「ship」の検索結果

Create or review revenue operations deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for revenue operations planning or analysis.

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。

Create or review meeting briefing deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for meeting briefing planning or analysis.

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。

Create or review journal entry prep deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for journal entry prep planning or analysis.

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。

Create or review legal risk assessment deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for legal risk assessment planning or analysis.

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。

Create or review lean canvas deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for lean canvas planning or analysis.

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。

Create or review call prep deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for call prep planning or analysis.

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。

Create or review brainstorm experiments new deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for brainstorm experiments new planning or analysis.

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。

Create or review audit support deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for audit support planning or analysis.

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。

Create or review business model deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for business model planning or analysis.

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。

Create or review daily briefing deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for daily briefing planning or analysis.

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。

Create or review close management deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for close management planning or analysis.

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。

Find cross-database references between NCBI databases using Biopython Bio.Entrez (ELink). Use when navigating gene to protein/structure, sequence to publication, PubMed to GEO, BioProject to SRA runs, or discovering all link relationships for a record. Covers linkname semantics, cmd= variants, asymmetric link warnings, neighbor_history for >200 input IDs, and per-database link tables.

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。

Compare gene regulatory and co-expression networks between biological conditions to identify rewired regulatory relationships using DiffCorr. Detects gained, lost, and reversed gene-gene correlations between conditions. Use when comparing co-expression networks between disease vs control, treatment conditions, or developmental stages.

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 and exploration of RNA-seq count matrices before differential expression. Check for outliers, batch effects, and sample relationships. Use when assessing count matrix quality before DE analysis.

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 and analyzes combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Cell Reports 36:109597), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical analysis with MAGeCK MLE interaction terms, and the relationship to single-cell combinatorial Perturb-seq. Use when designing a paralog or pathway-pair screen, choosing between paired-Cas9 (Big Papi) and Cas12a multiplex (Inzolia), interpreting genetic interaction scores, identifying synthetic-lethal targets for drug development, or scaling beyond single-gene CRISPR 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。

Build weighted gene co-expression networks to identify modules of co-regulated genes and relate them to phenotypes using WGCNA and CEMiTool. Detects hub genes and module-trait relationships from bulk or single-cell expression data. Use when finding co-expression modules, identifying hub genes, or relating gene networks to clinical or experimental variables.

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。

Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combining screens for joint analysis, when passage cohort confounds biology, when DepMap-style panels need Chronos with batch covariates, when picking ComBat vs RUV, or when correction harms biology and should be replaced with explicit covariate modeling.

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。

ESM2 protein language model for embeddings and sequence scoring. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai or boltz. For QC thresholds, use protein-qc.

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。

ESM2 protein language model for sequence scoring, embeddings, and plausibility checks. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai1-structure-prediction or boltz-structure-prediction. For QC thresholds, use protein-design-qc.

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 changing web CSS/UI styling so requirements, state ownership, and acceptance checks are locked before edits to prevent rework churn.

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 adding or modifying generator scripts that write files; keeps generated artifacts out of docs/ and aligns script defaults with ownership paths plus CI gate updates.

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 a devopsdays event that is already listed on the site has to be cancelled or postponed, and its pages need to stop advertising dates, registration, and sponsorship.

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 a devopsdays event has sold or confirmed a sponsorship and the sponsor needs to appear on the event's site - covers both sponsors already in the shared directory and brand-new ones needing a logo and data file.

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。

Produce pre-meeting lead intelligence briefs with Firecrawl. Use when the user needs company research, person research, recent news, talking points, pain points, or outreach preparation before a sales call, partnership meeting, investor conversation, or customer interview.

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。