384 件 ・ 関連度順
概要と使いどころ
Orchestrate an end-to-end data analysis or machine learning project from decision framing through reproducible handoff. Use when a request spans multiple lifecycle stages or an ambiguous modeling request must become a decision-ready result; use narrower audit or experiment-design skills for isolated reviews.
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。
Azure Data Lake Storage Gen2 SDK for Python. Use for hierarchical file systems, big data analytics, and file/directory operations.
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 Bigquery — google BigQuery job analysis, slot utilization, cost analysis, dataset management, and query optimization.
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。
Azure Data Lake Storage Gen2 SDK for Python. Use for hierarchical file systems, big data analytics, and file/directory operations.
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。
Azure Data Lake Storage Gen2 SDK for Python. Use for hierarchical file systems, big data analytics, and file/directory operations.
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。
Automate Big Data Cloud tasks via Rube MCP (Composio). Always search tools first for current schemas.
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。
Azure Data Lake Storage Gen2 SDK for Python. Use for hierarchical file systems, big data analytics, and file/directory operations.
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。
Azure Data Lake Storage Gen2 SDK for Python. Use for hierarchical file systems, big data analytics, and file/directory operations.
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。
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.
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。
Answer natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod requests vs. actual utilization (`kubectl top`). Don't use for applying cost optimization changes, creating rightsizing manifests (VPA/MPA), or selecting ComputeClasses (use gke-cost-optimization instead).
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 complete product information from any e-commerce product page. Returns name, price, currency, brand, images, description, SKU/ASIN/EAN/UPC/GTIN/MPN identifiers, stock availability, rating, review count, variants, and seller. Works on Shopify, Amazon, WooCommerce, eBay, Walmart, Etsy, AliExpress, Alibaba, Target, Best Buy, Rakuten, Magento, BigCommerce, PrestaShop, and any public e-commerce site. Accepts product URL, keyword, or product identifier (SKU/ASIN/EAN/UPC). Use when: scrape product page, get product details, extract price and availability, product info extraction, check product data, product detail scraping, get product from URL, keyword product search, ASIN lookup, EAN search, UPC lookup, price check, product research, compare products, monitor product price, get product images, product brand and description, ecommerce data extraction, product catalog scraping, product page scraper, get item price, fetch product info.
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[omh] Database work on a relational store -- a slow query, an index to size, a migration on a big table, a lock taken during deploy, N+1 queries: plan it with the checks that prove it safe, and never call a migration ready without its lock behaviour and rollback. Use when the user says: relational-db, relational database, online migration, migration for a large table, lock-safe ddl, alter table, took a lock, table lock.
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Moves data from one system to another without losing it or corrupting it — profiling the source before mapping, deciding between big-bang and parallel-run cutover, reconciling counts and values rather than assuming, handling the records that will not map cleanly, and planning a rollback that is actually executable. Use this to plan or run a migration, assess a migration plan someone else wrote, work out why a completed migration is producing wrong numbers, or size how long one will really take.
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。
Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot; MetaPlotR; deepTools computeMatrix scale-regions), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (log2 IP/input bigWig via deepTools bamCompare; pyGenomeTracks; Gviz; IGV/UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), feature-distribution stacked bars, and volcano/MA plots for differential modification. Establishes stop-codon enrichment in the metagene plot as the biological QC anchor for any MeRIP dataset (Dominissini 2012; Meyer 2012). Use when producing the canonical metagene plot with stop-codon enrichment as a QC anchor, building paired IP/input genome-browser tracks at single-locus resolution, plotting peak-centred heatmaps clustered by condition, summarising peak distribution across transcript features, generating DRACH motif logos as sanity checks, rendering volcano plots of differential m6A, or reproducing the stop-codon enrichment plot.
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Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.
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 isolate identity at the right resolution for the question -- ANI/Mash species triage, 7-locus MLST historical comparability, cgMLST/wgMLST outbreak resolution (chewBBACA, BIGSdb, Ridom SeqSphere, EnteroBase HierCC), in-silico serotyping (SISTR/SeqSero2 Salmonella, SerotypeFinder E. coli, Kaptive Klebsiella, SeroBA pneumococcus, spa+SCCmec S. aureus), and lineage callers (TB-Profiler/Mykrobe barcode for MTBC, Pangolin + Nextclade for SARS-CoV-2, PopPUNK GPSC for S. pneumoniae). Use when typing bacterial isolates for surveillance or outbreak investigation, choosing between cgMLST allele distance and core-SNP distance for cluster definition, harmonising calls across schemas/database versions, assigning MTBC lineage with the Napier 90-SNP barcode, calling Salmonella serovar via SISTR with monophasic Typhimurium awareness, running Pangolin UShER mode with pangolin-data version pinning, or selecting a typing resolution to match the surveillance question.
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Running experiments out of the data warehouse instead of via dedicated experiment platforms. SQL-based assignment, exposure logging discipline, metric definitions in dbt models, statistical analysis in SQL or Python, variance reduction with CUPED, sequential testing, and the operational tradeoffs vs platforms like Statsig and Optimizely. Triggers on warehouse-native experimentation, run experiments in BigQuery, run experiments in Snowflake, dbt experiments, SQL t-test, CUPED variance reduction, exposure log, sample ratio mismatch, sequential testing, mSPRT, doubly robust estimation, build vs buy experimentation. Also triggers when the team is choosing between platform and warehouse, building warehouse-native experiment infrastructure, auditing one, or running an experiment with a custom metric the platform cannot handle.
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数据排序工具。读取结构化数据文件，根据指定字段进行升序或降序排序，最后输出为相同格式的文件。 当用户提到数据排序、升序排列、降序排列、按某字段排序等需求时使用此skill。 即使用户没有明确说出"排序"，只要任务涉及对数据进行排序，就应该使用此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。
乱码修复工具。自动检测并修复因编码错误解释导致的乱码文本（Mojibake），支持 GBK/UTF-8、Latin-1/UTF-8、 Big5、Shift_JIS 等 11 种编码链自动尝试，通过字符合理度评分自动选择最佳修复结果。 当用户提到乱码修复、编码修复、mojibake、文字乱码、编码错误等需求时使用此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。
提取 PDF 文件元数据并输出 JSON 结果。 当用户提到把这次流程沉淀成 skill、保存为 skill等需求时使用此 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。
数值数据阈值检验工具。读取结构化数据文件（CSV、TSV、Excel等），检查数值字段是否在指定的门限范围内，不允许超出门限值之外，最后输出为同格式的文件。当用户需要对数据进行阈值检验、数值范围检查、数据质量控制等操作时使用此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。
数据类型检查工具。读取被检验表，检查指定数值型字段的数据类型是否正确（是否能转换为数值型）， 并标记不合格的数据点，最后输出为相同格式的文件。 当用户提到数据类型检查、数值类型校验、字段类型检查、数据格式校验等需求时使用此skill。 即使用户没有说出"数据类型检查"，只要任务涉及检查数据字段类型是否正确，就应该使用此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。
提取PDF文件的元数据（标题、作者、主题、页数等），输出为JSON格式。当用户需要查看PDF文档信息时使用此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。
低信息量过滤工具。读取结构化数据文件，对指定文本列同时校验字符总数与行数是否落在阈值区间内，输出过滤后的合规行。 当用户提到低信息量过滤、字符数筛选、短文本过滤、超长文本过滤、行数过滤、信息量阈值等需求时使用此skill。 即使用户没有明确说出"低信息量"，只要任务涉及同时按字符数和行数两个维度过滤文本行，就应该使用此skill。 不负责按单词数过滤（用words_num_filter）、按token数过滤（用token_num_filter）、按最长单行长度过滤（用maximum_line_length_filter）或按平均行长度过滤（用average_line_length_filter）。与这些skill的关键区别在于：本skill同时校验字符总数与行数两个维度，且原生支持CSV/TSV/Excel多列输入、不依赖data_juicer。
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。