Claude Skills by aipoch

Use this bioinformatics data analysis skill to construct a database-driven lncRNA-mRNA regulatory network from target lncRNA and/or gene lists by projecting shared miRNA evidence from local ceRNA reference tables. It does not infer networks from expression matrices.

Use when assessing how well a survival model's predicted probabilities agree with observed outcomes by fitting a Cox model and generating bootstrap calibration curves at one or more prediction horizons from a clinical CSV file. NOT for: nomogram construction, univariate Cox screening, ROC analysis, or decision-curve analysis.

Use when constructing a prognosis nomogram from survival-related clinical predictors, exporting the nomogram bundle and C-index table, and optionally rendering the final nomogram PDF. NOT for: univariate/multivariable Cox feature screening, calibration curves, ROC analysis, decision-curve analysis, or non-survival outcomes.

Use when performing PCA principal component dimensionality reduction on tabular numeric data. Supports command-line parameter input, automatic numeric feature selection, parameter validation, result directory creation, and CSV or TXT format result export.

Use when you need a standardized R CLI workflow to build a protein-protein interaction network from a local gene list and an offline STRING cache, export node and edge tables, and render a reproducible PDF network plot. NOT for online API fetching, arbitrary graph databases, multi-omics integration, or non-STRING interaction sources.

Use when you need a standardized R CLI workflow to train a two-class random forest model from an expression-like feature matrix, rank variable importance, and generate reproducible error and importance plots. NOT for regression tasks, multi-class classification, missing-value imputation, preprocessing, or remote data fetching.

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.

Use when performing correlation analysis between two variables including Pearson and Spearman correlation methods. Supports command-line parameter input, automatic data format detection, parameter validation, result directory creation, and CSV or TXT format result export.

Use when generating Sankey or alluvial plots from sample annotation tables where rows are samples and selected columns are categorical stages such as risk group, response status, subtype, or cohort labels. NOT for: gene network flow analysis, continuous-value trajectories, or graph-structured pathway visualization.

Use when estimating immune infiltration from bulk RNA-seq expression matrices with ssGSEA/GSVA, comparing case versus control groups, and generating downstream immune-score visualizations. NOT for single-cell RNA-seq, absolute cell proportion estimation, or clinical decision making.

Use when you need a standardized R CLI workflow to run two-class SVM-RFE feature ranking on an expression-like matrix, choose an informative feature count from cross-validated error, and generate reproducible ranking and error plots. NOT for regression, multi-class classification, missing-value imputation, or remote data fetching.

Use when analyzing transcription factor (TF) regulatory networks using Dorothea database. Input gene list, identify regulating transcription factors, generate TF-Target network visualization. For: transcription factor enrichment analysis, gene regulatory network research.

Use when performing time-dependent ROC curve analysis for survival data with follow-up time, event status, and a numeric marker. Supports CSV/TXT/TSV/Excel input, `risk_score` as the default marker unless `--marker_col` is provided, parameter validation, standardized output directories, AUC table export, ROC point export, and PDF figure generation.

Use when performing sample-level dimensionality reduction and visualization on abundance or OTU-style matrices with a companion group file, generating UMAP and/or t-SNE coordinates and plots for group separation assessment. NOT for: differential expression testing, single-cell workflows requiring dedicated embeddings pipelines, or analyses without a sample grouping file.

Use when running prognostic survival analysis on a clinical cohort with time-to-event data to estimate univariate and multivariable Cox proportional hazards models, export result tables, and generate forest plots. NOT for: nomogram construction, calibration curves, time-dependent ROC analysis, or model training/feature selection beyond the built-in univariate screening rule.

Use when building a weighted gene co-expression network from a bulk expression matrix and a sample group file, filtering variable genes by MAD, identifying co-expression modules with WGCNA, correlating modules with traits, and exporting module-level plots and gene tables. NOT for single-cell RNA-seq, differential expression testing, methylation analysis, or datasets that are too small for WGCNA after quality control.

Finds translational opportunities that connect basic-research discoveries to clinically meaningful use cases such as diagnosis, stratification, prognosis, treatment response prediction, monitoring, or therapeutic development. Use this skill when a user wants to turn a mechanism finding, pathway signal, cellular phenotype, experimental observation, or omics discovery into a stronger translational research direction. Always separate mechanistic relevance from translational usability, and never ...

Identifies translationally meaningful paths for bioinformatics findings by mapping omics or computational discoveries to diagnosis, stratification, prognosis, treatment-response, monitoring, or target-nomination use cases, while auditing bridge evidence, assayability, and validation burden. Use this skill when a user wants to know whether a bioinformatics finding can be framed as a stronger translational topic without overclaiming clinical relevance. Always separate statistical signal from tr...

Scans the biomarker landscape of a disease area by biomarker type, clinical/research use case, evidence layer, validation status, and maturity level. Use this skill when a user wants a field-level biomarker evidence map rather than a generic literature summary. Always separate exploratory biomarkers from externally validated or clinically embedded biomarkers, and never imply clinical maturity without explicit evidence support.

Builds professional search strategies for PubMed, Embase, Web of Science, and similar databases. Use when a user needs to construct a MeSH-based Boolean query, design a systematic review search, expand a concept with synonyms, apply study-type or date filters, or adapt a query across multiple databases. Also triggers when the user says "help me search for papers on X", "build a search strategy", "what are the MeSH terms for", or "I need a systematic review search string".

Clarifies a vague clinical or biomedical research idea into a structured, bounded, searchable, researchable, and testable question. Always use this skill whenever a user has an early-stage clinical or research thought, an over-broad topic, an ill-defined evidence question, or an unclear problem statement that must be translated into a question framing suitable for literature retrieval, evidence synthesis, gap analysis, study design, or downstream protocol planning. Never jump straight to answ...

Explains why studies on the same biomedical topic reach different or opposing conclusions by auditing differences in population, endpoint definition, sample source, assay or platform, study design, statistical model, adjustment strategy, validation chain, and bias control. It separates true contradiction from apparent contradiction caused by framing or methods. Never fabricate references, PMIDs, DOIs, trial identifiers, dataset details, platform details, study features, or conflict explanatio...

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.

Organizes the evidence and competitive landscape around a drug, target, or pathway by separating disease relevance, tractability, preclinical evidence, clinical evidence, modality fit, and crowding. Always map what is biologically supported, what is druggable, what has actually advanced, and what remains strategically open. Never confuse target relevance with druggability, preclinical activity with clinical promise, or narrative excitement with validated development maturity. Never fabricate ...

Ranks papers by evidence family, methodological quality tier, validation depth, and claim discipline; assigns anchor, context-setting, mechanistic support, or caution citation roles; prevents prestige-based or design-label-based ranking errors.

Reads a paper figure by figure before re-integrating the full narrative, so the user can identify the core findings quickly and check whether each visual actually supports the authors' main claims. Always separate figure content, figure-linked claim, evidentiary strength, and unsupported interpretation. Never fabricate references, PMIDs, DOIs, figure content, panel labels, result values, or study details that were not actually provided.

Quickly judges whether a biomedical paper is worth deep reading by screening for question fit, design quality, sample adequacy, methodological novelty, and reproducibility value.

Academic paper reading and research development system for biomedical researchers. Finds papers via Semantic Scholar, reads with structured notes, tracks discussion insights, and synthesizes literature into a Research Foundation Document (RFD) for downstream protocol design skills. 8 commands: /setup /feed /read /discuss /recap /update /sync /propose

Identifies real, evidence-audited, topic-specific research gaps in medical research by first retrieving and verifying literature from trusted sources, then mapping the current evidence landscape, rejecting pseudo-gaps, and converting only medium/high-confidence gaps into study-ready research opportunities. Always require real literature retrieval before formal gap claims. Never fabricate references, metadata, or findings.

A medical-research-native literature reading skill for users with clinical, bioinformatics, translational, and basic experimental backgrounds. Use this skill whenever a user wants to read, analyze, critique, or interpret a medical or scientific paper — whether they provide a PDF, abstract, DOI, PMID, or just a title. Triggers include requests like \\\"analyze this paper\\\", \\\"critique this study\\\", \\\"is this a strong paper?\\\", \\\"give me similar studies\\\", \\\"prepare me for journ...

Maps whether a biomedical research topic, subtopic, or study angle is truly saturated, superficially crowded, strategically occupied, or still open for differentiated entry. Use this skill when a user wants to know whether a hot medical research direction is already overworked, whether meaningful whitespace remains, whether major groups have already occupied the obvious claims, and whether the timing window is still open. Always distinguish popularity from true saturation, and distinguish cos...

Detects methodological gaps across study design, analysis, validation, bias control, reproducibility, and implementation readiness within a biomedical research area. Use this skill when a user wants to identify what current studies are still methodologically missing, which weaknesses are most consequential, and what upgrade path would produce a stronger next-step study. Always separate design gaps, analysis gaps, validation gaps, and reproducibility gaps. Never treat technical complexity as m...

Reverse-engineers the methods section of a biomedical paper into a structured, reproducible workflow. Use this skill when a user wants to understand how a study was actually executed, extract data sources, inclusion/exclusion logic, preprocessing, analytical sequence, software/tools, validation path, and critical parameters, or build a replication checklist from a paper, abstract, DOI, PMID, title, screenshot, or partial methods text. Do not treat this as generic summarization. Focus on recon...

Collects candidate biomedical literature across multiple databases, adapts search logic by database, preserves source metadata, and organizes results into a structured, screening-ready candidate pool. Always use this skill when a user wants cross-database literature collection, search strategy construction, candidate paper aggregation, or first-pass evidence organization before deduplication, screening, layered reading, or review planning. Requires real and verifiable literature records only....

Assesses whether a medical research topic is worth starting now by separating true novelty from pseudo-novelty, auditing real feasibility under stated resource constraints, and forcing a concrete start / narrow / redesign / stop decision. Always require explicit assumptions and never fabricate references, datasets, resource availability, precedent studies, or publication claims.

Verifies whether a scientific or biomedical claim is actually supported by the cited original papers rather than by citation drift, overstatement, selective citation, or correlation-to-causation inflation. Use this skill whenever a user wants to check whether a repeated statement, slide claim, manuscript sentence, review assertion, or “people often say” scientific conclusion is truly supported by the underlying primary literature. Always separate the claim itself, the cited paper(s), what the...

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 subgrou...

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".

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.

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.

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.

Extracts concrete unmet clinical needs from guidelines, reviews, real-world studies, and clinical-practice evidence. Use this skill when a user wants to turn broad medical research value into specific clinical pain points such as weak early detection, poor risk stratification, treatment-response heterogeneity, monitoring gaps, diagnostic delay, undertreatment, overtreatment, or implementation failure. Always ground unmet-need claims in retrieved evidence and distinguish true care gaps from ge...

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 d...

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...

Generates complete FAERS pharmacovigilance study designs for multi-drug or class-level safety comparison inside one predefined SOC or AE family using active comparators, disproportionality analysis, subgroup characterization, and reviewer-facing evidence control.

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.

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 associatio...

Generates complete bidirectional multi-phenotype Mendelian randomization research designs from a user-provided exposure family and outcome family. Always use this skill whenever a user wants to design, plan, or build a genome-wide causal-inference study based on publicly available GWAS summary statistics, especially when the article logic includes multiple exposures, multiple outcomes or subtypes, bidirectional MR, IV filtering, IVW as the main estimator, weighted median / MR-Egger / MR-PRESS...

Designs complete integrated research plans for bulk transcriptomics, proteomics, metabolomics, and related omics from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a bulk multi-omics or single-omics-plus-clinical study — including disease-focused, mechanism-focused, biomarker-focused, stratification-oriented, or translational projects. It should define the research question, choose the best-fit study pattern, recommend example...

Design a structured case-control study framework with explicit source population logic, control selection rules, matching decisions, exposure measurement planning, and bias-control checkpoints.