, and probabilistic fine-mapping with FOCUS and MA-FOCUS. Use when running TWAS from GWAS sumstats, prioritising candidate causal genes from a GWAS lead locus, picking single-tissue vs cross-tissue models, identifying LD-induced TWAS false positives, choosing ancestry-matched prediction weights, fine-mapping co-regulated TWAS hits, or triangulating TWAS with cis-eQTL Mendelian randomization and colocalization to nominate a causal gene.

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。

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.

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。

Performs logistic regression for clinical trial outcomes (binary, ordinal, multinomial) with marginal-vs-conditional estimand reporting per FDA 2023 covariate adjustment guidance, g-computation/standardisation for marginal effects, modified Poisson for RR, Brant test for proportional odds, Firth penalty for separation, and Hauck-Donner detection. Use when modeling binary or ordinal endpoints in confirmatory or exploratory clinical trials.

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。

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

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。

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.

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。

Analyzes differential transcript usage (DTU) and isoform switches with functional consequence prediction (NMD via 50nt rule, ORF disruption, protein domain loss/gain, signal peptide changes, IDR alterations, coding-potential shifts). Tools include IsoformSwitchAnalyzeR v2 (auto-selects satuRn for >5 reps else DEXSeq), the manual DRIMSeq -> DEXSeq/satuRn -> stageR DTU pipeline, and fishpond/swish for inferential-uncertainty-aware DTE. Distinguishes DTU from DGE and DTE; integrates external annotators (CPC2, Pfam, SignalP, IUPred2A or DeepTMHMM). Use when investigating how splicing differences alter protein function or trigger NMD-mediated degradation.

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。

Use when a carrier is evaluating ELD / telematics / dashcam platforms and needs an objective comparison of Samsara, Motive (formerly KeepTruckin), and Geotab. Covers ELD compliance, AI dashcams, driver scoring, integrations, pricing structure, and which fleet profile suits which platform.

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。

Use this skill when the user asks about International Fuel Tax Agreement (IFTA) compliance — quarterly returns, jurisdiction reporting, fuel + miles reconciliation, IFTA-100/101 forms, base jurisdiction selection, IFTA license + decals, recordkeeping requirements, common IFTA audit findings, or how to handle non-IFTA jurisdictions. Cite IFTA Articles of Agreement.

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。

Use this skill when the user asks about the FMCSA Drug & Alcohol Clearinghouse — full vs limited query, pre-employment vs annual query, query credit purchasing, who has access (employers, drivers, MROs, SAPs, state licensing agencies), reporting violations to the Clearinghouse, the "prohibited" status workflow, consent forms, or how to register/use the clearinghouse.fmcsa.dot.gov portal. Cite 49 CFR 382.701-727.

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。

Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

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。

Locate CCSPlayer_WeaponServices::DropWeapon in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the WeaponPaints gamedata entry "CCSPlayer_WeaponServices::DropWeapon" (symbol CCSPlayer_WeaponServices_DropWeapon). This is a VIRTUAL function — emit the vtable slot, NOT a byte signature. Resolve the CCSPlayer_WeaponServices vtable via RTTI and identify the DropWeapon slot: the body takes a CBasePlayerWeapon handle/pointer, detaches it from the player, spawns the dropped weapon entity and transfers ownership. Verify by xrefs from weapon-drop commands (!!drop) and pickup logic. vtable slots commonly differ between platforms — never assume identical indices. Trigger: CCSPlayer_WeaponServices_DropWeapon, CCSPlayer_WeaponServices::DropWeapon

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。

Find and identify the CCSPlayerController_Respawn virtual function in CS2 binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the Respawn vfunc slot by RTTI-walking the CCSPlayerController vtable and confirming the candidate's decompiled body performs a full controller-state reset (networked flags, per-round fields, comeback/damage-info bookkeeping) rather than any other lifecycle vfunc. Trigger: CCSPlayerController_Respawn

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。

Locate CBasePlayerController::FakeClientFlags in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "CBasePlayerController::FakeClientFlags" (symbol CBasePlayerController_FakeClientFlags). This is a VIRTUAL function - resolve the CBasePlayerController::FakeClientFlags vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: CBasePlayerController_FakeClientFlags, CBasePlayerController::FakeClientFlags

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。

Locate BotProfile::Teamwork in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::Teamwork" (symbol BotProfile_Teamwork). This is a VIRTUAL function - resolve the BotProfile::Teamwork vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_Teamwork, BotProfile::Teamwork

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。

Locate BotProfile::LookAngleDampingAttacking in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::LookAngleDampingAttacking" (symbol BotProfile_LookAngleDampingAttacking). This is a VIRTUAL function - resolve the BotProfile::LookAngleDampingAttacking vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_LookAngleDampingAttacking, BotProfile::LookAngleDampingAttacking

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。

Locate BotProfile::LookAngleMaxAccelAttacking in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::LookAngleMaxAccelAttacking" (symbol BotProfile_LookAngleMaxAccelAttacking). This is a VIRTUAL function - resolve the BotProfile::LookAngleMaxAccelAttacking vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_LookAngleMaxAccelAttacking, BotProfile::LookAngleMaxAccelAttacking

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。

Locate BotProfile::Aggression in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::Aggression" (symbol BotProfile_Aggression). This is a VIRTUAL function - resolve the BotProfile::Aggression vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_Aggression, BotProfile::Aggression

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。

Locate BotProfile::LookAngleStiffnessAttacking in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::LookAngleStiffnessAttacking" (symbol BotProfile_LookAngleStiffnessAttacking). This is a VIRTUAL function - resolve the BotProfile::LookAngleStiffnessAttacking vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_LookAngleStiffnessAttacking, BotProfile::LookAngleStiffnessAttacking

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。

Locate BotProfile::Difficulty in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::Difficulty" (symbol BotProfile_Difficulty). This is a VIRTUAL function - resolve the BotProfile::Difficulty vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_Difficulty, BotProfile::Difficulty

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。

Locate BotProfile::AttackDelay in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::AttackDelay" (symbol BotProfile_AttackDelay). This is a VIRTUAL function - resolve the BotProfile::AttackDelay vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_AttackDelay, BotProfile::AttackDelay

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。

Locate BotProfile::Cost in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::Cost" (symbol BotProfile_Cost). This is a VIRTUAL function - resolve the BotProfile::Cost vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_Cost, BotProfile::Cost

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。

Locate BotProfile::WeaponPrefCount in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::WeaponPrefCount" (symbol BotProfile_WeaponPrefCount). This is a VIRTUAL function - resolve the BotProfile::WeaponPrefCount vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_WeaponPrefCount, BotProfile::WeaponPrefCount

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。