Build evidence network graph for network meta-analysis — nodes, edges, geometry assessment
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。
Build evidence network graph for network meta-analysis — nodes, edges, geometry assessment
原文の言語: 英語
Searches for external evidence supporting or opposing specific claims. Returns structured evidence with source assessment and relevance scoring.
原文の言語: 英語
Plan the statistical synthesis approach — model selection, heterogeneity strategy, and reporting
原文の言語: 英語
Synthesize multi-source evidence into structured argumentation. Weaves findings from literature, web, and analysis into coherent evidence maps with explicit strength ratings.
原文の言語: 英語
Tactic: Evidence gathering, cross-examination, and quality judgment. External evidence is collected, presented, challenged, and scored for relevance and reliability.
原文の言語: 英語
Tactic for assessing the strength and relevance of evidence for causal claims — distinguishes correlation from causation.
原文の言語: 英語
Map evolution mechanisms to design operations. Translate selection, mutation, drift, radiation into design operators.
原文の言語: 英語
Use evolution mechanisms (selection, mutation, radiation) as design operators for generating and refining solution populations.
原文の言語: 英語
Leave the problem entirely and explore an unrelated domain. Produces excursion domain discoveries for later force-fitting.
原文の言語: 英語
Full 8-stage Gordon-Prince excursion process. Deliberate departure from the problem into unrelated domains, then force-fit discoveries back.
原文の言語: 英語
Orchestrate the excursion sequence — departure into unrelated domain, force-fit discoveries back to problem, launch springboard ideas.
原文の言語: 英語
SOP: generate executable experiment configuration files
原文の言語: 英語
Structured data extraction from deep-read papers — produces comparison tables (method, dataset, metrics, results, limitations). Used by systematic-survey and deep-survey.
原文の言語: 英語
Generate boundary and extreme test values for a given parameter dimension to stress-test claims.
原文の言語: 英語
Bridge two unrelated thinking matrices via Koestler bisociation. Identify independent frames of reference and force collision to produce creative insight.
原文の言語: 英語
List all key factors, conditions, and assumptions that support or enable the artifact's conclusion.
原文の言語: 英語
Identify independent, dependent, and control variables for an experiment
原文の言語: 英語
Strategy: Systematic factor removal — remove factors one at a time and observe whether the conclusion remains stable, identifying which factors are load-bearing.
原文の言語: 英語
DOE thinking: identify factors, define levels, and explore combinations to systematically cover the design space.
原文の言語: 英語
Build cause-mode-effect chains tracing upstream root causes and downstream cascading effects for each failure mode.
原文の言語: 英語
Tactic: Trace upstream causes and downstream effects of each failure mode. Builds multi-level cause-mode-effect chains for systemic understanding.
原文の言語: 英語
Group observed failures by mechanism (not symptom), identify common triggers per cluster, estimate frequency and severity.
原文の言語: 英語
Generate targeted solutions for each identified failure mode, ensuring every failure has at least one proposed mitigation.
原文の言語: 英語
Systematically catalog failure modes — generate edge cases, observe failures, cluster by mechanism, identify triggers and frequency.
原文の言語: 英語
Extract structured failure mode list from raw scenarios or artifact analysis. Produces standardized failure mode records.
原文の言語: 英語
Catalog all failure modes in a domain, classify them systematically, and generate targeted solutions for each failure type.
原文の言語: 英語
Detect false gaps — search failures, already-solved gaps, and inherently unanswerable questions masquerading as research gaps.
原文の言語: 英語
Tactic: hypothesis quality assurance — check falsifiability, repair failing hypotheses, complete operationalization and boundary-condition specification
原文の言語: 英語
Wish-fulfillment thinking — ignore physical laws for ideal solution. Use unconstrained imagination to reveal what the problem truly needs.
原文の言語: 英語
Unconstrained wish-fulfillment ideation. Ignore all physical laws to imagine the ideal solution, then identify realization directions.
原文の言語: 英語
Cross-reference the GoalTree against ActorProfile (capabilities), ObstacleReport (known barriers), and timeline (deadline feasibility). Identify infeasible paths and suggest OR alternatives.
原文の言語: 英語
Strategy: reshape a research question under resource constraints — pragmatic adjustment that preserves core value
原文の言語: 英語
Synthesize all assessments into a feasibility matrix, recommendation, and risk summary.
原文の言語: 英語
Create anonymized feedback report summarizing group judgment distribution for a given round.
原文の言語: 英語
Tactic for identifying circular causation — detect feedback loops, classify as reinforcing or balancing, document loop structure.
原文の言語: 英語
Aggregate, deduplicate, and classify findings from multiple probes into a coherent vulnerability report.
原文の言語: 英語
Iterative "Why?" questioning (5+ levels) to drill from surface phenomenon to actionable root cause. Each level verified against evidence.
原文の言語: 英語
Find the minimal change magnitude along a dimension that causes the conclusion to flip from true to false.
原文の言語: 英語
Force-fit excursion discoveries back to the original problem. Deliberately create connections between unrelated findings and the challenge.
原文の言語: 英語
Force connections between unrelated technologies. Deliberately construct bridges where none naturally exist to discover novel integration possibilities.
原文の言語: 英語