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概要と使いどころ
Analyzes the feasibility of a proposed Meta-analysis topic by searching for existing Meta-analyses and Clinical Trials on PubMed/ClinicalTrials.gov. Use when you need to evaluate if a topic is viable for a new Meta-analysis.
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Use when writing medical school personal statements, residency application essays, fellowship statements, or graduate school admissions essays. Crafts compelling narratives highlighting clinical experiences, research achievements, and career motivations for healthcare education applications.
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Conduct professional peer reviews for papers or theses, providing structured evaluations and improvement suggestions; use when you need a pre-submission assessment, an internal review, or academic quality control.
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Generates complete conventional oncology bulk-transcriptome biomarker and hub-gene research designs from a user-provided cancer type and study direction. Always use this skill whenever a user wants to design, plan, or build a tumor bioinformatics study centered on differential expression, prognostic filtering or risk modeling, PPI-based hub-gene prioritization, diagnostic/prognostic evaluation, clinical association, immune infiltration context, methylation context, and optional tissue or cell validation. Covers five study patterns (signature-first prognostic workflow, hub-gene-first biomarker workflow, hybrid signature-to-hub workflow, immune-context biomarker workflow, translational validation workflow) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path...
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Generates complete conventional non-oncology diagnostic machine-learning research designs from a user-provided disease context, optional mechanism theme, and validation direction. Use when a study centers on disease-vs-control transcriptome comparison, optional mechanism-gene restriction, feature shrinkage, diagnostic model construction, ROC / calibration / DCA evaluation, interpretation layers, and orthogonal validation. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
日本語の概要は準備中です。原文の説明を表示しています。
Designs QTL colocalization studies that connect eQTL, pQTL, sQTL, or related molecular QTL signals with GWAS loci. Always use this skill whenever a user wants to plan, scope, or structure a locus-level study asking whether a GWAS association and a molecular QTL association may reflect the same underlying causal signal. Covers locus definition, QTL/GWAS source architecture, ancestry and LD alignment, single-locus vs multi-locus strategy, candidate-gene prioritization, optional fine-mapping, linked MR/SMR follow-up, and functional annotation. Always output four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, stepwise workflow, method rationale, evidence hierarchy, figure plan, minimal executable version, and strictly verified literature guidance with no fabricated references. Never equate colocalization with causality proof, mediation proof, or automatic target validation. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets, repositories, consortia, or public resources.
日本語の概要は準備中です。原文の説明を表示しています。
Generates complete conventional non-oncology bioinformatics research designs from a user-provided disease context, process-related gene family or biological theme, and validation direction. Use when a study centers on multi-dataset bulk transcriptome integration, DEG analysis, process-gene intersection, enrichment analysis, GSEA, PPI hub-gene prioritization, TF/miRNA regulatory networks, ROC-based biomarker evaluation, and immune infiltration analysis. Covers five study patterns (process-DEG discovery, enrichment/GSEA interpretation, hub-gene prioritization, regulatory-network and immune interpretation, multi-layer public validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
日本語の概要は準備中です。原文の説明を表示しています。
Generates complete dual-disease shared-transcriptome biomarker and hub-gene research designs from a user-provided disease pair and shared-biology direction. Always use this skill whenever a user wants to design, plan, or build a non-oncology two-disease transcriptome study centered on per-disease differential expression, shared-signal intersection or concordance, PPI-based hub-gene prioritization, diagnostic evaluation across both diseases, immune infiltration context, pathway interpretation, and optional orthogonal validation. Covers five study patterns (shared-DEG-first workflow, hub-gene-first shared-biomarker workflow, hybrid shared-biomarker compression workflow, immune-context shared-biomarker workflow, orthogonal validation workflow) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path...
日本語の概要は準備中です。原文の説明を表示しています。
Use when evaluating diagnostic biomarker performance from case-control expression data with logistic regression and ROC curves, exporting coefficient and AUC tables together with a ROC PDF. NOT for: survival analysis, time-to-event outcomes, multiclass classification, calibration curves, decision-curve analysis, or nomogram construction.
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Use when evaluating the clinical utility of a binary prediction model from a single clinical CSV file by fitting a logistic decision-curve model, plotting decision and clinical-impact curves, and exporting summary outputs. NOT for: survival calibration, ROC-only discrimination analysis, nomogram construction, or time-to-event outcomes.
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Use when building a decision tree model in R and generating feature importance ranking outputs. Supports classification and regression, automatic task detection, parameter validation, model evaluation summaries, and exports of feature-importance tables and figures.
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Use when evaluating Richard Dennis / Turtle Trading-style breakout systems: Donchian channels, ATR/N-unit sizing, pyramiding, portfolio heat, and fully rule-based trend following.
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Use when evaluating A-share short-term technical setups through Tang Nengtong's classic TA lens: volume-price relationships, moving average systems, breakout signals, and short-term momentum entry/exit.
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Use when evaluating macro dislocations, reflexive feedback loops, policy shifts, positioning squeezes, and asymmetric bets in a George Soros style.
日本語の概要は準備中です。原文の説明を表示しています。
Use when evaluating David Tepper-style distressed macro opportunities: crisis assets, policy backstops, capital structure, bank/credit stress, and asymmetric recovery trades.
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Use when evaluating investments through Zhang Lei / Hillhouse Capital's long-term research-driven value lens: deep research, do time's friend, invest in great companies, and actively help them grow.
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Use when evaluating Jim Simons / Renaissance-style quantitative strategies: many weak signals, statistical validation, signal decay, portfolio construction, execution cost, and model risk.
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Use when evaluating Spruce Point-style accounting and governance shorts: earnings quality, cash conversion, aggressive adjustments, acquisition accounting, and capital-allocation red flags.
日本語の概要は準備中です。原文の説明を表示しています。
Use when evaluating platforms, marketplaces, and networks through a USV-style lens: network effects, defensibility, protocols, openness, and scalable distribution.
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Use when evaluating Bitcoin and crypto cycles through a Willy Woo-style on-chain lens: holder behavior, realized value, supply dynamics, and on-chain demand.
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Use when evaluating macro trends through Shi Hanbing's interest-analysis (利益分析法) lens: trace who benefits from what policy, follow the money, and predict macro direction from incentive structures.
日本語の概要は準備中です。原文の説明を表示しています。
Use when evaluating early-stage startups through a YC-style lens: talk to users, achieve product-market fit, grow simply, default alive, and avoid premature scaling.
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Use when evaluating John Templeton-style global contrarian value: buy at points of maximum pessimism, search worldwide, and demand a large discount to long-term value.
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Use when evaluating David Einhorn-style forensic shorts and longs: accounting red flags, governance problems, capital allocation failures, and evidence-based short theses.
日本語の概要は準備中です。原文の説明を表示しています。