「graph」の検索結果

Derive an execution roadmap from a project's vision/spec docs by first principles, then diff that fresh derivation against the existing roadmap to prove the task ORDER is correct. Use this whenever the user wants to review, validate, re-derive, or sanity-check an execution roadmap or delivery plan; asks whether the tasks/epics are "in the right order"; suspects a task is scheduled before its prerequisite (a producer-after-consumer / dependency-inversion error like "OpenAPI after the web client that consumes it", OR a cross-cutting inherited default like observability/auth/tenancy scheduled after the feature work that inherits it — a seam-after-consumer error); wants to find missing tasks, circular dependencies, or over-fragmented epics; or wants to compact a plan to raise velocity. Also trigger on "review the roadmap", "re-derive the plan from the spec", "is this the right build order", "check the dependency graph of our epics", "which task should come first". It reads the vision + spec corpus, decomposes actors → use-cases → modules → epics → typed tasks, identifies cross-cutting inherited-default seams, builds a real dependency graph with hard + soft (seam) edges (deterministic helper), breaks cycles, topologically orders, compacts, and emits a derived roadmap PLUS a delta report against the current one — it never blindly overwrites a hand-crafted roadmap.

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。

The primary tool for code-navigation questions in ANY language (frontend or backend) — walk the structural graph from an entry point (page, route, handler, command, `main`) down through imports to the IO boundary (network call, query, file read). Use this skill, in preference to the Explore agent or global grep, whenever the user asks "where does X come from?", "what API/data populates Y?", "what renders or wraps the Z panel?", "which file for feature F?", "how is this wired up?", or any code-navigation question mapping a UI element, route, or feature concept to source files. Also reverse queries ("what uses /api/foo?", "who consumes this service?"). Keyword search routinely picks up unrelated flows sharing a domain word and produces wrong answers; the walk follows the actual graph. The JS/TS `walk.mjs`/`discover.mjs` are an optional accelerator, not required — the walk is language-agnostic.

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。

Cross-consistency review of a drafted spec's diagrams. Verifies that C4 components appear in the dependency graph, class-diagram changes have matching DDL, every AC resolves to a concrete sequence, and the dependency graph is acyclic. Read-only. Run after `/spec-lint` passes and before implementation.

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。

Take a markdown file of raw material and shape it into an article through a conversational session — drafting candidate openings, growing the piece paragraph by paragraph, arguing about format (lists, tables, callouts, quotes) at each step. Use when the user has a pile of notes, fragments, or a rough draft and wants help turning it into something publishable.

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。

REST/GraphQL API testing with automated validation — test endpoints, validate responses, check status codes, and ensure API contracts

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 building creative browser demos with @chenglou/pretext — DOM-free text layout for ASCII art, typographic flow around obstacles, text-as-geometry games, kinetic typography, and text-powered generative art. Produces single-file HTML demos by default.

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。

Infographics: 21 layouts x 21 styles (信息图, 可视化).

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 algorithms and complexity theory covering sorting, searching, graph algorithms, dynamic programming, greedy algorithms, NP-completeness, and algorithm design paradigms.

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 geology covering plate tectonics, rock cycle, mineralogy, stratigraphy, geomorphology, structural geology, and economic geology.

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 analytical chemistry knowledge. Use when working with chromatography, spectroscopy, electroanalytical methods, sample preparation, quantitative analysis, calibration, statistics, or instrumental analysis. Also use when the user mentions 'HPLC', 'GC', 'mass spectrometry', 'atomic absorption', 'ICP', 'titration', 'calibration curve', 'detection limit', 'NMR', 'chromatography', 'electrochemistry', 'sample preparation', or 'method validation'.

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 discrete mathematics knowledge. Use when working with logic, set theory, combinatorics, graph theory, relations, functions, induction, recursion, boolean algebra, or discrete probability. Also use when the user mentions 'combinatorics', 'permutations', 'combinations', 'pigeonhole principle', 'inclusion-exclusion', 'generating functions', 'recurrence relations', 'graph theory', 'trees', 'logic', 'propositional calculus', or 'boolean algebra'.

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。

Improves typography by fixing font choices, hierarchy, sizing, weight, and readability so text feels intentional. Use when the user mentions fonts, type, readability, text hierarchy, sizing looks off, or wants more polished, intentional typography.

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 the user asks about GitNexus itself — available tools, how to query the knowledge graph, MCP resources, graph schema, or workflow reference. Examples: "What GitNexus tools are available?", "How do I use GitNexus?"

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。

[Code Intelligence] Use when you need to analyze the blast radius of current code changes using the structural knowledge graph.

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。

[Code Intelligence] Use when you need to detect frontend-to-backend API connections using the knowledge graph.

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。

[Code Intelligence] Use when you need to query code relationships and connections using the structural knowledge graph.

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。

[Code Intelligence] Use when you need to build, update, or sync the code review knowledge graph. Flag: --scope={full|update|sync} (default auto-detect); --scope=full forces a full rebuild, --scope=update re-parses uncommitted working-tree changes, --scope=sync syncs committed git changes then updates the working tree.

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。

Manages brand identity, visual design system, and creative quality for MMIND.ai. Generates complete image prompts for Nano Banana Pro (Gemini), enforces the strict 5-color palette, flat-vector SaaS style, and typography standards. Use when user needs graphic prompts, brand consistency checks, visual style guidance, design briefs, or creative QA for any marketing 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。

Derive a type system from a style constraint — font pairings, scale/line-height/weights, DTCG tokens via design-tokens. Use to choose fonts or build a typographic scale.

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。

Route codebase-structure questions (who calls X, where used, change-impact) to an existing code-graph first: impact, tests-for, dead cheaper, never more precise; grep routine. Also 'call graph'.

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 the user asks about GitNexus itself — available tools, how to query the knowledge graph, MCP resources, graph schema, or workflow reference. Examples: "What GitNexus tools are available?", "How do I use GitNexus?"

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 designing, reviewing, or implementing typography in Apple-platform apps — SwiftUI text styles, hierarchy, readable sizes, monospaced data, custom fonts, labels, captions, and accessibility.

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。

Transforms data into compelling visual narratives by applying narrative structure, annotation techniques, scrollytelling patterns, and honest framing to data journalism, presentations, and infographics. Use when creating data-driven articles or reports, designing infographics with narrative, building scrollytelling experiences, annotating charts to guide interpretation, or when user mentions data storytelling, presentation design, annotated chart, narrative visualization.

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。