Results for “ray data”

Analyze data using MATLAB. Use when the task involves tables, timetables, time-series data, numeric arrays, sensor matrices, or gridded data — including but not limited to exploring, row filtering, sorting, cleaning, transforming, aggregating, smoothing, padding, trimming, and answering questions about data. MATLAB provides extensive, easy-to-use built-in functions for these workflows with no additional products required.

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Provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files. GDS is portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited

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Review JavaScript loops and replace suitable ones with standard array and object methods. Make transformations, filtering, and aggregation easier to understand, with practical explanations and browser checks to confirm that changes preserve the intended behavior.

• Refactoring manual loops into array methods
• Clarifying data transformation, filtering, and aggregation
• Reviewing object transformations and deep copying

Review TypeScript settings and indexed access to arrays and objects to find values that may be undefined. Enable compiler checks and update unsafe code so potential failures become visible before the code runs.

• Audit TypeScript safety settings
• Review array access in data processing
• Investigate unexpected undefined errors

Provides large-scale single-cell omics data manipulation using Genomic Data Structure (GDS) files. It combines dense and sparse matrices stored in GDS files and the Bioconductor infrastructure framework (SingleCellExperiment and DelayedArray) to provide out-of-memory data storage and large-scale manipulation using the R programming language.

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This tool fetches microarray data directly from GEO database, based on the GEOQuery R package. Given a GSE accession ID, it returns an Rdata object containing the data and a text file (.cond file) summarizing the conditions of the experimen

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Ray分布式视频去重工具。使用精确匹配（MD5哈希）在文档级别删除重复视频，基于Ray分布式框架。 当用户提到视频去重、删除重复视频、分布式去重、大规模视频去重等需求时使用此skill。 即使用户没有明确说出"Ray"或"分布式"，只要任务涉及大规模视频去重处理， 就应该使用此skill。

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Ray分布式图像去重工具。使用图像哈希精确匹配在文档级别删除重复样本，基于Ray分布式框架。 当用户提到图像去重、删除重复图片、分布式去重、大规模图像去重等需求时使用此skill。 即使用户没有明确说出"Ray"或"分布式"，只要任务涉及大规模图像去重处理， 就应该使用此skill。

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Ray分布式文档去重工具。使用精确匹配（MD5哈希）在文档级别删除重复的样本，基于Ray分布式框架。 当用户提到文档去重、删除重复文档、样本去重、分布式去重、大规模数据去重等需求时使用此skill。 即使用户没有明确说出"Ray"或"分布式"，只要任务涉及大规模文档去重处理， 就应该使用此skill。

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Algorithms and data structures from first principles through advanced analysis. Covers sorting (bubble, insertion, selection, merge, quick, heap, radix), searching (linear, binary, BFS, DFS, Dijkstra, A*), fundamental data structures (arrays, linked lists, stacks, queues, hash tables, trees, heaps, graphs, tries), complexity analysis (Big-O, Big-Omega, Big-Theta, amortized), recurrence relations, and algorithm design paradigms (divide-and-conquer, greedy, dynamic programming, backtracking). Use when analyzing, selecting, implementing, or comparing algorithms and data structures.

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Apply a set of Rosetta Stone attribute mappings to a Narrative dataset by wrapping them in a one-shot workflow that calls the `CreateRosettaStoneMappingsIfNotExist` task. Consumes the structured output of `/generate-rosetta-stone-mappings`, normalizes the generator's snake_case to the workflow task's camelCase, re-validates every expression against the dataset's current schema, gates on user approval, submits via `narrative_workflows_create`, polls the triggered run, and reports per-mapping created / conflict / failed. Use when: "apply these mappings to dataset N", "create the Rosetta Stone mappings I just generated", "push the mappings I saved earlier to <dataset>", "productionize this mapping list", "submit the suggested_mappings array". (narrative-common)

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Prepare LLM training datasets with NeMo Curator by removing duplicates, filtering low-quality content, and redacting personal information. The skill also covers image, video, and audio curation, including GPU processing and distributed workflows.

• Clean web-scraped text for LLM training
• Remove exact, near, and meaning-based duplicates
• Remove or replace personal information in text

Find JavaScript code that changes existing objects or arrays, then replace those updates with modified copies. Review shared state and browser behavior to make data changes easier to trace and check fixes in context.

• Finding direct changes to shared objects
• Replacing push and splice with copy-based updates
• Reviewing state updates in client components

Build and evaluate time-to-event models with scikit-survival, including records whose events remain unobserved when follow-up ends. Keep preprocessing separate from evaluation data, compare models, predict survival probabilities, and summarize censoring-aware metrics.

• Validate event indicators and follow-up times in local CSV data
• Train survival models without leaking evaluation data
• Evaluate survival probabilities with censoring-aware metrics

Build and review numerical workflows for MATLAB or GNU Octave, including arrays, tables, graphics, tests, and MAT files. Helps plan release migrations, reproducible projects, and Python integration while checking runtime, compatibility, and license requirements.

• Reviewing array operations and numerical tolerances
• Planning MATLAB release migrations and compatibility checks
• Preparing reproducible projects and unit test scaffolds

Extract fields, filter records, and reshape JSON from API responses, configuration files, or logs using Crush’s built-in jq. Get help writing and debugging transformations, constructing new JSON, and formatting output for other commands.

• Extract selected fields from API responses
• Filter JSON arrays by conditions
• Reshape configuration data or construct JSON

Build and render TanStack Table v9 tables in Alpine, with data updates reflected in the interface. Covers table setup, Alpine markup, cell rendering, and guidance for sharing defaults and reusable UI across tables.

• Building a basic table in Alpine
• Reflecting replaced data arrays in a table
• Adding buttons and inputs to table markup

Assess security risks in AI systems by scanning prompts for known attack patterns and scoring data exposure, poisoning, and tool abuse risks. Map findings to MITRE ATLAS, an AI threat framework, and identify protective controls.

• Scan application prompts for injection and jailbreak patterns
• Review malicious instructions in retrieved documents
• Assess training data exposure and poisoning risks

Create a near-black animated canvas background with large square pixels and ordered dithering, a repeating pattern that represents shades. Broad waves and cloud-like forms provide atmosphere behind framed interfaces, hero text, or data displays.

• Adding monochrome atmosphere behind a dark interface
• Placing pixelated waves behind hero text
• Creating cloud-like backgrounds for data displays

Collect posts from public Facebook Pages or personal profile timelines as structured data. Capture text, author details, reactions, comments and share counts, plus media information, with date options and pagination for larger collections.

• Comparing reactions across Facebook posts
• Collecting posts around a specified date range
• Gathering post text, media links and author details

Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.

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Golang data structures — slices (internals, capacity growth, preallocation, slices package), maps (internals, hash buckets, maps package), arrays, container/list/heap/ring, strings.Builder vs bytes.Buffer, generic collections, pointers (unsafe.Pointer, weak.Pointer), and copy semantics. Use when choosing or optimizing Go data structures, implementing generic containers, using container/ packages, unsafe or weak pointers, or questioning slice/map internals. Not for applying optimization patterns once profiling has identified a bottleneck (→ See `samber/cc-skills-golang@golang-performance` skill).

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Use when optimizing the performance of an existing native scalar expression in the datafusion-comet-spark-expr crate (native/spark-expr/) — casts, string/JSON/array/math kernels that run per-row or per-batch. Covers benchmarking, keeping output bit-identical, and the no-regression gate. Not for adding new expressions (use implement-comet-expression) or wiring upstream functions (use wire-datafusion-function).

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Query and download from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI's BioStudies/ArrayExpress mirror. Use when finding expression datasets, navigating SuperSeries vs SubSeries, choosing between series-matrix (submitter-normalized) and raw supplementary files, downloading via GEOparse (Python) or GEOquery (R/Bioconductor), linking GEO to SRA for raw reads, or distinguishing GSE/GSM/GPL/GDS record types. Encodes the SuperSeries trap, the series-matrix normalization-trust caveat, GEOmetadb deprecation, ArrayExpress migration to BioStudies, and processed-vs-raw decision matrix.

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