Are resources sufficient? — Quantify compute, data, time, human, and financial resource constraints
原文の言語: 英語
メニュー
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。
Are resources sufficient? — Quantify compute, data, time, human, and financial resource constraints
原文の言語: 英語
Estimate resources, budget, and timeline using parametric, analogous, and three-point (PERT) estimation methods.
原文の言語: 英語
Quantify resource demand vs supply vs gap for each resource category
原文の言語: 英語
Statistically analyze collected results, verify reproducibility, and synthesize findings
原文の言語: 英語
Validate results through statistical testing, ROPE judgment, reproducibility re-runs, and final synthesis
原文の言語: 英語
Argue for rejected candidates using Devil's Advocacy, Dialectical Inquiry, and Adversarial Collaboration to ensure elimination was justified.
原文の言語: 英語
Systematically reverse positive statements to generate creative inversions. Produces reversed statements with initial associations.
原文の言語: 英語
How to make it worse? → reverse for solutions. Generate anti-solutions then invert to discover novel approaches.
原文の言語: 英語
Hostile reviewer perspective — find fatal flaws, logical gaps, and missing evidence in a solution.
原文の言語: 英語
Balance portfolio risk and return using Markowitz mean-variance, CVaR, Risk parity, and Kelly criterion methods.
原文の言語: 英語
Assess methodological bias using RoB2, PROBAST, or QUADAS-2 validated tools
原文の言語: 英語
Strategy: Action Priority matrix — classifies failure modes into H/M/L priority using severity-weighted scoring per AIAG-VDA 2019 Action Priority tables.
原文の言語: 英語
Design experiments to identify failure boundaries and robustness limits
原文の言語: 英語
Compute robustness index across scenarios with sensitivity analysis
原文の言語: 英語
Test conclusion robustness via multi-model convergence — enumerate assumptions, generate alternatives, compare results, flag fragile conclusions.
原文の言語: 英語
Select portfolios that perform well across multiple future scenarios using Minimax regret, Robust optimization, Scenario planning, and Info-gap methods.
原文の言語: 英語
Role-play as reviewer/practitioner/theorist/novice/competitor to generate diverse perspectives on a solution.
原文の言語: 英語
Drill from surface symptoms to root causes via 5 Whys, Ishikawa decomposition, and Current Reality Trees. Validates each causal link with literature evidence.
原文の言語: 英語
Decide whether to continue iterating or stop based on consensus score, round number, and stability.
原文の言語: 英語
Find and challenge domain's unquestioned beliefs. Systematic identification and productive violation of dogma.
原文の言語: 英語
Find domain's unquestioned beliefs. Systematic identification of dogma that constrains innovation.
原文の言語: 英語
Classify stakeholders by Mitchell et al. framework (Power, Legitimacy, Urgency). Assigns salience category and identifies systematically excluded parties.
原文の言語: 英語
SOP: power analysis and required experiment count estimation
原文の言語: 英語
Track score trajectories, detect saturation/failure points — 15 benchmarks, 50 papers, 60 web searches
原文の言語: 英語
Design scaling experiments to characterize performance-resource relationships
原文の言語: 英語
Analyze behavior across scales — detect regime changes, identify capacity limits, fit scaling laws within regimes.
原文の言語: 英語
Detect regime changes in scaling behavior — breakpoints where behavior qualitatively shifts, mechanisms behind transitions.
原文の言語: 英語
Execute SCAMPER 7 operators on a target solution. Subagent self-selects best 2-3 operators for deepest exploration.
原文の言語: 英語
7 operators (Substitute/Combine/Adapt/Modify/Put/Eliminate/Reverse) for systematic transformation of existing solutions.
原文の言語: 英語
Construct distinct future scenarios spanning key uncertainties for portfolio stress testing.
原文の言語: 英語
Identify key uncertainty drivers using PESTEL framework scanning
原文の言語: 英語
Assess each scenario's impact on the research approach across multiple dimensions
原文の言語: 英語
Build rich narratives for surviving morphological configurations using Shell method
原文の言語: 英語
Construct distinct future scenarios, evaluate portfolio performance under each, and identify vulnerabilities and robustness characteristics.
原文の言語: 英語
Strategy: Adjust research question scope — zoom in/out until the scope is appropriate
原文の言語: 英語
Broad landscape mapping strategy — quickly understand what exists in a field. Prioritizes breadth over depth with high paper-overview volume and minimal deep reading. Use when entering a new field or needing orientation before committing to deeper…
原文の言語: 英語
Synthesize score matrix, rankings, and sensitivity analysis into a final recommendation.
原文の言語: 英語
Two-phase sensitivity — Morris quick screening to eliminate unimportant factors, then Sobol precise decomposition on survivors. Efficient allocation of analytical effort.
原文の言語: 英語
First eliminate non-qualifying candidates with non-compensatory rules, then score survivors with full MCDA methods.
原文の言語: 英語
SOP: design random seed strategy for reproducibility
原文の言語: 英語