Force connection between two unrelated concepts. Deliberately construct bridging paths where no natural connection exists.
原文の言語: 英語
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このリポジトリの skills
SkillsMP は yogsoth-ai/de-anthropocentric-research-engine から 961 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
yogsoth-ai/de-anthropocentric-research-engine収集済み skill 961 件中 40 件を表示しています。
Force connection between two unrelated concepts. Deliberately construct bridging paths where no natural connection exists.
原文の言語: 英語
Split concepts into smaller units and recombine them differently to produce novel structures.
原文の言語: 英語
Identify which specific assumption changes cause conclusion divergence. Rates fragility severity and plausibility of alternatives.
原文の言語: 英語
Compute a fragility index from flip-point distances and degradation scores, summarizing how robust the conclusion is.
原文の言語: 英語
Strategy: Select an RQ framework (PICO/SPIDER/SPICE/ECLIPSE) and apply it systematically
原文の言語: 英語
Tactic: Select the most suitable RQ framework and apply it systematically
原文の言語: 英語
Produce a complete ordering of all candidates using PROMETHEE I/II, ELECTRE III, or MAVT methods.
原文の言語: 英語
FMEA Step 3: Decompose artifact into function tree — identify what each component is supposed to do before analyzing how it can fail.
原文の言語: 英語
TRIZ function analysis: function-level recombination and redistribution
原文の言語: 英語
Build substance-field functional model of a system, annotating useful, harmful, insufficient, and excessive interactions.
原文の言語: 英語
Redistribute functions across different components
原文の言語: 英語
Remove components while preserving function via TRIZ trimming methodology. Simplify systems by redistributing functions.
原文の言語: 英語
Map technical functions to biological functions, find organisms solving equivalent problems. Deep functional matching across domains.
原文の言語: 英語
Build biological system functional model. Map energy, matter, and information flows.
原文の言語: 英語
Project solution effects using Future Reality Tree logic
原文の言語: 英語
Aggregate probability judgments across perspectives using Real-Time Delphi or prediction market mechanisms.
原文の言語: 英語
Classify identified gaps using Miles 7-type taxonomy and AHRQ 4-reason framework. Determines gap type (theoretical, methodological, empirical, etc.) and root cause of gap existence.
原文の言語: 英語
Generate solutions targeting specific coverage gaps — detect gaps, generate failure-driven solutions, and design factor-level experiments.
原文の言語: 英語
Extract gap-indicating sentences and phrases from papers/reviews. Identifies linguistic markers of research gaps (e.g., "remains unclear", "has not been explored", "limited understanding").
原文の言語: 英語
Compile all gap analysis intermediate products into a coherent final report with executive summary, detailed findings, and research agenda.
原文の言語: 英語
Compile all gap analysis products into a coherent final report with evidence gap maps, research agenda, and concept matrices.
原文の言語: 英語
Classify gaps using Miles 7-type taxonomy (theoretical, methodological, empirical, population, practical, knowledge void, evidence gap).
原文の言語: 英語
Validate gap authenticity via cross-database verification, temporal sensitivity testing, and false-gap filtering. Ensures gaps are genuine absences, not search artifacts.
原文の言語: 英語
Define gate criteria and pass thresholds for a specific stage in the Stage-Gate process.
原文の言語: 英語
Evaluate a candidate against gate criteria and render GO/KILL/RECYCLE verdict with evidence.
原文の言語: 英語
Ritchey GMA: complete iterative morphological process
原文の言語: 英語
Propose 3-8 candidate research fields based on the full ActorProfile. When user wants to explore beyond their current stack, use other ActorProfile signals (intentionality, boundary) to determine exploration space. Free exploration within the boundary.
原文の言語: 英語
Aggregate all accumulated context from the crystallization process into a structured ResearchBrief document. This is the final output artifact alongside the North Star — a comprehensive requirement context document for downstream research strategies.
原文の言語: 英語
Extract shared abstract structure from two input spaces
原文の言語: 英語
Structure the user's chosen direction into a formal goal tree using KAOS-style AND/OR decomposition. Validate feasibility against ActorProfile and ObstacleReport. Use after obstacle-analysis confirms the direction is viable.
原文の言語: 英語
Apply Argyris framework to identify governing variables — the unstated rules driving behavior in a research field.
原文の言語: 英語
Structured creative thinking in Six Hats Green Hat mode — pure creative output with judgment suspended.
原文の言語: 英語
Strategy: 10th Man Rule and Liberating Structures — institutionalized dissent to prevent premature consensus and expose suppressed objections.
原文の言語: 英語
Explain why different studies reach different conclusions — heterogeneity investigation protocol. Budget: 30 studies, 30 effect sizes, 50 web searches.
原文の言語: 英語
Identify and classify sources of between-study heterogeneity (clinical, methodological, statistical)
原文の言語: 英語
Tactic for building is-a and part-of hierarchies — establish parent-child relationships, verify transitivity, detect cycles.
原文の言語: 英語
Generate "How Might We" questions at different scope levels (narrow, medium, broad). Ensures each is actionable without being prescriptive.
原文の言語: 英語
Minimal crystallization strategy for users who already have a specific research topic or problem (e.g., "I want to improve CoT faithfulness in LLMs") and need structuring into a formal North Star. Heavily simplifies or skips exploration tactics, focusing on…
原文の言語: 英語
Strategy: refine a working hypothesis into a precise, testable form
原文の言語: 英語
Synthesize diverse ideas into coherent solution concepts. Combines fragments from multiple ideation passes into structured, actionable ideas with clear mechanism descriptions.
原文の言語: 英語