299 件 ・ 関連度順
概要と使いどころ
Detects differential alternative splicing between conditions using rMATS-turbo (BAM-based) or SUPPA2 diffSplice (TPM-based). Reports events with FDR-corrected significance and delta PSI effect sizes. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
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Find differentially accessible chromatin regions between conditions using DiffBind or DESeq2. Use when comparing chromatin accessibility between treatment groups, cell types, or developmental stages in ATAC-seq experiments.
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Provide US dividend tax and account-location workflow for Kanchi-style income portfolios. Use when users ask about qualified vs ordinary dividends, 1099-DIV interpretation, REIT/BDC distribution treatment, holding-period checks, or taxable-vs-IRA account placement decisions for dividend assets.
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Use this skill when the user asks about Ni Haisha / 倪海厦 TCM course material, especially Shang Han Lun / 伤寒论, Jingui / 金匮要略, Zhongjing Xinfa / 仲景心法, clinical cases / 临床案例 / 倪师医案, Bagang Bianzheng / 八纲辨证, Fuyang Forum / 扶阳论坛, Yijinjing / 易筋经, Liang Dong dialogue / 梁冬对话倪师, Stanford lecture / 斯坦福大学演讲, Tianji / 天纪 / 易经 / 阳宅 / 紫微斗数, Huangdi Neijing / 黄帝内经, Shennong Bencao / 神农本草, acupuncture / 针灸, meridians, acupoints, acupuncture depth safety / 危险进针深度 / 气胸风险, six-channel pattern identification, symptom-to-formula routing, formula comparison, lesson review, board/PPT screenshot evidence, or course-derived study notes. This skill is for educational distillation and study support only, not medical diagnosis, prescriptions, dosage, individualized treatment, or self-administered acupuncture.
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Configure a Next.js app using posthog-js so session recording and dead-click capture survive browser ad blockers. Aliases the three blocker-flagged JS chunks (dead-clicks-autocapture.js, recorder.js, posthog-recorder.js) to bland same-origin paths via Next.js rewrites, and monkey-patches HTMLScriptElement so posthog-js requests the aliases. TRIGGER when posthog-js session replay or dead-click capture is failing in production with ERR_BLOCKED_BY_CLIENT or 404s on /static/dead-clicks-autocapture.js / /static/recorder.js / /static/posthog-recorder.js, or when the user wants to bypass ad blockers for these specific assets. Works alongside an existing PostHog reverse proxy (managed or otherwise) — only the three blocked files divert through Next.js, all other PostHog traffic stays on the original api_host. SKIP for unrelated PostHog setup, non-Next.js apps, or when capture/decide endpoints (/e/, /decide/, /array/*/config.js) are the ones being blocked — those need a different treatment.
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Applies a deliberate visual direction to an interface — minimalist editorial, industrial utilitarian, or high-polish commercial — each with its own type scale, palette behavior, surface treatment, and motion. Use this when a product needs a point of view rather than defaults, when choosing between visual directions, when an interface reads as generic, or when restyling something without changing its structure.
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Identifies, assesses, and tracks organizational risk — building and maintaining a risk register, scoring exposure, assigning owners and treatments, and preparing for audit. Use this to stand up a risk program, assess the risk in a decision or initiative, prepare for a certification or audit, decide whether a risk should be accepted, mitigated, transferred, or avoided, or report risk posture to leadership.
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Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). ...
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Designs a translational blueprint for moving a biomedical finding toward diagnosis, prognosis, treatment response prediction, patient stratification, or therapeutic development, with explicit translational milestones, validation thresholds, and feasibility-sensitive route framing.
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Designs complete research plans that integrate clinical variables with multi-omics data from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a study that combines clinical variables with transcriptomics, proteomics, metabolomics, epigenomics, or related omics layers for mechanism interpretation, biomarker development, risk stratification, treatment-response analysis, or translational use. It should define the clinical use case, alignment across data layers, feature-reduction and fusion logic, modeling route, mechanism-interpretation layer, validation ladder, and four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, accession numbers, sample counts, metadata completeness, platform coverage, literature references, PMIDs, DOIs, or validation status. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.
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Designs discovery, modeling, and validation workflows for prognostic biomarkers in biomedical and clinical research. Always use this skill when the user needs a prognostic biomarker study blueprint rather than a diagnostic test protocol, predictive biomarker design, treatment recommendation, or a completed manuscript. Focus on endpoint family, follow-up horizon, time scale, candidate marker strategy, model-building logic, risk stratification framework, and internal/external validation requirements. Do not invent cohort size, event rate, assay readiness, literature support, or validation access.
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Refines broad, vague, or aspirational biomedical research objectives into clear, bounded, measurable, executable, and downstream-ready study objective statements. Always use this skill when a user has a general aim such as “explore a mechanism,” “study prognosis,” “investigate biomarkers,” or “look at treatment response,” but the objective is still too broad, non-operational, or too ambiguous to support protocol framing, design selection, analysis planning, or hypothesis design. Never assume that polished wording alone means the objective is actionable. Focus first on objective type, missing operational elements, scope discipline, and downstream-ready formulation.
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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 present a basic finding as clinically actionable unless the evidence supports that level.
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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.
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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.
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Use when asked 医保怎么报销, 异地就医怎么备案, 住院能报多少, 门诊报销比例, 手工报销要什么材料, 惠民保值不值得买, or work out what China's basic medical insurance (医保) will pay for a treatment. Produces the person's scheme (职工医保 or 居民医保), how a claim works for this case (direct settlement or manual reimbursement), an out-of-pocket estimate built from the city's own deductible, rate and cap, the cross-region registration (异地就医备案) steps, and the documents to keep.
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Prepare for China's annual individual income tax reconciliation (个税年度汇算): check income, deductions and special additional deductions, compare the two treatments of a year-end bonus, and estimate the refund or amount to pay before filing in the 个人所得税 app. Use when asked 个税汇算怎么弄, 年终奖单独计税还是合并, 我能退多少税, 专项附加扣除, or how does China's annual tax reconciliation work. Produces a filing checklist, the deductions the person may be missing, a bonus comparison, the estimate with workings, and deadlines. Not tax advice.
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Check whether you're burning out as a caregiver — and get a realistic plan to protect yourself before you can't keep going. Use when asked I'm exhausted from caregiving, am I burning out caring for my parent, caregiver stress help, or I have nothing left to give. Produces an honest read on your burnout signs and how depleted you are, permission and reasons to accept help (which caregivers resist), specific ways to get support and respite, the guilt and identity traps to address, and a realistic self-care plan that fits a caregiver's actual life — because a caregiver who collapses can't care for anyone. Not medical or mental-health treatment.
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Use when the identification argument is the bottleneck for an American Economic Review: Insights (AER: Insights) short-format manuscript — causal identification in an empirical design, parameter identification in a structural model, or treatment-effect identification in an experiment. Stress-tests the strategy so it is clean enough to defend in a few pages, before exhibits are finalized.
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Use when building figures and tables for an Agricultural Systems (AgSy) manuscript so exhibits communicate interactions, dynamics, trade-offs, and model performance clearly. AgSy is a systems journal, so the best exhibits show trade-off frontiers, observed-vs-simulated fit, resource flows, and conceptual system diagrams — not just a bar chart of one treatment. Designs exhibits; it does not run the analysis.
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Compares Hi-C contact maps between conditions across the right scale -- differential bin-pair contacts (multiHiCcompare, diffHic), differential A/B compartments (dcHiC), differential TAD boundaries (delta insulation), and differential loops (diffloop, DiffHiChIP) -- with distance-stratified between-sample normalization, replicate-aware NB-GLM FDR, HiCRep SCC reproducibility gating, and CNV correction for cancer/aneuploid samples. Use when comparing Hi-C between treatment and control, finding differential contacts/compartments/boundaries/loops, normalizing two maps of unequal depth, choosing a replicate-aware test, gating replicates with SCC, or correcting copy-number artifacts before a tumor-vs-normal comparison.
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Tracks ctDNA across serial liquid-biopsy timepoints for molecular residual disease (MRD) and treatment-response monitoring, treating MRD as a binary integrated detection call across the patient's full variant set (with a defined LoD95 and per-sample specificity) rather than a per-timepoint VAF threshold, and handling undetectable samples as left-censored at the per-sample limit of detection rather than true zeros. Covers tumor-informed bespoke vs tumor-naive design, landmark vs surveillance sampling, molecular-response definitions and their non-standardization, censoring-aware clearance kinetics, and the multiple-testing structure of repeated surveillance. Use when monitoring ctDNA during therapy, calling molecular relapse before imaging, or estimating clearance half-life from serial samples.
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Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.
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Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-bias-aware differential via its bam_ip/bam_input control + bam_treated_ip/bam_treated_input treatment arms), QNB beta-binomial, MeTDiff HMM, and RADAR, plus the paired-symmetric edgeR/DESeq2-on-peak-counts route when batch/lot covariates need fixed-effect handling that exomePeak2's API does not accept. Covers paired vs unpaired vs interaction designs, batch confounding and per-lot meta-analysis, the stoichiometry-vs-expression-vs-IP-efficiency confound, and effect-size filtering against under-powered N=2 designs. Use when comparing m6A across two or more conditions, choosing between exomePeak2/QNB/RADAR/MeTDiff for a design, handling batch confounding when exomePeak2's API is too rigid, distinguishing real hyper/hypo-methylation from expression shifts, applying effect-size thresholds, or planning orthogonal stoichiometry validation (GLORI/SAC-seq/m6Anet mod_ratio).
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