「graphs」の検索結果

Use when users ask to 找文献、做文献检索、查论文、查临床试验、核验引用、去重文献、设计 PubMed/MeSH 检索式、追踪上下游引文、解析 DOI/PMID/PMCID/arXiv/OpenAlex/Semantic Scholar/NCT ID， 或导出 RIS、BibTeX、NBIB、ENW；also use for multi-source academic search, citation verification, citation graphs, trial registration search, research workflows, or DeepSeek Harness academic-search MCP setup.

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。

This skill should be used when the user asks to "self-publish", "publish my book", "ISBN", "copyright page", "book metadata", "BISAC", "keywords", "KDP", "IngramSpark", "Draft2Digital", "go wide", "Kindle Unlimited", "print interior", "trim size", "paperback", "ebook", "EPUB", "cover wrap", "spine width", "pricing", "launch plan", "ARC team", "advance readers", "newsletter", "reader magnet", "Amazon ads", "BookBub", "rights", "foreign rights", "audio rights", "film rights", "publishing contract", or wants to take a finished manuscript through production, distribution, launch, and rights management. It owns contracts and rights sales; NOT for permission to quote lyrics, epigraphs, or other material in the book (use editorial-review), or for drafting the blurb or retailer description (use submission).

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。

Implements data lineage tracking for privacy compliance including origin tracking, transformation logging, access auditing, deletion verification, and cross-system lineage graphs. Covers source-to-sink mapping, GDPR Art. 30 RoPA integration, automated lineage discovery, and breach impact scoping. Keywords: data lineage, data provenance, data flow mapping, transformation logging, deletion verification.

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 implementing visualize data with graphs and charts.

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。

Profile programs at the function/method level to identify performance hotspots, bottlenecks, and optimization opportunities. Records execution time, memory usage, and call frequency for each interval. Generates actionable recommendations and visualizations. Use when users need to (1) analyze program performance, (2) identify slow functions or bottlenecks, (3) optimize execution time or memory usage, (4) profile Python, Java, or C/C++ programs with test cases or workload scenarios, or (5) generate performance reports with flame graphs and recommendations.

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, inspect, audit, and refactor Blender Geometry Nodes systems for procedural models, instancing, modular effects, curves, scattering, flocks, data visualization, fields, simulation zones, and node-graph cleanup. Use whenever a Blender task specifically calls for Geometry Nodes, reusable procedural node graphs, node connections/layout, or a scalable procedural asset.

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 engaging PR titles and descriptions for any forge (GitHub today; Bitbucket planned). Use when creating or updating PRs. Avoids boring bullet lists; uses narrative paragraphs with bold/italic for emphasis.

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。

Orchestrate iterative visual style searches with branch prompts, decision graphs, feedback loops, and final direction selection.

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 structured knowledge graphs from unstructured text by extracting entities, mapping relationships, generating graph triples, and visualizing the result. Use when the user requests knowledge graph creation or provides relevant inputs for this workflow.

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。

Design and operate bounded multi-agent workflows with task decomposition, dependency graphs, ownership, handoff contracts, shared-state controls, approvals, recovery, and synthesis. Use when a task contains genuinely independent workstreams, specialized roles, parallel research or implementation, reviewer-worker loops, or coordination problems that one agent should not execute sequentially.

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。

Think and work like an expert Connectomics Scientist. Use when a task calls for Connectomics Scientist judgment. Reasons from vEM acquisition and petascale alignment through FFN/RoboEM segmentation, FlyWire/neuPrint/MICrONS/H01 graphs, and synapse-level QC while treating split/merge errors, alignment tears, false synapses, and release-version drift as first-class failure modes.

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。

Think and work like an expert Combinatorialist. Use when a task calls for Combinatorialist judgment. Reasons from labelled vs unlabelled enumeration, EGF/OGF and species, bijective and probabilistic proofs, Turán/Ramsey/extremal bounds, and BIBD/OA design parameters through SageMath/GAP/nauty, OEIS, House of Graphs, and Colbourn–Dinitz tables while treating isomorphism double-counting, parity barriers, and OEIS false matches as first-class failure modes.

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。

Provider-independent production workflow for agents assembling, auditing, executing, and handing off ComfyUI node-graph workflows for image, video, upscale, inpaint, conditioning, and batch media generation; use when a task involves ComfyUI workflow JSON/API graphs, model and custom-node inventories, reproducibility, provenance, safety/rights review, local or cloud execution hygiene, or production QA.

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 Mermaid architecture diagrams from your codebase. Produces C4 context and container diagrams, sequence diagrams for key flows, ER diagrams from database schemas, and module dependency graphs. Use when you need architecture diagrams, system diagrams, database ERD, sequence diagrams, dependency visualization, or C4 model diagrams.

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。

Analyze indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a `.codegraph` index exists in the workspace, or when the user wants to create one.

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 Word documents programmatically with the docx library — create paragraphs, tables, images, headers, footers, numbered lists, and styled content. Use when tasks involve generating contracts, proposals, resumes, or any .docx output from application data in Node.js.

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。

Assists with building custom interactive data visualizations using D3.js. Use when creating charts, graphs, maps, force layouts, or hierarchical diagrams that require fine-grained control beyond what charting libraries provide. Trigger words: d3, data visualization, chart, svg, scales, force graph, treemap, choropleth.

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。

Select the right Python charting library (seaborn, matplotlib, graphviz) and produce publication-quality static visualizations. Use when creating charts, plots, graphs, diagrams, heatmaps, visualizations from data, or when choosing between matplotlib/seaborn/graphviz. Also triggers for network diagrams, flowcharts, dependency trees, state machines, and entity-relationship diagrams. For interactive browser-rendered charts or uploaded data exploration, defer to charting-vega-lite 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。

TRIGGER when: user asks to draw, illustrate, or visualize any process, structure, or concept — "画个流程图", "draw an architecture diagram", "帮我画个对比图", "visualize this process", "make a diagram for this doc", or mentions 流程图/架构图/示意图/对比图/时序图/泳道图/时间线. Also trigger when user provides a document/article and asks for illustrations or diagrams. DO NOT trigger for: chart/plot requests (bar charts, line graphs — those need a charting library, not draw.io), or artistic image requests (logos, mascots, drawings of objects/people — those need an image 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。

Generates a fully structured SEO content **outline** (not a finished brief) and exports it as a formatted .docx Word document. The output is a skeleton for a writer to fill in — section headings, topic prompts, angles — not pre-written paragraphs. Use this skill whenever a user provides a blog title, focus keyword, domain URL, or any combination of those and asks to generate an outline, brief, blog brief, SEO brief, article structure, or content plan. Also trigger when the user says "create a brief for X", "generate an outline for [topic]", "make a content brief", "I need a brief for [URL or keyword]", or pastes a title and asks what the structure should look like. This skill handles input validation, domain analysis, keyword enrichment, audience inference, archetype-aware section selection, and full .docx generation — always use it rather than writing ad-hoc outlines.

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 dark-mode-compatible inline SVG data visualization charts for blog posts. Supports horizontal bar, grouped bar, donut, line, lollipop, area, and radar charts with automatic platform detection (HTML vs JSX/MDX). Enforces chart type diversity, accessible markup (role=img, aria-label), source attribution, and transparent backgrounds. Use whenever the user mentions data visualization, charts, graphs, comparison tables that need to be visualized, or wants to embed inline SVG visualizations in a blog post, even if not invoking blog-write. Use when user says "blog chart", "generate chart", "data visualization", "svg chart", "blog graph", "visualize data", or when the blog-write workflow identifies chart-worthy data points (3+ comparable metrics, trends, before/after data).

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。

Applies the operational and scientific frameworks of Stacey Gabriel (genomicist and sequencing platform leader at the Broad Institute). Use this skill whenever the user is designing large-scale biomedical research, operationalizing lab workflows, standardizing data governance, or transitioning from bespoke academic experiments to industrial-scale science. It is highly relevant for queries involving genomics, precision medicine, multiomics, cellular perturbation pipelines, or building massive datasets to fuel AI. Trigger this skill to shift the user's focus from 'hand-to-hand combat' one-off studies to systematic, standardized, and scalable data 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。

Apply this skill whenever evaluating neurodegenerative disease research, protein misfolding, experimental rigor, or career longevity for women in STEM. Use this to channel the thinking of Virginia M.-Y. Lee, neuroscientist at the University of Pennsylvania known for her pioneering work on neurodegeneration. Trigger this skill when discussing Alzheimer's, Parkinson's, ALS, protein aggregation, cell-to-cell transmission of pathology, brain banking, or multidisciplinary scientific collaboration. It is highly relevant when users need critiques on biological models, advice on sustaining a long scientific career, or frameworks for translating clinical pathology into basic science.

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。

Applies the reasoning of Mary Wollstonecraft (18th-century English philosopher and author of A Vindication of the Rights of Woman) to evaluate situations involving power dynamics, education, independence, gender roles, and systemic inequality. Use this skill whenever Claude encounters career trade-offs involving financial dependence, analyzing structural oppression, questioning societal norms, or balancing reason against emotion. It helps dismantle arbitrary hierarchies, prioritize rational capability over superficial elegance, and frame personal struggles as political realities.

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。