調査、論文、教育、資格学習（4,142 件）
概要と使いどころ
Create interactive AI role-play training activities as self-contained HTML. Use when the user asks to generate a role-play, persona practice, sales simulation, customer conversation drill, compliance scenario, or any learner-vs-AI-character training exercise.
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Save important context to persistent memory for future sessions. PROACTIVELY USE THIS when you detect: - Decisions: "let's go with", "decided to", "chose", "went with" - Learnings: "I learned", "turns out", "TIL", "realized", "discovered" - Errors resolved: "fixed", "the problem was", "solution was" - Client info: names, emails, phone numbers, addresses, invoice amounts - Preferences: "I prefer", "I like", "don't like", "always use" - References: API keys locations, file paths, URLs, credentials - Patterns: "pattern", "convention", "always do", "standard approach" - Important commands run worth remembering Do NOT ask permission - just capture silently when these patterns appear.
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Generate and save a session summary before ending conversation. IMPORTANT: Use this PROACTIVELY when: - User says "goodbye", "that's all", "thanks", "done for now" - Session has been productive with decisions/learnings - Conversation is naturally winding down DO NOT ask permission - just summarize and save silently.
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Map unfamiliar code or research an approach before planning, read-only and fast.
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Save a project fact, decision or gotcha so your next session already knows it.
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Close out a session — summarise what changed, capture what was learned, and prepare the commit.
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Research complex questions and synthesize relevant evidence into a clear, cited answer. Useful for competitive and market analysis, company research, prospect discovery, enrichment, historical comparisons, and evaluating conflicting claims. Adapt the depth and format to the question; use when a focused search or single-page read is insufficient.
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Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning over DRACH 5-mer signal). Covers the upstream pipeline (Dorado/Guppy basecalling -> minimap2 map-ont -> nanopolish eventalign -> m6anet dataprep -> m6anet inference), per-site vs per-read probability including the mod_ratio stoichiometry column, the DRACH-only constraint, minimum-coverage thresholds (20-50 reads/site), multi-condition comparison via xPore/Nanocompore/ELIGOS, Dorado native modification calling (RNA004, 2024+), and the cDNA-vs-DRS distinction (cDNA Nanopore CANNOT detect modifications). Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting probability_modified vs mod_ratio vs per-read probabilities, deciding between m6Anet (known DRACH sites) and Dorado/Remora (genome-wide screening), pinning RNA002 vs RNA004 chemistry and basecaller versions, or troubleshooting eventalign/dataprep failures.
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Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.
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Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2024 bioRxiv; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.
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Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or Enformer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.
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Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Search arXiv preprint repository for papers in physics, mathematics, computer science, quantitative biology, and related fields
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Suggests encouraging patients to use appropriately adjusted embedded bolus calculators in CSII and to provide education regarding their use and limitations. Triggers include: 'Patient starting pump, discuss bolus calculator', 'Review bolus calculator education at pump initiation'.
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Suggests that adults with type 1 or type 2 diabetes using continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) receive education, training, and ongoing support to achieve and maintain individualized glycemic goals. Trigger phrases include: 'Patient on pump plus CGM, arrange education' and 'Ensure CSII+CGM users get training and support'.
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This skill recommends glucocorticoid replacement and patient education for hypocortisolemic patients after surgical remission of Cushing's syndrome. Use when a postoperative Cushing's patient exhibits low cortisol (e.g., morning cortisol <5 µg/dL) or symptoms of adrenal insufficiency.
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This skill provides structured education to patients and families about Cushing's syndrome pathophysiology, treatment options, and post-remission expectations. It is triggered when a clinician encounters a new Cushing's syndrome patient or family needing disease information, such as at initial diagnosis or before treatment initiation.
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This skill recommends that a multidisciplinary team, including an experienced endocrinologist, incorporates patient values and preferences while providing education about treatment options for Cushing’s syndrome. Use when planning treatment for a CS patient requiring coordinated care; triggers include persistent hypercortisolism after transsphenoidal surgery, consideration of bilateral adrenalectomy, radiotherapy, or medical therapy, and need for shared decision‑making.
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Educates patients with primary adrenal insufficiency on increasing glucocorticoid dosage during intercurrent illness, fever (≥38°C), or physiological stress, including recognition of precipitating symptoms (e.g., fever, vomiting, trauma) and actions to prevent impending adrenal crisis. Trigger phrases: fever, intercurrent illness, stress, vomiting, inability to tolerate oral meds, need for parenteral glucocorticoid.
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This skill outlines the emergency management of suspected adrenal crisis, recommending immediate parenteral hydrocortisone (100 mg adult, 50 mg/m² pediatric) plus fluid resuscitation, then 200 mg/24 h (50–100 mg/m²/24 h pediatric) hydrocortisone. Trigger when patient presents with hypotension, hyponatremia, hyperkalemia, or unexplained shock suggestive of adrenal insufficiency.
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Clinicians educate adrenal insufficiency patients taking nondexamethasone glucocorticoids who initiate enzyme-inducing antiepileptic drugs about early signs of adrenal insufficiency. Trigger when an AI patient on nondexamethasone GC starts an enzyme-inducing AED such as phenytoin, carbamazepine, or oxcarbazepine.
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Teaches AI patients about stress-dosing and emergency glucocorticoid administration; instructs to obtain emergency card/bracelet/necklace and kit with injectable high-dose GC. Triggers when counseling any patient with adrenal insufficiency (e.g., "Use when counseling patient with adrenal insufficiency...").
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。