4,288 件 ・ 関連度順
概要と使いどころ
Statistical analysis for metabolomics data. Covers univariate testing, multivariate methods (PCA, PLS-DA), and biomarker discovery. Use when identifying differentially abundant metabolites or building classification models.
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Taxonomic classification of metagenomic reads using Kraken2. Fast k-mer based classification against RefSeq database. Use when performing initial taxonomic classification of shotgun metagenomic reads before abundance estimation with Bracken.
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Predict peptide-MHC class I and II binding affinity using MHCflurry and NetMHCpan neural network models. Identify potential T-cell epitopes from protein sequences. Use when predicting MHC binding for vaccine design or neoantigen identification.
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Differentially methylated region (DMR) detection using methylKit tiles, bsseq BSmooth, and DMRcate. Use when identifying contiguous genomic regions with methylation differences between experimental conditions or cell types.
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Profile functional potential of metagenomes using HUMAnN3 and similar tools. Use when obtaining pathway abundances, gene family counts, or functional annotations from metagenomic data.
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Perform differential expression analysis using DESeq2 in R/Bioconductor. Use for analyzing RNA-seq count data, creating DESeqDataSet objects, running the DESeq workflow, and extracting results with log fold change shrinkage. Use when performing DE analysis with DESeq2.
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Perform differential expression analysis using edgeR in R/Bioconductor. Use for analyzing RNA-seq count data with the quasi-likelihood F-test framework, creating DGEList objects, normalization, dispersion estimation, and statistical testing. Use when performing DE analysis with edgeR.
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Extract, filter, annotate, and export differential expression results from DESeq2 or edgeR. Use for identifying significant genes, applying multiple testing corrections, adding gene annotations, and preparing results for downstream analysis. Use when filtering and exporting DE analysis results.
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Remove batch effects from RNA-seq data using ComBat, ComBat-Seq, limma removeBatchEffect, and SVA for unknown batch variables. Use when correcting batch effects in expression data.
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Analyze time-series RNA-seq data using limma voom with splines, maSigPro, and ImpulseDE2. Identify genes with dynamic expression patterns. Use when analyzing time-series or longitudinal expression data.
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Spillover compensation and data transformation for flow cytometry. Covers compensation matrix calculation, application, and biexponential/arcsinh transforms. Use when correcting spectral overlap between fluorophores or transforming data for analysis.
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Predict CRISPR off-target sites using Cas-OFFinder and CFD scoring algorithms. Identify potential unintended cleavage sites genome-wide and assess guide specificity. Use when evaluating guide RNA specificity or selecting guides with minimal off-target risk.
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Statistical methods for calling hits in CRISPR screens. Covers MAGeCK, BAGEL2, drugZ, and custom approaches for identifying essential and resistance genes. Use when identifying significant genes from screen count data after QC passes.
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JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens) for modeling sgRNA efficacy and gene essentiality. Use when analyzing multiple CRISPR screens simultaneously or when accounting for variable sgRNA efficiency across experiments.
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MAGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout) for pooled CRISPR screen analysis. Covers count normalization, gene ranking, and pathway analysis. Use when identifying essential genes, drug targets, or resistance mechanisms from dropout or enrichment screens.
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。
Quality control for pooled CRISPR screens. Covers library representation, read distribution, replicate correlation, and essential gene recovery. Use when assessing screen quality before hit calling or diagnosing poor screen performance.
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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
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Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.
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Review closed trades, partial exits, and monthly trade aggregates for process adherence, risk discipline, execution quality, and evidence-based trading behavior patterns. Use after trader-memory-core and signal-postmortem have produced records, or when the user asks for a post-trade coach, risk-manager style review, rule-adherence review, next-session operating rules, or psychology-aware trading behavior feedback. This skill does not provide buy/sell advice, therapy, or broker execution.
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Operate GJC SDK sessions from the CLI (`gjc sdk session list|inspect|send|status|tail|close|raw` plus the explicit raw control|query|global hatch). Advisory reference: broker-bound, credential-free output; mutating verbs run only when explicitly invoked.
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Use when the user asks to "design our change narrative", "build the old-world-to-new-game story arc", or "frame the shift our category is undergoing"; produces a Raskin-style strategic narrative arc — old world → the undeniable shift → winners vs losers under the new rules → the promised land → proof the promised land is real — derived from the positioning truth set and the belief/objection map, feeding the TALE Architecture arc sub-item. Every proof beat is labeled Measured / User-provided / [needs source] and this skill never adjudicates a claim. Not for the message house hierarchy — use message-system-architect; not for the pitch deck narrative — use pitch-narrative-builder; not for claim substantiation — use offer-claims-registry. 变革叙事弧/旧世界/应许之地/战略叙事结构
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Use when the user asks to "audit our brand narrative" or "is this message on-canon"; runs separate typed TALE truth, system, or effectiveness profiles and never averages them into one composite. Checks differentiation, canon, landing consistency, and evidence integrity. Not for launch readiness — use launch-readiness-auditor; not for social operations — use social-quality-auditor. 品牌叙事分层审计/发布前一致性放行
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Use when the user asks to "check pacing", "am I over/under-spending", "is this campaign on track to hit budget", or "why did spend spike/stall mid-flight"; returns a spend-vs-target-curve read, learning-phase status, an over/under-delivery call, and a reallocation trigger. Not for initial budget allocation — use budget-optimizer; not for choosing the bid strategy — use bid-strategy-planner; not for the RQS gate — use ad-account-auditor. 付费广告预算节奏监控/跑量过快过慢/在途配速
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Use when the user asks to "run a deliverability pre-flight before I send", "check my SPF/DKIM/DMARC/BIMI", "why am I landing in spam / promotions", or "score my sender reputation and list hygiene"; runs the ONE-TIME pre-send SEND S1 authentication pre-flight and builds the SEND S (Sender-integrity / Deliverability) evidence read — DNS + DMARC-RUA auth, domain/IP reputation, inbox placement, content/link/render, and point-in-time bounce/complaint hygiene — using Pass/Partial/Fail/Unknown/N/A states and scoring only at complete applicable coverage. Not for the recurring hygiene trend — use list-hygiene-monitor; not for final EQS or veto verdicts — use email-quality-auditor; not for segments/suppression lists — use list-segment-builder. 邮件送达率预检/SPF DKIM DMARC认证/发件域声誉
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。