「graphs」の検索結果

generate, refine, validate, and render diagrams from natural language, notes, code snippets, schemas, tables, or existing diagram source. use for flowcharts, swimlanes, sequence diagrams, state diagrams, er diagrams, class diagrams, architecture/c4-style diagrams, dependency graphs, gantt charts, mind maps, user journeys, sankey-style flows, org charts, network graphs, and other visual models. supports mermaid by default, graphviz dot for complex graph layout, plantuml for uml-heavy engineering diagrams, and svg output when direct markup is more reliable.

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。

Entity and relation tracking skill using MCP memory server patterns and local knowledge graph storage. Build persistent knowledge graphs of entities, relationships, and observations across agent sessions. Covers the MCP memory server (@modelcontextprotocol/server-memory), local JSON-based graphs, and entity-relation querying patterns for long-running agents.

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 a repository's type system and generate type-safe DAG execution pipelines with GraphSentry-style certificate verification. This skill should be used when building LLM-driven workflows that need deterministic type safety, when composing typed operations into validated execution graphs, or when adding contract-checked pipeline orchestration to any codebase. Supports Python (Pydantic) and TypeScript (Zod).

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。

Fix GraphSnapshot tests blocking CI on every feature PR. Use when: (1) every PR fails CI with "Snapshot mismatch" despite correct behavior, (2) fixing snapshots requires build → regen → commit → wait CI cycle per PR, (3) snapshot test in test/unit/*.test.js glob breaks on any intentional change, (4) pre-push hook runs pnpm build + snapshots:update on every push (2-3min overhead). Solution: move GraphSnapshot.test.js to test/unit/snapshots/ so it's excluded from the *.test.js glob. Run manually via pnpm snapshots:update when needed.

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。

Review owned or authorized code for security using structure-first evidence: AST/structure maps, call graphs, complexity, Source/Sink flow, and defensive findings. Use when asked for security review, vulnerability review, AST structure map review, SAST triage, Source/Sink, taint flow, parser/scanner hardening, CI/CD security, LLM/agent tool boundary review, 脆弱性レビュー, 構造マップ, セキュリティレビュー.

Representations for hierarchical skill structures including knowledge graphs and ontological decomposition

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 practical DAG decomposition, transitive-edge reduction, and reachability indexing to dense dependency graphs. Use when low width and repeated queries justify preprocessing. NOT for cyclic graphs, one-off graph checks, or exact-minimum-chain requirements.

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。

Decompose DAGs into execution chains using width bounds, stratification, virtual nodes, and matching. Use when minimizing workflow lanes in dependency graphs. NOT for cyclic graphs, generic scheduling, or one-off queries with no preprocessing payoff.

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。

Expert-level data structures covering arrays, linked lists, trees, heaps, hash tables, graphs, and advanced structures like segment trees, tries, and union-find.

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。

Expert-level graph theory knowledge. Use when working with graph algorithms, network flows, matching, graph coloring, planar graphs, spectral graph theory, random graphs, or graph applications in computer science and networks. Also use when the user mentions 'shortest path', 'minimum spanning tree', 'network flow', 'bipartite matching', 'graph coloring', 'clique', 'independent set', 'vertex cover', 'planarity', 'graph isomorphism', 'adjacency matrix', or 'Laplacian'.

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。

Load this skill whenever the project contains charts, graphs, data visualizations, infographics, or any visual representation of data (bar charts, line charts, pie charts, scatter plots, dashboards). Under no circumstances render a chart without a text alternative, data table, or summary. Prioritize conveying the same information through non-visual means.

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。

Expert en algorithmes (Big-O, dynamic programming, graphs, sorting, LeetCode patterns)

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。

Algorithms specialist v2 — Big-O, dynamic programming, graphs, sorting, competitive programming

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。

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

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。

NVIDIA CUDA GPU 並列コンピューティングリファレンス。 CUDA C++ / CUDA Python, kernel, nvcc, Unified Memory, CUDA Graphs, Cooperative Groups, Driver API, マルチ GPU。 PTX ISA 命令セット, state space, MMA 命令。 Blackwell チューニング, Streaming Multiprocessor, NVLink。 CUTLASS / CuTe DSL / CuTe C++ GEMM, Tensor Core, tcgen05, Operator API, nsight-compute, compute-sanitizer。

Apply practical DAG decomposition, transitive-edge reduction, and reachability indexing to dense dependency graphs. Use when low width and repeated queries justify preprocessing. NOT for cyclic graphs, one-off graph checks, or exact-minimum-chain requirements.

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 practical DAG decomposition, transitive-edge reduction, and reachability indexing to dense dependency graphs. Use when low width and repeated queries justify preprocessing. NOT for cyclic graphs, one-off graph checks, or exact-minimum-chain requirements.

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。

Decompose DAGs into execution chains using width bounds, stratification, virtual nodes, and matching. Use when minimizing workflow lanes in dependency graphs. NOT for cyclic graphs, generic scheduling, or one-off queries with no preprocessing payoff.

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。

Feishuのクラウド文書を読み取り、段落の修正や追記、表の編集、新規作成を支援します。文書内の構造を確認し、画像やファイルのアップロードも扱います。

• 共有されたFeishu文書を読みたいとき
• 段落の修正や指定位置への挿入
• 新規文書の作成と本文入力

画像の内容を質問したり、説明文を作ったりするために、LLaVAの導入と利用を支援します。画像を見ながら続ける対話や、画像付き文書の理解にも使えるスキルです。

• 写真の説明文を作りたいとき
• 画像内の物や場面について質問したいとき
• 画像について話せるチャットの試作

Use when applies to hyperlinks (`<a>` elements) that appear inline within paragraphs or text blocks. Does not apply to navigation menus, button-style links, or standalone links that are visually isolated from body text. Use when reviewing CSS that removes text-decoration from inline body copy links.

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。

Query AgentDB through the controller bridge -- semantic routing, hierarchical recall, causal graphs, context synthesis, pattern store/search

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。

Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.

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 RNA velocity analysis with scVelo from spliced and unspliced single-cell RNA counts. Fits deterministic or dynamical models, examines gene phase portraits, builds velocity graphs, estimates relative latent time, and ranks velocity-associated genes. Use for directional trajectory hypotheses and kinetic-model diagnostics alongside Scanpy; velocity alone does not establish cell fate or causal drivers.

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。