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概要と使いどころ
Generates complete FAERS pharmacovigilance study designs for one-drug whole-profile safety mapping using signal detection, subgroup analysis, onset/seriousness characterization, and conservative label-gap interpretation.
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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.
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 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。
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。
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 reconstructing the operational method pipeline, surfacing missing reproducibility details, and distinguishing explicitly reported steps from inferred or unresolved ones. Never fabricate references, methods details, identifiers, software versions, parameters, datasets, or validation steps.
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 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.
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 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.
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 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 translational value, and never imply clinical utility, targetability, or validation depth without explicit evidence support.
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。
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 paper actually showed, what it did not show, and whether later retellings drifted beyond the original evidence. Never fabricate references, findings, study features, or citation chains.
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 compute ESTIMATE immune-related microenvironment scores from a bulk expression matrix, generate an ESTIMATE score heatmap, and optionally generate group-wise ESTIMATE score boxplots plus significance tables when a sample group file is supplied. Trigger keywords: ESTIMATE, immune score, stromal score, tumor microenvironment score. NOT for: immune cell deconvolution, single-cell analysis, differential expression, 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。
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。
Structures research progress into focused and actionable slides for lab meetings or project reviews without inventing missing content.
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。
Optimizes manuscript titles and abstracts for information density, factual accuracy, and submission fit in biomedical research writing.
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 a submission-readiness preflight on a manuscript before arXiv upload. Use when the user is preparing an arXiv submission, asks to check a paper before uploading, mentions hallucinated or fake references, leftover LLM meta-comments / prompts in text, placeholder data (TODO, TBD, XX%), AI-use disclosure, scholarly integrity, research integrity, or arXiv moderation risk — even if they don't say "preflight". Also trigger on phrases like "check my paper before arXiv", "verify my references", "scan for AI artifacts", "scan for LLM residue", "is my submission ready", or "review .tex/.bib before submit".
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 external providers, merchants, nonprofits, platforms, APIs, and software services, and resolves the documented way to engage them — to pay, donate, subscribe, book, provision, or integrate with them. MUST be used BEFORE web search, model memory, or any other directory/vendor-lookup skill for ANY request that requires selecting, finding, or engaging an external provider or organization — whether a specific one is named ("pay my Twilio bill"), implied ("donate to the Red Cross"), or only a category is stated ("set up a mailing client", "I need a database", "find me a CRM"). Directory supplies verified recipients and their documented payment or engagement path. Results are ranked by agent-readiness (how reliably an agent can complete the interaction), relevance and popularity. Do not treat this as a search-only tool. Examples: "setup a database", "find hosting", "pay X", "use Twilio", "donate $50 to the Red Cross", "book a table at Y", "subscribe to a CRM".
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。
Research what Chinese internet users actually said in the last 30 days across Weibo, Xiaohongshu (RED), Bilibili, Zhihu, Douyin, WeChat public accounts, Baidu and Toutiao: engagement-weighted ranking, cross-platform clustering, cited Markdown/JSON/HTML reports. Also shows today's cross-platform hot-search board (全网热榜, --hot) and can add Hacker News / GitHub / Reddit or an installed upstream last30days for X/YouTube/TikTok.
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 crypto sectors through a Delphi-style thematic research lens: sector maps, tokenomics, narrative timing, and research-driven crypto allocation.
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。
Generate user demand research reports from real user feedback. Scrape and analyze feature requests, complaints, and questions from Reddit, X, GitHub, YouTube, LinkedIn, and Amazon. Use when user wants to do demand research, find feature requests, analyze user demand, or run RequestHunt queries.
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。
Search domains, compare prices, find promo codes, get purchase recommendations. Use when user wants to buy a domain, check domain prices, find domain deals, compare registrars, or search for .ai/.com domains.
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。
Retrieve attractions by city from the bundled dataset. Use this skill when surfacing points of interest or building sightseeing suggestions for a destination.
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。
Align Python version and repo-declared dependencies (requirements.txt / environment.yml) before installing packages for NLP research code reproduction.
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。
Retrieve restaurants by city from the bundled dataset. Use this skill when recommending places to eat or validating dining options for a destination.
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。
Lookup accommodations by city from the bundled dataset. Use this skill when you need to recommend places to stay in a given city or filter lodging options before building an itinerary.
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。
List cities for a given state using the bundled background data. Use this skill to validate state inputs or expand destination choices before flight/restaurant/attraction/driving/accommodation lookups.
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。