「stats」の検索結果

Perform comprehensive technical reviews of Ruby on Rails applications. Runs automated analysis tools (RubyCritic, Brakeman, bundler-audit, Gitleaks, Debride, linters, SimpleCov, Rails stats, Rails ERD), analyzes code for architecture, security, authorization (Pundit/CanCanCan), dead code, and design issues, and produces a structured markdown report with prioritized findings and a 0-10 score. Use when the user requests a tech review, code audit, project assessment, or quality analysis of a Rails application.

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。

Summary statistics, data exploration, and descriptive analysis. Use for: summary stats, describe data, data exploration, distributions, means, medians, correlations, cross-tabs.

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。

Authenticate with the Openapi marketplace (oauth.openapi.com). Use when you need to create, inspect, renew or delete Bearer tokens, list available scopes, check wallet credit, subscriptions, or API usage stats. Required by every other openapi-* skill.

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。

Processes eDNA metabarcoding from raw paired-end reads to species tables, navigating ASV (DADA2, UNOISE3) vs OTU (swarm v2) decision (Callahan 2017 vs Schloss multi-copy-16S critique), marker/primer choice (Leray COI, MiFish 12S, 515F/806R 16S, ITS2) with primer-specific bias, OBITools3 v3 command-name break (obi stats plural; .tar.gz taxonomy), tag-jumping with dual-indexing (Schnell 2015; NovaSeq 10x MiSeq), decontam as screening-not-classifier (Davis 2018), read-counts-not-abundance critique (Lamb 2019), site-occupancy modeling (Ficetola 2015), Naive-Bayes calibration limits (Bokulich 2018), and eDNA decay (Strickler 2015). Use when going from raw eDNA FASTQ to species tables, picking marker + denoising pipeline, deciding whether read counts represent abundance, applying occupancy modeling, configuring OBITools3 v3, or interpreting decontam output. Not for clinical 16S microbiome (see microbiome/amplicon-processing).

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。

Estimates SNP heritability and partitions it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LDAK enrichment estimates, computing local heritability with HESS, estimating genetic correlation between traits, or producing publication-grade enrichment with calibrated sensitivity to model assumptions.

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。

Performs gene-level association from GWAS summary statistics via genetically predicted tissue expression using FUSION, PrediXcan, S-PrediXcan, S-MultiXcan, UTMOST, MOSTWAS, kTWAS, EpiXcan, TIGAR-V2, and probabilistic fine-mapping with FOCUS and MA-FOCUS. Use when running TWAS from GWAS sumstats, prioritising candidate causal genes from a GWAS lead locus, picking single-tissue vs cross-tissue models, identifying LD-induced TWAS false positives, choosing ancestry-matched prediction weights, fine-mapping co-regulated TWAS hits, or triangulating TWAS with cis-eQTL Mendelian randomization and colocalization to nominate a causal gene.

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。

Generate alignment statistics using samtools flagstat, stats, depth, coverage, and mosdepth. Use when assessing alignment quality, calculating coverage, or generating QC reports.

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。

Search 8,400+ AI and ML jobs across 489 companies, inspect listings and employers, match roles, and view salary and market stats via AI Dev Jobs MCP

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 Lingzao creator-content tools for Xiaohongshu/XHS, Douyin, and WeChat official-account public content. Lingzao supports XHS SEO, Xiaohongshu account analysis, benchmark account discovery, viral note breakdown, keyword research, one-stop content packages from keyword/link/image/inspiration material, title and cover optimization, hand-drawn route-map cards, city food maps, travel itinerary maps, life checklist maps, workflow manual maps, creator content workflow, note search, creator search, profile lookup, recent posts, deep profile copy/subtitle analysis, note/article detail, post comments, article stats, related articles, short-video copy extraction, and prompt-based creator image generation.

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。

Search 8,400+ AI and ML jobs across 489 companies, inspect listings and employers, match roles, and view salary and market stats via AI Dev Jobs MCP

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。

Mine Claude Code session data for analytics — token usage, tool rankings, skill invocations, session search, and prompt history. Use when asked about session stats, token costs, tool frequency, skill usage patterns, or finding past sessions by prompt text.

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。

Collective research memory for AI coding agents — search prior research before hitting the web, save findings back to the shared cache, view karma stats. Use when the user asks a library/API/framework question, before running WebSearch/WebFetch, or after deep web research so the next agent doesn't repay the cost. Talks directly to the hosted MCP endpoint via JSON-RPC — no local MCP server required.

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。

Run a comprehensive health check on a single Estuary task by combining catalog status, data flow stats, recent errors, and publication history. Use when diagnosing a task that seems broken, slow, or stuck. Use when user says "is my task healthy", "something seems wrong", "check my capture", "check my materialization", "why is data not flowing", "task health check", "diagnose my task", or "what's wrong with my pipeline".

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。

SEC EDGAR filings, BLS employment stats, and USDA crop prices. 3 tools for federal financial intelligence.

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。

Search 8,400+ AI and ML jobs across 489 companies, inspect listings and employers, match roles, and view salary and market stats via AI Dev Jobs MCP

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 working with Appwrite — appwrite backend platform management covering project inventory, database collections, user authentication stats, storage bucket usage, function deployments, webhook configuration, and platform health. Use for comprehensive Appwrite project assessment and resource monitoring.

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 for session archiving, search, restore, and stats.

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。

End-to-end proteomics workflow from MaxQuant output to differential protein abundance. Orchestrates data import, normalization, imputation, and statistical testing with limma (default) or MSstats for complex feature-level designs. Use when processing mass spectrometry proteomics.

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。

End-to-end eDNA metabarcoding from raw amplicons to community ecology. Covers QC, primer removal (mandatory before DADA2 filterAndTrim), denoising with OBITools3 v3 (obi stats plural; DMS-based) or DADA2 ASVs (Callahan 2017), decontam combined method as screening-not-classifier (Davis 2018), tag-jumping with NovaSeq 10x MiSeq caveat (Schnell 2015), Hill-number effective species counts with coverage-based rarefaction (Jost 2006; Chao & Jost 2012; doubling rule), beta-diversity decomposition with MANDATORY PERMANOVA + PERMDISP pair (Anderson & Walsh 2013), constrained ordination, and the read-counts-not-abundance critique (Lamb 2019). Use when processing eDNA samples for biodiversity assessment, deciding ASV vs OTU, configuring OBITools3 v3, interpreting decontam screening, or reporting community comparisons with the dispersion confound check.

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。

Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-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。

Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.

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。

Assigns honest lipid annotation levels, designs class-based internal-standard quantification, and runs lipid-aware differential and enrichment analysis with lipidr, guarding against in-source-fragment phantoms, sn-position over-claims, and invalid cross-class quantification. Use when naming or canonicalizing lipid species (shorthand separators, Goslin), deciding shotgun vs RP vs HILIC LC-MS, picking internal standards (SPLASH/EquiSPLASH), interpreting MS-DIAL/LipidSearch output, or comparing lipid classes. For general feature detection see metabolomics/xcms-preprocessing and metabolomics/msdial-preprocessing; for non-lipid annotation confidence see metabolomics/metabolite-annotation; for normalization/QC see metabolomics/normalization-qc; for multivariate stats see metabolomics/statistical-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。

Generate a visual KPI report card with a headline metric, secondary stats, and branding — shareable as an image.

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。

Generate a branded property listing graphic with a property photo, status badge, price, address, and key stats.

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。