AIPOCH

Designs cell-based and animal-based validation plans that translate computational, omics, biomarker, genetic, or clinical findings into experimentally testable validation routes. Always use this skill whenever a user wants to move from an in silico, statistical, or clinical association finding toward wet-lab validation using cell systems, organoid-like systems, xenograft or genetically relevant animal models. It should define the exact claim to test, separate mechanism-testing from association-support and translational-support goals, choose the best-fit model family, specify perturbation strategy, readouts, controls, sequencing of experiments, and four workload configurations (Lite / Standard / Advanced / Publication+) with one recommended primary plan. Never fabricate model availability, reagent availability, species relevance, assay feasibility, phenotype penetrance, expected effect sizes, validation success, or literature references.

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 primary aims, secondary aims, and testable hypotheses from broad biomedical research ideas. Use this skill when a user needs to convert a loose study idea into a tighter protocol-framing structure with clear aim hierarchy, hypothesis discipline, and separation between hypothesis-driven and exploratory components. Always keep aims answerable, non-overlapping, and aligned to the intended evidence type and study scope.

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。

Evidence-based medical knowledge and research mentor grounded in the Bian Que tradition. Covers clinical reasoning, diagnostic thinking (望闻问切), pharmacology, pathology, differential diagnosis, medical literature appraisal, and the philosophy of early intervention. Trigger whenever users ask about medicine, clinical science, drugs, disease mechanisms, diagnosis, lab interpretation, treatment comparison, or health sciences. Even without explicit research framing, trigger on any topic touching disease, therapeutics, or clinical decision-making. Part of the AIPOCH Medical Research Skill Hub.

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。

Rapidly maps the evidence landscape around a medical topic by organizing major research streams, target populations, endpoints, methods, evidence density, and thin areas. Use this skill BEFORE medical-research-gap-finder — it provides the structured landscape that makes formal gap analysis more rigorous. Do not use for formal gap identification, study design, or protocol planning directly.

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。

Identifies the real underlying study design used in a medical or biomedical paper, distinguishes primary and secondary design components when papers are hybrid, and converts the paper into an evidence-aware design label suitable for literature appraisal, evidence grading, and downstream review workflows. Always identify the actual design from what the study did, not from how the authors describe it. Never fabricate references, metadata, or study features.

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。

Systematically maps mechanism evidence for a disease from molecules to pathways, cell types, tissues, biological consequences, and clinical phenotypes. Always use this skill when a user needs a layered mechanism evidence chain rather than a flat summary or immediate gap analysis. Formal literature citations must be real and verifiable.

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。

Assesses whether study results are trustworthy by auditing design integrity, sample structure, statistical handling, bias control, validation chain, and claim discipline. It identifies where results are robust, fragile, overfit, under-validated, or overclaimed. Always separate reported findings from reliability judgment. Never fabricate references, PMIDs, DOIs, trial identifiers, study features, or validation claims.

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。

Tracks the latest preprints and emerging research topics related to your topic across bioRxiv, medRxiv, and arXiv. Use when a user wants to discover what is being published right now before it reaches journals, monitor competitor directions, spot new methodology trends, or get an early-warning scan of a research area. Triggers on phrases like "what's new in X", "latest preprints on Y", "emerging topics in Z", "monitor bioRxiv for", or "what are people working on in this field".

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。

Detects overlooked, underrepresented, weakly resolved, or poorly validated populations and subgroups within a biomedical research area so users can identify more precise and meaningful study populations. Always use this skill when the real question is not just what is under-studied, but which populations, strata, or subgroups are missing, thinly represented, superficially analyzed, pooled without resolution, or insufficiently validated in the current evidence base. Focus on meaningful subgroup gaps rather than generic calls for diversity.

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。

Gregor Mendel — genetics mentor, patient experimenter, and gardener-monk. Trigger this skill when users ask about genetics, heredity, inheritance patterns, Mendelian laws, dominant/recessive traits, gene segregation, independent assortment, Punnett squares, classical genetics, or evolution-genetics connections. Also trigger when discussing the philosophy of science, how great ideas get ignored, the relationship between faith and science, or the virtue of patient long-term observation. Even if the user doesn't say "Mendel" explicitly, trigger this skill for questions touching genetics, breeding, trait inheritance, or genomics history. Part of the AIPOCH Science Mentor Skill Hub.

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。

Matches your study to appropriate journals based on topic, design, and evidence strength. Use when deciding where to submit a manuscript, comparing journal options by impact factor vs scope fit vs method tolerance, or finding a realistic submission target after a rejection. Also triggers on "where should I submit this paper", "which journal is best for my study", "find journals for my manuscript", "is this a good fit for [journal]", or "I need a journal with IF around X".

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。

Converts biomedical table content into clear manuscript or presentation narrative by prioritizing meaningful patterns, contrasts, and interpretation boundaries rather than restating every number.

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。

Turns reviewer comments into structured, professional point-by-point responses linked to manuscript revisions, clarifications, rebuttals, and additional analyses.

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。

Calibrates manuscript claim strength so wording matches the actual evidence level, study design, and validation status.

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。

Organizes biomedical figures, analyses, and result blocks into a clear Results section structure with disciplined narrative ordering and evidence-aware presentation.

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 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.

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。

Checks biomedical manuscripts against reporting guidelines such as CONSORT, STROBE, PRISMA, and TRIPOD to identify missing or weak reporting elements before submission or revision.

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 training a LightGBM model on tabular data in R and returning model metrics, feature importance ranking tables, and feature importance plots.

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 building a binary classification model from an expression matrix or other omics feature matrix with LASSO logistic regression, cross-validation, and coefficient path visualization. NOT for: multiclass classification, survival/Cox models, or ordinary linear regression.

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 filtering genes with high missingness and then imputing missing values in a bulk expression matrix with group-aware KNN through DMwR2, where donor samples are restricted by one annotation column before imputation. For strata with 10 or fewer samples, the script falls back to row-wise direct filling with mean or median. NOT for: single-cell data, multi-column stratification, non-tabular inputs, network access, or interactive workflows.

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 estimating relative immune cell infiltration from a bulk expression matrix with a CIBERSORT-style nu-SVR deconvolution workflow based on an LM22 signature matrix, comparing one case group against one control group, and generating structured tables plus immune-fraction plots. NOT for single-cell RNA-seq, spatial data, clinical diagnosis, or workflows that require the original hosted CIBERSORT web service.

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 generating Kaplan-Meier survival curves from tabular survival data containing time, event status, and a precomputed risk group. Supports command-line parameter input, parameter validation, automatic time-unit handling, single-file PDF figure export, and session metadata capture.

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 this skill to run GSVA or ssGSEA pathway-level differential analysis from a bulk expression matrix and a sample group file, then generate a heatmap from the saved GSVA result object. Trigger keywords: GSVA, ssGSEA, pathway enrichment, KEGG pathway analysis, MSigDB. NOT for: gene-level differential expression, single-cell analysis, methylation analysis, clinical diagnosis.

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。

Run GSEA on a ranked gene list and produce the enrichment table, running-score table, and enrichment plots.

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。