Full-text deep reading of methodology papers — complete understanding of algorithms, proofs, and implementation details.
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。
Full-text deep reading of methodology papers — complete understanding of algorithms, proofs, and implementation details.
原文の言語: 英語
Paper AI summary reading — deeper understanding of specific methodology papers without full-text commitment.
原文の言語: 英語
Determines when to stop iterating — coverage threshold met or marginal returns diminishing. Shared across all campaigns.
原文の言語: 英語
Build a complete scoring matrix through criterion definition, weighting, scoring, normalization, and sensitivity testing.
原文の言語: 英語
Tests conclusion robustness by perturbing parameters and observing rank changes. Shared across scoring, portfolio, and steel-manning campaigns.
原文の言語: 英語
Deep web research with full-page extraction — detailed methodology guides, tutorials, implementation references.
原文の言語: 英語
Quick web scan to discover relevant pages — methodology references, case studies, best practices for convergence methods.
原文の言語: 英語
Surface, perturb, and prioritize assumptions by disruption potential. Orchestrates assumption surfacing → perturbation → sacred cow identification → prioritization.
原文の言語: 英語
Perturb each assumption, observe system response. Systematic stress-testing of assumptions to reveal fragility and opportunity.
原文の言語: 英語
Enumerate implicit assumptions in a problem statement or existing solution. Produces categorized assumption inventory (physical, social, temporal, economic, technical).
原文の言語: 英語
Catalog all known solutions/methods in a domain with performance, applicability, and limitations.
原文の言語: 英語
Systematically enumerate parameter dimensions and generate viable combinations. Orchestrates parameter extraction → value enumeration → compatibility assessment → synthesis.
原文の言語: 英語
Pairwise consistency evaluation to reduce solution space by identifying and removing inconsistent combinations.
原文の言語: 英語
Evaluate pairwise value consistency (logical/empirical/normative)
原文の言語: 英語
Identify factors and their levels for a problem, then design an experiment matrix for systematic exploration.
原文の言語: 英語
Systematically catalog all failure modes in a domain or method, producing a classified failure taxonomy.
原文の言語: 英語
Score ideas on novelty dimensions — structural distance from known solutions, conceptual surprise, domain-crossing depth. Produces ranked novelty assessment.
原文の言語: 英語
Abstract-level paper scanning for broad coverage. Import of literature-engine/literature-overview skill. Abstract-level only — no methodology conclusions from abstracts.
原文の言語: 英語
Deep paper analysis with full text reading. Import of literature-engine/literature-research skill. Full text access — required for quoting results, verifying claims, extracting detailed methodology.
原文の言語: 英語
Mid-depth paper analysis via AI-generated summaries. Import of literature-engine/literature-search skill. Reads AI summary — sufficient for methodology understanding but not for quoting specific results.
原文の言語: 英語
Rotate through reviewer/practitioner/theorist/time-machine/novice perspectives systematically. Ensures comprehensive viewpoint coverage.
原文の言語: 英語
Generate PO provocations and extract constructive movement. Orchestrates assumption surfacing → provocation creation → movement extraction → idea formation.
原文の言語: 英語
Determine when additional ideation yields diminishing returns. Analyzes latest idea batch against existing corpus to judge continue/near-saturation/saturated.
原文の言語: 英語
Deep web page analysis with full content extraction. Import of web-browsing/web-research skill. Must fetch full page via apify — no shortcuts.
原文の言語: 英語
Quick web scanning for landscape understanding. Import of web-browsing/web-search skill. Snippets only — no conclusions from snippets alone.
原文の言語: 英語
Systematically identify all assumptions in a method/model — structural, parametric, distributional, and scope assumptions.
原文の言語: 英語
Systematically extract all assumptions (stated, implicit, boundary, mathematical, practical) from a method or model.
原文の言語: 英語
One-at-a-time assumption perturbation — extract assumptions, define negations, re-derive conclusions under each negation, measure sensitivity. Identifies which assumptions are load-bearing.
原文の言語: 英語
Systematically extract implicit assumptions from methods, frameworks, or arguments. Identifies what is taken for granted without explicit justification.
原文の言語: 英語
Double-loop learning escalation — surface governing variables, generate counter-assumptions, test if problem dissolves under alternatives, score wickedness if it persists.
原文の言語: 英語
Systematic failure mode cataloging — generate boundary inputs, observe failures, cluster by mechanism, identify triggers, estimate frequency.
原文の言語: 英語
Identify research gaps via PICOS frameworks, concept matrices, evidence gap maps, keyword extraction, citation analysis, and topic modeling. Systematic discovery of what is missing in the literature.
原文の言語: 英語
Score and rank validated gaps on importance, feasibility, novelty, and urgency. Multi-criteria decision analysis with stakeholder confirmation.
原文の言語: 英語
Score gaps on multiple dimensions (importance, feasibility, novelty, urgency, impact) using weighted multi-criteria decision analysis.
原文の言語: 英語
Simulate multiple stakeholder perspectives evaluating a research gap, method, or proposal. Identifies blind spots from single-perspective analysis.
原文の言語: 英語
Paper metadata and abstract-level overview. Import of literature-engine/literature-overview skill. Abstracts only — no substantive claims without deeper reading.
原文の言語: 英語
Full-text paper reading via three-pass Keshav method. Import of literature-engine/literature-research skill. Authoritative source for claims about paper content.
原文の言語: 英語
AI-powered paper summary and search. Import of literature-engine/literature-search skill. AI summary level — cite as "AI-extracted" not "paper states".
原文の言語: 英語
Generate de Bono lateral thinking provocations to challenge dominant ideas using escape, reversal, exaggeration, and distortion.
原文の言語: 英語
Combine multi-axis perturbation data into a multi-dimensional validity description with boundary conditions and interaction effects.
原文の言語: 英語