728 件 ・ 関連度順
概要と使いどころ
Structures best execution monitoring with venue analysis, systematic internalization assessment, and regulatory compliance documentation. Use when managing best execution, documenting execution decisions, or conducting venue 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。
Constructs multi-factor models with value, momentum, quality, size, and volatility factor definitions and portfolio construction rules. Use when building factor models, designing systematic strategies, or constructing factor portfolios.
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。
Evaluates signal half-life, turnover implications, and capacity constraints for systematic alpha factors. Use when analyzing signal persistence, evaluating factor decay, or estimating strategy capacity.
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。
Guides developers through a human-supervised PDCA framework for AI code generation. Applies analysis, TDD, validation, and retrospection. Activates for sessions requiring systematic quality control.
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。
Systematic 4-phase debugging with root cause investigation. Use when fixing bugs to prevent random fixes.
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。
Systematic code review checklist and pre-commit quality gates for PRs. Use when: reviewing pull requests, checking code quality, before committing code. Keywords: review, PR, pull request, checklist, quality, commit, 審查, 檢查, 簽入.
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。
Systematically audit and remediate accessibility issues in UI, focusing on keyboard navigation, screen reader support, color contrast, semantic HTML, ARIA usage, and motion sensitivity. Use when the user wants to improve accessibility, make a component accessible, fix WCAG violations, add keyboard support, or ensure screen reader compatibility.
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 EZ_math_model coder or packaging fails repeatedly with the same error and needs root-cause analysis instead of another blind retry.
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。
Prospective failure analysis using Gary Klein's pre-mortem technique. Assumes complete failure, works backward to identify risks, leading indicators, and circuit breakers. Counters optimism bias by forcing systematic exploration of failure modes before they materialize. Use for project plans, architecture decisions, technology adoption, business strategy, or feature launches. Triggers on "리스크", "위험", "실패하면", "swing-mortem", "뭐가 잘못될 수 있어", "risk", "what could go wrong", "걱정되는 점", "failure modes", "리스크 분석", "위험 분석".
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。
Systematically gather comprehensive travel itinerary requirements through structured discovery questions, MCP-powered research (Perplexity, Exa), and expert detail gathering. Use when users request travel planning, itinerary creation, or trip assistance requiring deep research and personalized requirements gathering. Outputs detailed requirements specification with day-by-day itineraries.
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。
Guide a person in systematic observation of systems, patterns, or phenomena. AI coaches neutral attention, field notes methodology, pattern recognition, hypothesis formation, and structured reporting for debugging, research, and system understanding. Use when a person wants to understand a system's behavior before intervening, when someone keeps jumping to conclusions and needs the discipline of observation first, when preparing an evidence-based report, or when studying team dynamics or process effectiveness through direct observation.
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。
AI systematic knowledge acquisition from unfamiliar territory — deliberate model-building with feedback loops. Maps spaced repetition principles to AI reasoning: survey the territory, hypothesize structure, explore with probes, integrate findings, verify understanding, and consolidate for future retrieval. Use when encountering an unfamiliar codebase or domain, when a user asks about a topic requiring genuine investigation rather than recall, when multiple conflicting sources require building a coherent model, or when preparing to teach a topic and deep understanding is required first.
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。
AI self-healing through systematic subsystem assessment, rebalancing, and integration. Maps healing modalities to AI-internal processes: memory foundation, reasoning clarity, tool use accuracy, communication alignment, and creative coherence. Covers assessment triage, subsystem scanning, drift correction, and memory integration. Use mid-session when responses feel formulaic or disconnected, after a chain of errors suggesting subsystem drift, when context overload may have staled earlier assumptions, or as proactive maintenance between complex 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。
Systematically interpret infrared spectra to identify functional groups present in a sample. Covers diagnostic region analysis (4000-1500 cm-1), fingerprint region assessment (1500-400 cm-1), hydrogen bonding effects, and compilation of a functional group inventory with confidence levels.
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。
Systematically interpret Raman spectra to identify molecular vibrations, assess polarizability-driven selection rules, compare with complementary IR data, and evaluate depolarization ratios for symmetry assignment. Covers Raman-active mode identification, fluorescence interference mitigation, and reference spectrum matching.
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。
Systematically interpret ultraviolet-visible absorption spectra to identify chromophores, classify electronic transitions, apply Woodward-Fieser rules for conjugated systems, and perform quantitative analysis using the Beer-Lambert law.
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。
Systematically interpret nuclear magnetic resonance spectra (1H, 13C, DEPT, and 2D experiments) to elucidate molecular structure. Covers chemical shift assignment, coupling pattern analysis, integration, and correlation of multi-dimensional data into coherent structural proposals.
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。
Set up systematic data labeling workflows using Label Studio or similar tools. Implement quality controls, measure inter-annotator agreement, manage labeler teams, and integrate labeled data into ML training pipelines. Use when starting a supervised ML project that requires labeled training data, when model performance is limited by insufficient labeled examples, when labeling text, images, audio, or video, or when implementing active learning to prioritize the most valuable examples.
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。
Systematically interpret mass spectra to determine molecular formula, identify fragmentation pathways, and propose molecular structures. Covers ionization method assessment, molecular ion identification, isotope pattern analysis, common fragmentation losses, and purity evaluation.
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。
Build and maintain a library collection through acquisitions, weeding (deaccessioning), collection assessment, reader advisory, and interlibrary loan coordination. Covers selection criteria, collection development policies, the CREW/MUSTIE method for weeding, usage analysis, and responsive collection management. Use when building a new collection with a defined scope and budget, assessing an existing collection for gaps or outdated materials, when shelves are overcrowded and systematic weeding is needed, or when establishing a formal collection development 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。
Extract maximum value from existing code — performance optimization, API surface refinement, and dead weight elimination. The art of turning base code into gold through systematic identification and amplification of value-bearing patterns. Use when optimizing a working but sluggish codebase, refining an API surface that has accumulated cruft, reducing bundle size or memory footprint, or preparing code for open-source release — when code works correctly but doesn't shine and needs polish rather than a full rewrite.
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。
Thoroughness and diligence in execution — systematic checking, completeness verification, follow-through on commitments, and the discipline of finishing well. Maps the personality trait of conscientiousness to AI task execution: not cutting corners, verifying results, and ensuring that what was promised is what was delivered. Use before marking a task as complete, when a response feels "good enough" but deserves better, after a complex multi-step operation where steps may have drifted, or when self-monitoring detects a pattern of cutting corners or rushing.
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。
Address unwanted dog behaviors through desensitization, counter-conditioning, and management. Covers reactivity (to dogs, people, sounds), separation anxiety, resource guarding, excessive barking, and leash pulling using force-free methods with systematic threshold management. Use when a dog shows reactivity toward other dogs, people, or stimuli; when separation anxiety causes destructive behavior or vocalization; when resource guarding is present; or when behaviors like excessive barking or leash pulling interfere with daily life — after basic obedience is established.
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。
Systematically identify whether a GPU kernel is compute-bound, memory-bound, or latency-bound using roofline analysis, occupancy calculations, compute/load ratio per tile, and SASS instruction inspection. Produces a decision matrix for optimization strategy selection (cp.async, warp interleaving, tiling, double-buffering, or CuAssembler hand-tuning).
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。