1,005 件 ・ 関連度順
概要と使いどころ
Query historical crypto market data from 0xArchive across Hyperliquid, Lighter.xyz, and HIP-3. Covers orderbooks, trades, candles, funding rates, open interest, liquidations, and data quality. Use when the user asks about crypto market data, orderbooks, trades, funding rates, or historical prices on Hyperliquid, Lighter.xyz, or HIP-3.
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Search real-time news with bias scoring, get live stock/ETF/crypto data with AI analysis, price options with ML models, and synthesize balanced news perspectives via the Helium MCP server.
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。
Auto-activate for uv build, hatch build, PyApp, PYAPP_*, wheel assets, GitHub release matrices, cargo-zigbuild, or python-build-standalone. Not for runtime deployment.
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Use this skill when authoring or debugging GitHub Actions workflows (.github/workflows/*.yml) — e.g. "add CI for this Drupal project on GitHub", "run phpcs/phpstan/phpunit on pull requests", "cache composer dependencies", "deploy over SSH when main is pushed", "why didn't my workflow trigger", or anything involving jobs, matrices, runners, secrets, or reusable workflows.
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Researches state-specific charitable solicitation filing requirements keyed to gross revenue, producing a citation-backed state-by-state compliance matrix of audit thresholds, financial-statement tiers, filing fees, and due dates. Use when building charity compliance matrices, researching nonprofit filing thresholds, charitable solicitation registration renewals, comparing state audit requirements for 501(c)(3) organizations, or when the user mentions audit threshold, review threshold, compilation requirement, charity filing deadline, or multi-state nonprofit compliance.
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。
Drafts a U.S. CRE personal property Bill of Sale transferring equipment, fixtures, FF&E, inventory, and other tangible assets. Handles inclusion/exclusion schedules, price allocation, tax responsibility, and risk of loss. Trigger when the user needs a bill of sale, asset schedule, equipment transfer document, or personal property conveyance tied to a CRE closing.
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。
Drafts a U.S. Asset Purchase Agreement for deals where a buyer acquires specified business assets while avoiding unwanted liabilities. Enforces itemized asset/liability schedules, purchase price mechanics with working capital true-ups, IRS Form 8594 tax allocation, seller and buyer reps and warranties with disclosure schedules, pre- and post-closing covenants, closing conditions and deliveries, and indemnification with basket/cap limits. Use when drafting an asset purchase agreement, APA, asset acquisition, purchase and sale of assets, asset deal, or excluded liabilities agreement.
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Drafts a U.S. asset purchase letter of intent (LOI) separating binding from non-binding terms and capturing deal points, diligence, exclusivity, and closing path. Trigger on "letter of intent", "LOI", "asset purchase", "M&A LOI", "no-shop", "exclusivity", "purchase price", "due diligence".
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 visuals that sell a specific offer and value proposition. Translates an offer (price, bundle, guarantee, bonus, deadline) into hero visuals that make the value legible at a glance. Use when the user says "visualize the offer", "promo creative", "make the deal look good", "value prop hero", or has a concrete offer to merchandise. Part of the Creative Production set.
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 designing matrix builds (OS x version x dimension), composing reusable workflows, gating jobs on conditions, doing OIDC to AWS/GCP/Cloudflare without long-lived secrets, sharding tests across runners, or fixing cache invalidation. Triggers: workflow_call between repos, fail-fast: false tradeoffs, exclude/include matrix surgery, environment protection rules, OIDC trust policy setup, concurrency cancellation on duplicate pushes, artifact reuse across jobs, conditional job matrices computed at runtime. NOT for GitLab CI / CircleCI / Jenkins (different yaml dialects), self-hosted runner administration, or local act dev.
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Applies game theory to multi-agent coordination protocols with advisory claims, repeated interactions, daemon-mediated correlation, and deviation penalties. Use for Nash equilibrium checks, folk-theorem cooperation analysis, correlated-equilibrium routing, and price-of-anarchy audits in strategic agent systems. NOT for consumer pricing, card-game strategy, or general microeconomics outside agent coordination.
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。
Imports gene expression count matrices from featureCounts, HTSeq, STAR ReadsPerGene, Salmon/kallisto via tximport or tximeta, RSEM, 10X Genomics MTX/H5, AnnData H5AD, and RDS. Handles silent-miscounting traps (featureCounts -p v2.0.2 API break, STAR strandedness column choice, salmon NumReads-sum without tximport, RSEM non-integer expected_count, GENCODE _PAR_Y suffix, zero-length-transcript TPM divide-by-zero), and encodes the tximport countsFromAbundance decision tree with the "lengthScaledTPM is not TPM" warning. Use when assembling a gene-by-sample count matrix from aligner or quantifier output, importing salmon/kallisto for DESeq2 vs limma-voom, choosing strandedness column for STAR, debugging zero-count panics, or building tx2gene mapping.
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Normalizes and transforms RNA-seq count matrices for DE, visualization, clustering, and ML. Covers between-sample (TMM, TMMwsp, RLE/median-of-ratios, upper quartile), within-sample (TPM, FPKM/RPKM), variance-stabilizing (VST, rlog, log-CPM), GC-content correction (cqn, EDASeq), and single-cell (scran deconvolution, scanpy normalize_total). Encodes the composition-bias rationale, the "most genes not DE" assumption and its catastrophic failure modes (MYC amplification, apoptosis, viral host shutoff, prokaryotic stress), the "lengthScaledTPM is not TPM" naming trap, the "TPM is not for DE" rule, the blind=TRUE vs FALSE decision, ERCC spike-in normalization (SBN), and the single-cell zero-inflation breakdown of TMM/RLE. Use when choosing or applying normalization, debugging shifted-MA-plot diagnostics, handling zero-heavy single-cell data, or correcting GC bias.
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。
Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.
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 OncoPrint and co-mutation matrix plots from somatic-variant cohorts using ComplexHeatmap, maftools, and comut.py with alteration-type stacking, sample ordering by mutational burden, mutual-exclusivity overlays, and clinical annotation tracks. Use when visualizing per-sample mutation patterns across recurrent driver genes, comparing alteration classes, or identifying mutually-exclusive / co-occurring driver pairs.
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。
Compute genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.
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。
Model gene-family birth-death dynamics across a species tree using CAFE5 (Mendes et al 2020 Bioinformatics 36:5516 gamma-distributed rate categories), CAFE5-error (annotation-error-aware), Count (Csurös 2010 ancestral state reconstruction), BadiRate (Librado 2012 likelihood + parsimony), DupliPHY-Family, and ALE/AleRax (for per-family DTL; see [[gene-tree-species-tree-reconciliation]]). Test lineage-specific gene-family expansions and contractions, distinguish biological dynamics from annotation artifacts, account for assembly fragmentation, identify functional enrichment in expanded / contracted families. Use when correlating gene-family changes with phenotype evolution, ranking lineages by adaptive gene-family-rate shifts, post-WGD dosage-balance analysis, or building Birth-death models from OrthoFinder presence/absence 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。
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。
Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.
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。
Calculate alignment statistics including sequence identity, conservation scores, substitution matrices, and similarity metrics. Use when comparing alignment quality, measuring sequence divergence, and analyzing evolutionary patterns.
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。
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
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。
Bedside prescribing reference for Bemdec (bempedoic acid / NEXLETOL) — indication check, dose, statin co-prescribing safety caps, monitoring plan, warnings for hyperuricemia and tendon rupture, and special population guidance. Use when a clinician asks "can I start bempedoic acid", "Bemdec indication", "is bempedoic acid safe with my statin", "patient on Nexletol has joint pain or high uric acid", "bempedoic acid in CKD or liver disease or pregnancy", or any prescribing or monitoring question about bempedoic acid.
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。
Extract product data, prices, reviews, and seller information from any e-commerce platform using Apify's E-commerce Scraping Tool.
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。
Query Polymarket: markets, prices, orderbooks, history.
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。