Find blockers and showstoppers using TOC, TRIZ contradiction analysis, and Pre-mortem techniques.
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。
Find blockers and showstoppers using TOC, TRIZ contradiction analysis, and Pre-mortem techniques.
原文の言語: 英語
Identify constraints for a candidate using TOC, TRIZ, and Pre-mortem methods.
原文の言語: 英語
Inject artificial constraints to force creative divergence. Generates and applies constraints (resource, time, material, audience, scale) to existing ideas to produce variants.
原文の言語: 英語
Inject constraints → force creative response → extract transferable principles. Orchestrates constraint injection, response generation, and principle extraction.
原文の言語: 英語
Generate creative solutions under extreme constraints — no "impossible" allowed, find a way.
原文の言語: 英語
Build Current Reality Tree from UDEs through causal chains to core conflicts
原文の言語: 英語
Evaluate whether benchmark measures its claimed capability
原文の言語: 英語
Build constructive alternatives from destructive negation. Transform violated assumptions into viable innovation directions.
原文の言語: 英語
Detect train-test data leakage and memorization artifacts
原文の言語: 英語
Negate a claim, derive logical consequences step by step, detect whether a genuine contradiction or absurdity emerges.
原文の言語: 英語
Evaluate whether a derivation chain has reached a genuine contradiction, absurdity, or inconclusive state.
原文の言語: 英語
Identify technical and physical contradictions in a system through functional modeling and matrix analysis.
原文の言語: 英語
Query the 39x39 TRIZ contradiction matrix to find recommended inventive principles for a given technical contradiction.
原文の言語: 英語
Systematically vary parameters along defined axes, recording performance at each point to identify degradation thresholds.
原文の言語: 英語
Compare results across multiple model variants — quantitative agreement metrics and qualitative conclusion stability.
原文の言語: 英語
Evaluate whether the ranking has stabilized by analyzing rating history and computing stability metrics.
原文の言語: 英語
Iterative convergence to a single answer through Classic Delphi, Modified Delphi, or Nominal Group Technique rounds.
原文の言語: 英語
Extract core conflict in Evaporating Cloud format (A-B-C-D-D')
原文の言語: 英語
Generate dialectical opposites for governing variables — coherent alternative worldviews where the opposite is true.
原文の言語: 英語
Construct the strongest possible counter-argument to the convergence decision using Dialectical Inquiry and Thesis-Antithesis-Synthesis methods.
原文の言語: 英語
Systematically generate counterexamples (monsters) to a given claim using diverse heuristic strategies.
原文の言語: 英語
Generate counterexamples (monsters), attempt monster-barring, incorporate surviving counterexamples as lemma refinements (Lakatos method).
原文の言語: 英語
Tactic for reasoning about what would happen if variables were different — supports causal identification and intervention analysis.
原文の言語: 英語
Construct precise, internally consistent counterfactual scenarios where specified factors are altered, then reason about the resulting conclusion.
原文の言語: 英語
Strategy: Legal adversarial structure — prosecution presents case, defense responds, evidence is cross-examined, judge delivers verdict. Emphasizes evidence quality and procedural rigor.
原文の言語: 英語
Systematic coverage evaluation pipeline — benchmark inventory, method-problem crossing, and intersection evaluation to map explored vs unexplored solution space.
原文の言語: 英語
Detect uncovered regions in the solution space, producing a prioritized gap list.
原文の言語: 英語
Map evaluation coverage, identify untested capability dimensions — 20 benchmarks, 30 papers, 50 web searches
原文の言語: 英語
Compute coverage completeness, redundancy, and gap severity scores from a coverage map.
原文の言語: 英語
Creative Generation Engine — transforms research hypotheses into diverse solution spaces via 10 parallel creativity campaigns spanning structural, analogical, destructive, and combinatorial methods.
原文の言語: 英語
Challenge the evaluation criteria themselves using Assumption-based Planning, Critical Systems Heuristics, and Boundary Critique to ensure the framework is sound.
原文の言語: 英語
Extract evaluation criteria from research goals and candidate alternatives.
原文の言語: 英語
Attack an advocate's case with multiple arguments rated by severity.
原文の言語: 英語
Strategy: Classic triangular debate — Critic attacks, Defender responds, Judge adjudicates. Based on Irving AI Safety via Debate with Toulmin argumentation structure.
原文の言語: 英語
Flyvbjerg critical case methodology: select most-likely and least-likely cases to maximize inferential power.
原文の言語: 英語
Identify the critical chain — longest path considering resource contention
原文の言語: 英語
CPM forward/backward pass with float calculation to identify the critical path
原文の言語: 英語
Identify which input uncertainties contribute most to output uncertainty and compute EVPI for research prioritization.
原文の言語: 英語
Identify the shortest execution path via CPM forward/backward pass, resource leveling, and buffer insertion
原文の言語: 英語
CCA: pairwise consistency checking to reduce solution space 90-99%
原文の言語: 英語