1,471 件 ・ 関連度順
概要と使いどころ
Guide annuity and life-insurance sales workflows—discovery, suitability, product selection, objections, closing, and servicing—when supporting licensed insurance sales conversations or deliverables.
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 a mock server for an API — tooling selection, response fixtures, and error scenarios. Use when asked to "build a mock server", "mock this API for development", or "create response fixtures".
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 a multi-cloud or cloud portability strategy — provider selection, workload placement, and lock-in management. Use when asked "should we go multi-cloud", "design for cloud portability", or "where should this workload run".
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 asked to select chart types for analytics dashboards, choose BI visualizations, or design data displays. Examples: "best chart for sales data", "dashboard visualization for metrics", "analytics chart 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。
Design a service mesh deployment — technology selection, mTLS policy, and traffic management config. Use when asked "should we use a service mesh", "Istio or Linkerd", or "design our mTLS policy".
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。
Manage vendor relationships — vendor selection scorecard, contract review checklist, renewal tracking, and vendor consolidation audit. Use when asked to "evaluate this vendor", "review this contract", "track vendor renewals", or "reduce our SaaS spend".
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 direction advisor — generates 3 differentiated visual directions with live HTML demos for selection. Use when the brief is vague ("make something nice", "pick a style", "explore directions"), when no design system exists, or when the user wants to compare visual approaches before committing. Richer than /form-style: produces actual visual demos, not just recommendations. Trigger words: "design direction", "style exploration", "pick a direction", "what should this look like", "give me options", "explore styles", "design philosophy".
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 a model training pipeline — algorithm selection, cross-validation, and serialization. Use when asked to "train a model for this", "design a training pipeline", or "which algorithm should we use".
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 embedding pipelines — model selection, batching, normalization, index refresh strategy. Use when asked to "design an embedding pipeline", "which embedding model should we use", or "how should we batch embeddings".
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 and implement an AI feature integration — model selection, architecture pattern, system prompt, data flow, error handling, cost estimate. Use when asked to "add AI to this", "LLM integration", "add Claude/GPT", or "AI-powered feature".
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 a caching strategy for an application — pattern selection, key design, TTL, and eviction policy. Use when asked to "add caching to this", "design a cache strategy", or "what should we cache".
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。
Filter rows of a Datagrok DataFrame via the datagrok_exec tool through the Filters panel — by range, equals/contains/in-set, multi-value, boolean, free-text row expressions, or substructure (SMILES / SMARTS / molblock). Also covers clearing, inverting, the show-only-filtered vs destructive-drop split, and the filter event lifecycle (onRowsFiltering / onFilterChanged / onRowsFiltered). Use whenever the user says "filter", "show only", "hide rows where", "narrow to subset", "find rows that", "contains", "substructure search", "categorical filter", "range filter", "invert", "clear the filter", "clear filters", "drop rows", or asks for the filtered subset as a new table. Does NOT cover selection (separate skill) or generic DataFrame cloning (datagrok-df-and-columns).
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。
Improve a Hinge dating profile through research-backed screenshot audits, photo selection and ordering, conversational discovery, prompt and caption writing, settings review, or post-launch troubleshooting. Use for a full profile rebuild or a focused request about photos, copy, positioning, match quality, or profile performance.
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。
Show a table and its map, diagram, timeline or chart together, with selection synced both ways. Use when data appears in two representations at once.
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。
Searches and discovers academic papers across multiple sources (Semantic Scholar, arXiv, Tavily, Exa, Gemini deep research, AMiner, Google Scholar) with adaptive engine selection based on query type. Returns ranked, deduplicated results with metadata (authors, venue, year, citations, abstract, PDF link). Use when the user asks to find papers / literature / publications / preprints / references on a topic, search for related work, look up a specific paper by title or DOI or arXiv ID, find papers by an author, find recent SOTA / state-of-the-art work, survey a research area, or run a deep / comprehensive literature search with synthesis.
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。
Runtime HAL for multi-runtime agent orchestration. Detects active AI assistant (Claude Code, Codex, Gemini, Cursor) and exposes uniform interface for startup injection, GUPP enforcement, and communication 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。
Vocabulary learning strategies and retention science for any language -- frequency-based word selection, spaced repetition systems (Ebbinghaus forgetting curve, Leitner system, SM-2 algorithm), cognate exploitation, word family networks, context-based learning, collocations, depth of word knowledge (form, meaning, use), reading progression from controlled to authentic texts, and productive vs. receptive vocabulary thresholds. Use when building vocabulary plans, optimizing review schedules, selecting learning materials, or diagnosing vocabulary gaps.
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。
Real-world problem formulation, mathematical abstraction, and applied mathematics for translating between practical problems and mathematical frameworks. Covers the modeling cycle (problem identification, assumptions, formulation, analysis, validation, interpretation), Polya's framework adapted for modeling, common model types (linear, exponential, logistic, periodic, power-law), dimensional analysis (Buckingham Pi theorem), optimization (linear programming, gradient descent, constraint satisfaction), probability models (Markov chains, queuing theory, Monte Carlo simulation), statistical modeling (regression, hypothesis testing, model selection), model criticism (overfitting, underfitting, sensitivity analysis), and real-world case studies. Use when formulating mathematical models, performing dimensional analysis, optimizing systems, running simulations, or evaluating model validity.
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。
Traditional Chinese internal arts — tai chi chuan and qigong — as transmitted by classical lineages (Chen, Yang, Wu, Wu/Hao, Sun for tai chi; Ba Duan Jin, Yi Jin Jing, Liu Zi Jue, Wu Qin Xi, and other named sets for qigong) with an emphasis on alignment, rooted stance, breath-posture-mind coordination (the three harmonies), and the traditional lineage voice that Dr. Yang Jwing-Ming's YMAA line preserves. Also covers the safety boundary between traditional practice and clinical "medical qigong" claims. Use for queries about tai chi form learning, qigong set selection, internal-arts pedagogy, and the practical question of where to start.
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。
Field observation, data collection, and research methods for geographic inquiry. Covers site selection and sampling strategies, field observation techniques (landscape reading, transects, quadrats), survey and interview methods for human geography, GPS and field mapping, environmental monitoring and instrumentation, data recording and field notebooks, ethical considerations in fieldwork, and the integration of field data with GIS analysis. Use when planning geographic field research, designing data collection protocols, evaluating field evidence, or connecting field observations to broader geographic 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。
CAD fundamentals, 3D printing (FDM/SLA/SLS), CNC machining, workshop skills, rapid prototyping methodology, and testing of physical prototypes. Covers fidelity levels, material selection for prototypes, dimensional tolerancing, assembly planning, and the iterate-test loop. Use when building prototypes, selecting fabrication methods, planning physical tests, or choosing between prototyping technologies.
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。
Statics, dynamics, stress-strain analysis, materials selection, failure modes, and finite element analysis concepts for engineered structures. Covers free-body diagrams, equilibrium, internal forces (shear and moment), beam deflection, column buckling, material properties, factor of safety, fatigue, fracture mechanics, and FEA workflow. Use when analyzing loads on structures, selecting materials, predicting failure, or interpreting structural behavior.
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。
Historical argumentation, evidence-based writing, and oral history methods. Covers thesis development for historical arguments, evidence selection and deployment, counterargument construction and rebuttal, historical writing conventions, and the methodology of oral history as both source and practice. Use when constructing historical arguments, writing analytical essays, conducting oral history interviews, or assessing the quality of historical writing.
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。
Algorithms and data structures from first principles through advanced analysis. Covers sorting (bubble, insertion, selection, merge, quick, heap, radix), searching (linear, binary, BFS, DFS, Dijkstra, A*), fundamental data structures (arrays, linked lists, stacks, queues, hash tables, trees, heaps, graphs, tries), complexity analysis (Big-O, Big-Omega, Big-Theta, amortized), recurrence relations, and algorithm design paradigms (divide-and-conquer, greedy, dynamic programming, backtracking). Use when analyzing, selecting, implementing, or comparing algorithms and data structures.
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。