「arize」の検索結果

sechm provides a simple interface between SummarizedExperiment objects and the ComplexHeatmap package. It enables plotting annotated heatmaps from SE objects, with easy access to rowData and colData columns, and implements a number of features to make the generation of heatmaps easier and more flexible. These functionalities used to be part of the SEtools package.

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Intuitive framework for identifying spatially variable genes (SVGs) via edgeR, a popular method for performing differential expression analyses. Based on pre-annotated spatial clusters as summarized spatial information, DESpace models gene expression using a negative binomial (NB), via edgeR, with spatial clusters as covariates. SVGs are then identified by testing the significance of spatial clusters. The method is flexible and robust, and is faster than the most SV methods. Furthermore, to the

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We propose an Asymmetric Within-Sample Transformation (AWST) to regularize RNA-seq read counts and reduce the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experi

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PhIPData defines an S4 class for phage-immunoprecipitation sequencing (PhIP-seq) experiments. Buliding upon the RangedSummarizedExperiment class, PhIPData enables users to coordinate metadata with experimental data in analyses. Additionally, PhIPData provides specialized methods to subset and identify beads-only samples, subset objects using virus aliases, and use existing peptide libraries to populate object parameters.

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Harmonize data management of multiple experimental assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of st

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Microbiome time series simulation with generalized Lotka-Volterra model, Self-Organized Instability (SOI), and other models. Hubbell's Neutral model is used to determine the abundance matrix. The resulting abundance matrix is applied to (Tree)SummarizedExperiment objects.

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Get a PDF into the model without blowing the context window or losing structure. Native PDF beats OCR-then-text for most cases; extract-then-summarize beats native for very long docs.

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Maintains a repo-local task forest or task DAG for the current workspace. Use when the user asks to initialize or update a task forest, close a session, summarize evolving project work, align a request with a global goal, track progress/history/deviations/todos, save or apply a task proposal, or export the client-readable task-forest HTML. Do not use for executing the tracked tasks themselves or for generic HTML work.

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Turn a completed task, browser flow, artifact pipeline, failure-recovery trace, or repeatedly refined workflow into a new reusable skill package or a reviewed skill-design plan. Use when the user asks to make a new skill from real run history, extract a reusable workflow from conversation/logs/files, summarize lessons into a new skill, or produce a plan before writing files. Do not use to upgrade an existing skill or to execute the business workflow itself.

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Generates user-facing changelogs and release notes from git commit history. Parses conventional commits, categorizes changes into features, fixes, breaking changes, and more, then rewrites technical messages into customer-friendly language. Use when the user asks to generate a changelog, write release notes, summarize recent commits, prepare CHANGELOG.md, or document what changed between versions.

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Use when the user asks where the project stands, what to do next, or to resume after a break. Summarizes recent work and what is ahead, then routes to executing an existing plan or planning the next one.

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Create or update a structured change log for a product or platform. Use when someone asks to "create a change log", "log this change", "what changed", "update the change log", "release notes", or "changelog". Reads project memory (CLAUDE.local.md Recent Changes) and meeting notes to build chronological change records. Supports init (backfill from memory), update (append new entries), and release (summarize for a version).

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Generate structured meeting notes from transcripts or pasted text. Use when someone says "meeting notes", "process this transcript", "summarize this call", "notes from today's meeting", or pastes/references a meeting transcript. Auto-enriches attendees from stakeholder directory, routes output to correct project subfolder, flags downstream artifact impacts (PRD, deck, architecture), and proposes CLAUDE.local.md updates. Produces date-first Markdown files.

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Summarizes GitHub PR, GitLab MR, or Azure DevOps PR metadata, review threads, changed files, and template completeness. Use during review-ticket or code-review workflows when PR/MR context exists.

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Summarize and review what changed while you were away. Use after a weekend, vacation, or flight to check missed PRs, git commits, Linear tickets, and meetings — one prioritized brief, not a firehose.

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Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.

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Agents should invoke this skill for academic or technical papers, arXiv/PubMed/IEEE/ACM links, PDFs, methodology review, limitations, practical implications, or extracting findings for engineering decisions.

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Performs deep AI-powered analysis and understanding of long-form videos (up to 3 hours, 10GB) using Volcengine LAS large language models. Video analysis, video comprehension, and video summarization — generates comprehensive video summaries, video recaps, chapter breakdowns, event timelines, key moments, and structured content indexing. Supports behavior detection, action detection, video annotation, video tagging, and video content recognition. Enables intelligent video question answering — ask questions about video content and get AI answers. Handles long meeting recordings, lecture videos, webinars, surveillance and security footage, movies, tutorials, and any long video that needs detailed understanding. Async processing with submit-poll workflow. Use this skill when the user wants to analyze or understand long videos (up to 3h/10GB) with LLM-based deep comprehension, summarize video content or generate recaps, extract chapters/key moments/timelines from videos, do video Q&A (ask questions about video content), detect actions/behaviors in videos, review meeting recordings/lectures/webinars, or get structured video descriptions.

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Generates English pull request titles and bodies by comparing the current branch with main. Use when the user asks to draft PR content, summarize branch changes, or produce a PR title/body from git diff.

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Decision protocol for the map-reduce / dynamic fan-out pattern in LM pipelines — "given list L, run f(item) for each item in parallel, then combine". Activates when the coder agent is about to process N items with N LM calls (per-doc summarize, per-query retrieve, per-candidate rank, parallel tool fan-out). Encodes the *when*, *how many at once*, *what to do when one fails*, and *how to reduce* — not the API of any single framework. Cross-framework: LangGraph `Send`, CrewAI parallel tasks / Flow, `asyncio.gather`, `ThreadPoolExecutor`, LlamaIndex batch retrieval.

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Automatically fetch YouTube video transcripts, generate structured summaries, and send full transcripts to messaging platforms. Detects YouTube URLs and provides metadata, key insights, and downloadable transcripts.

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Podcast knowledge workflows powered by Podwise CLI: search podcasts and episodes by keyword, monitor followed shows for new releases, find popular episodes, ask questions and extract insights from transcript content, process Podwise episode URLs, YouTube videos, Xiaoyuzhou links, and local audio or video files to retrieve transcripts, summaries, chapters, Q&A, mind maps, highlights, and keywords — plus catch up on your backlog, refine your listening taste, generate weekly recaps, export episode notes to PKM tools, research topics across podcasts, debate episode ideas, and generate language learning cards. Use when the user wants to find, summarize, transcribe, or extract insights from any podcast or audio content, or manage their listening library.

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Summarize authored commits over a user-specified time period into a concise update

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Fetch and summarize review comments from the active pull request

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