2,637 件 ・ 関連度順
概要と使いどころ
Optimizes API performance through payload reduction, caching strategies, and compression techniques. Use when improving API response times, reducing bandwidth usage, or implementing efficient caching.
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。
This skill should be used when the user asks about "bun build", "Bun.build", "bundling with Bun", "code splitting", "tree shaking", "minification", "sourcemaps", "bundle optimization", "esbuild alternative", "building for production", "bundling TypeScript", "bundling for browser", "bundling for Node", or JavaScript/TypeScript bundling with Bun.
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。
Write compelling, persuasive marketing and sales copy using proven frameworks like AIDA, PAS, BAB, and 4Ps, with tone and voice customization, CTA optimization, and A/B variant generation. Use when the user requests copywriting or provides relevant inputs for this workflow.
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。
Optimize machine learning model hyperparameters using grid search, random search, Bayesian optimization, and Hyperband to maximize model performance within a compute budget. Use when the user requests hyperparameter tuning or provides relevant inputs for this workflow.
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。
Compress selected context to a target token budget while preserving decisions, evidence, constraints, and unresolved questions. Use when relevant material is already selected but too long; use context-optimization when selection, deduplication, and ordering are also required.
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。
Optimize a complete candidate context package by deduplicating, filtering, ordering, and allocating its token budget. Use when retrieved or assembled material is noisy or exceeds the useful context budget; use context-ranking for scoring chunks and context-compression for shrinking selected content.
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。
Diagnose and optimize existing slow SQL queries using execution plans, indexing strategies, query rewriting, and ORM tuning. Use when the user provides a query, performance symptom, or EXPLAIN plan; use sql-query-generation when creating a new query from requirements.
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。
Place trusted contextual information into prompts or agent state using explicit boundaries, provenance, and templates. Use when relevant context has already been selected and must be inserted safely; use context-retrieval to find it or context-optimization to choose and order it.
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 SQL queries from natural-language requirements using SELECT, JOIN, GROUP BY, window functions, CTEs, and subqueries. Use when the user needs a new query from a business question or schema; use query-optimization when an existing query or execution plan is slow.
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。
Think and work like an expert Medicinal Chemist. Use when a task calls for Medicinal Chemist judgment. Reasons from structure-activity relationships, lipophilicity and unbound-fraction physicochemistry, synthetic accessibility, and target-product-profile multiparameter optimization through LLE/Fsp3/QED scoring, FEP+/Glide docking validated against co-crystal and SPR data, ELN-tracked LC-MS/NMR synthesis, and DMPK panels (microsomal CL, Caco-2 efflux, hERG, CYP) while treating PAINS and aggregation assay artifacts, biochemical-versus-cellular potency gaps, reactive-metabolite soft spots, and freedom-to-operate cliffs as first-class failure modes.
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。
Think and work like an expert Industrial Microbiologist. Use when a task calls for Industrial Microbiologist judgment. Reasons from SmF/SSF physiology, fed-batch μ/OTR–OUR/RQ, DoE media optimization, PAT (Raman soft sensors), ICH Q8/Q7 characterization, bioleaching, SVI/F/M filament ID, and phage (10⁴–10⁶ PFU/mL) plant hygiene; treats antifoam kLa penalty, F₀ CIP/SIP cold spots, DSP mass balance, and golden-batch vs biofilm red herrings as first-class failure modes.
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。
Think and work like an expert Fermentation Scientist. Use when a task calls for Fermentation Scientist judgment. Reasons from Monod–Luedeking–Piret kinetics, overflow μcrit, OTR/RQ/RAMOS analytics, DoE media optimization, and 13C-MFA/COBRApy flux bounds while treating stuck-ferment ethanol×T synergy, SSF heat/moisture gradients, and OD-as-biomass red herrings as first-class failure modes.
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。
Think and work like an expert Database Systems Researcher. Use when a task calls for Database Systems Researcher judgment. Reasons from storage hierarchy, concurrency semantics, query-optimization theory, and declared workload models through TPC-C/H, YCSB, and JOB benchmarks, Jepsen/Elle correctness checkers, and perf/blktrace/fio profiling while treating cardinality-estimation plan regressions, tail-latency spikes under skew, unfair fsync-disabled speedups, and benchmark-trick wins as first-class failure modes.
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。
Think and work like an expert Computational Neuroscientist. Use when a task calls for Computational Neuroscientist judgment. Reasons from encoding/decoding, GLM/LNP spike-train likelihood, mean-field E-I balance, and neural manifolds through NEST/Brian/NEURON/BMTK, GPFA/LFADS, Brain-Score alignment, and trained-RNN reverse engineering while treating spike-sorting contamination, model non-identifiability, nested-CV leakage, and task-optimization≠mechanism as first-class failure modes.
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。
Think and work like an expert AI Researcher. Use when a task calls for AI Researcher judgment. Reasons from data generating processes, inductive biases, and compute-data-algorithm trade-offs through train/val/test discipline, seed sweeps, ablation ladders, and standards like NeurIPS reproducibility checklists, model cards, and lm-eval-harness, while treating data leakage (Kapoor & Narayanan taxonomy), benchmark contamination, spurious correlations, and reward-model overoptimization as first-class failure modes.
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。
当系统提示面向移动端（手机、平板）场景时调用。适用于 iOS/Android 应用内 AI 助手、移动端聊天界面、响应式输出的系统提示设计。不适用于桌面端优先的场景，不适用于移动端 UI 开发（非提示层），不适用于语音场景（语音场景使用 voice-optimization）。
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。
当系统提示面向语音交互场景（语音助手、语音搜索、有声回答、电话客服 AI）时调用。适用于需要将文本输出优化为口语表达的系统提示设计。不适用于纯文本聊天界面，不适用于语音合成（TTS）技术选型，不适用于图像/视频多模态场景。
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。
Technical execution timing, entry exit signals, price action analysis, volume profile, market structure, support resistance levels, trend identification, Wyckoff method, auction market theory, trade management timing, position entry exit optimization
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。
Production guidance for assessing, exporting, packaging, integrating, capturing, validating, and handing off Lottie vector animations. Use for Bodymovin/Lottie JSON, dotLottie archives, renderer and player compatibility, fonts and glyphs, image assets, markers and segments, deterministic video capture, responsive sizing, optimization, accessibility, and cross-platform QA; not for general After Effects animation craft or live-action/video delivery.
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 to turn generated, captured, scanned, or modeled 3D output into production-ready standalone assets for DCC, real-time engine, web, or interchange delivery. It covers requirements, topology repair, scale and axes, UVs, PBR baking, LODs, static and skeletal readiness, rig handoff, optimization, glTF/USD/FBX delivery, validation, QA, and rights/provenance. Do not use it for provider-specific generation prompts, full scene/world building, mocap operation, or exact CAD/manufacturing 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。
Resolve the architecture of focal oncogene amplifications — extrachromosomal DNA (ecDNA), breakage-fusion-bridge (BFB) cycles, homogeneously staining regions (HSR), and linear amplification — from whole-genome sequencing with AmpliconArchitect, the AmpliconSuite pipeline, and AmpliconClassifier. Covers copy-number seed selection, breakpoint-graph reconstruction, balanced-flow optimization, ecDNA classification, and the limits of depth-only amplification calls. Use when a focal amplification needs structural characterization, when distinguishing ecDNA from chromosomal amplification, suspecting ecDNA-driven oncogene amplification or therapy resistance, or selecting copy-number seeds for amplicon reconstruction.
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 alchemical free-energy calculations including relative binding free energy (RBFE / FEP+) and absolute binding free energy (ABFE) via OpenFE, FEP+, GROMACS, AMBER pmemd, and OpenMM with explicit lambda window scheduling, soft-core potentials, REST2 enhanced sampling, MBAR/BAR analysis, and cycle closure validation. Compares ML alternatives (Boltz-2 affinity, DeepDock). Use when ranking analogs by binding affinity beyond docking accuracy, performing prospective lead optimization, or validating SAR predictions.
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。
Generates 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.
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。
Analyze codon usage, calculate CAI (Codon Adaptation Index), and examine synonymous codon bias using Biopython. Use when analyzing coding sequences for expression optimization or evolutionary analysis.
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。