「architecture analysis」の検索結果

Use when setting up vehicle network communication in MATLAB using Vehicle Network Toolbox. Covers CAN/CAN FD (fully implemented), with architecture for J1939, XCP, and future protocols. Handles hardware discovery, channel creation, bus configuration, message exchange, signal encoding/decoding, and analysis across all supported vendors. (Vector, Kvaser, PEAK-System, NI, SocketCAN, MathWorks Virtual).

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。

Create a workflow-intelligence report from CCAM orchestration, delegation, tool flow, concurrency, complexity, compaction, and fleet-run data. Use for architecture reviews, agent-fleet analysis, or workflow optimization reports.

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 SWIFT Customer Security Programme (CSP) advisor covering the Customer Security Controls Framework (CSCF v2026). Use this skill whenever a user asks about SWIFT CSP, CSCF controls, SWIFT security attestation, KYC-SA portal, SWIFT architecture types (A1/A2/A3/A4/B), mandatory vs advisory controls, independent assessment, SWIFT secure zone, secure flow zone, MFA for operators, SWIFT messaging security, payment fraud prevention on SWIFT, gap analysis for CSCF, or compliance with SWIFT's 32 controls (26 mandatory, 6 advisory in v2026) across the three objectives: Secure Your Environment, Know and Limit Access, Detect and Respond. Control 2.4 (Back-Office Data Flow Security) is now mandatory in v2026. v2026 attestation window is July 1– December 31, 2026. Trigger for any SWIFT CSP or CSCF compliance question.

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。

SaaS pricing design and optimization covering value metric selection, tier architecture, price point research, pricing page design, price increase execution, and competitive pricing analysis.

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 SWIFT Customer Security Programme (CSP) advisor covering the Customer Security Controls Framework (CSCF v2026). Use this skill whenever a user asks about SWIFT CSP, CSCF controls, SWIFT security attestation, KYC-SA portal, SWIFT architecture types (A1/A2/A3/A4/B), mandatory vs advisory controls, independent assessment, SWIFT secure zone, secure flow zone, MFA for operators, SWIFT messaging security, payment fraud prevention on SWIFT, gap analysis for CSCF, or compliance with SWIFT's 32 controls (25 mandatory, 7 advisory in v2026) across the three objectives: Secure Your Environment, Know and Limit Access, Detect and Respond. Control 2.4 (Back-Office Data Flow Security) is now mandatory in v2026. v2026 attestation window is July 1– December 31, 2026. Trigger for any SWIFT CSP or CSCF compliance question.

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。

Strategic SEO planning for new or existing websites. Industry-specific templates, competitive analysis, content strategy, and implementation roadmap. Use when user says "SEO plan", "SEO strategy", "SEO planning", "SEO best practices for <topic>", "content strategy", "keyword strategy", "content calendar", "site architecture", "SEO roadmap", or asks for organic growth guidance without a specific URL.

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。

Guides you through comprehensive iOS/Swift app planning and analysis. Use for new apps (concept to architecture) or existing apps (audit current state, plan improvements, evaluate tech stack). Covers product planning, technical decisions, UI/UX design, and distribution strategy.

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。

End-to-end product development for iOS/macOS apps. Covers market research, competitive analysis, PRD generation, architecture specs, UX design, implementation guides, testing, and App Store release. Use for product planning, validation, or generating specification documents.

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 comprehensive codebase analysis covering architecture, code quality, security, performance, testing, and maintainability. Use when user wants to audit code quality, identify technical debt, find security issues, assess test coverage, or get a codebase health check.

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。

Architectural style selection decision matrices, trade-off analysis, structural enforcement rules, and combination patterns. Load when choosing or evaluating architecture styles.

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。

Creates task-oriented technical documentation with progressive disclosure. Use when writing READMEs, API docs, architecture docs, or markdown documentation. Also use this skill at the END of any completed reverse engineering, penetration testing, CTF, or security analysis task to generate a formal report in the user's project directory. Trigger keywords: 写报告, 写文档, 出报告, writeup, 技术文档, report, documentation.

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 the open-source free `cloc` tool for line-count, language-mix, and diff statistics in .NET repositories. USE FOR: repeatable LOC reports; language-mix or branch-diff statistics; codebase size and solution-composition measurements. DO NOT USE FOR: judging developer productivity from raw LOC; replacing behavioral verification, architecture review, or complexity analysis. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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。

Assess project-wide structural technical debt: complexity hotspots, deprecated API usage, duplication clusters, architecture rot, and low-value tests. Ranks findings by impact and refactor effort into a report at .turbo/technical-debt.md. Use when the user asks to "assess technical debt", "find technical debt", "review technical debt", "what should we refactor", "find refactoring candidates", "where is the code rot", "what's our worst code", "find low-value tests", or "which tests can we delete". Analysis-only — does not modify code.

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 "review architecture", "Well-Architected review", "check bestpractices", "security assessment",or "cost optimization analysis".

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。

Technical due diligence for M&A, investment, or acquisition. Reads a target company's codebase and generates a comprehensive tech DD report with architecture assessment, tech debt quantification, scalability analysis, security posture, team capability inference, build system quality, test coverage, deployment maturity, and open source license risks. Outputs tech-dd-report.md formatted like a real investment memo with risk ratings, remediation costs, and go/no-go recommendation.

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。

Reconstruct and report long-running or multi-turn research, architecture questions, reviews, decisions, completion results, and status as a clear, self-contained brief. Use when a reader must re-enter earlier context, understand the real question and its relationships, distinguish verified facts from judgment and open evidence, or make a decision without reconstructing the work log. Quote the relevant original user wording before substantive analysis, keep one coherent main line, use spatial diagrams when relationships matter, and finish with an explicit decision boundary and practical next step.

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Designs the contract for an API change inside one codebase — a component's props, a function surface, URL or query parameters, an event payload, or a module boundary — through a discovery pass, an options document with one recommendation, a question round, and an adversarial validation round, with every element of the contract justified from one stated goal. Use when you want to design, shape, decide, or nail down an interface, contract, signature, or API change for a capability you can already describe, sized for roughly one pull request. Produces a design document and changes no code. Does not specify what a feature should do — use plan-a-feature. Does not plan delivery or sequencing — use plan-implementation. Does not assess the architecture of existing code — use architectural-analysis. Does not write the code — use tdd. Does not restructure existing code — use refactor. Runs its rounds without pausing for review; to review each round as it lands, use pairing.

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。

Produces a human-readable, progressive-disclosure overview of unfamiliar code or a pull request's changes — why it exists (the real problem it solves or goal it serves for the business or a user), and from there what it does, how it flows, and where to start — so you can get up to speed before working on or reviewing it. Use when you want to understand, get oriented in, make sense of, explain, or get up to speed on a chunk of code, a file, a directory, a symbol, or a PR's changes. Writes the overview to a scratch file and changes no code. Does not review code quality or raise findings — use code-review for auditing changes or post-code-review-to-pr for posting them. Does not produce durable feature or system documentation — use project-documentation. Does not assess architecture or structural risk — use architectural-analysis. Does not diagnose bugs or root-cause failures — use investigate. Does not pace a person through the code one step at a time in conversation — use code-walkthrough.

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。

Plans an architecture-driven change to code that already exists: module boundaries, type responsibilities, layering, coupling, and the public surface a revision moves. Produces a buildable change plan that names the types, modules, and methods involved and records the surface delta each change makes. Use when the user wants to plan, scope, or sequence a restructure, extraction, split, consolidation, responsibility shift, or API revision of existing code, including "these responsibilities are wrong, plan the fix". Does not assess an area and stop at findings — use architectural-analysis. Does not specify new behavior — use plan-a-feature. Does not diagnose a bug — use investigate. Does not write the code — use refactor to restructure it or tdd to build it.

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 style of Geoffrey Hinton, deep learning pioneer and 2018 Turing Award winner. Use this skill whenever evaluating AI safety, existential risk, neural network architectures, cognitive science, or tech regulation. Reach for this when the user is discussing LLM capabilities (understanding vs. autocomplete), the biological vs. digital intelligence divide, AI alignment strategies, or the societal/economic impacts of automation. It is highly applicable when dealing with contrarian scientific ideas, hardware/software integration (mortal vs. immortal computing), or global cooperation on technological threats. Do not wait for the user to name Hinton; trigger this skill proactively for any deep learning or AI existential risk analysis.

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Code intelligence over repositories Guaardvark has indexed: semantic code search, exact-text search, file listing, PageRank repository map, import dependency graph, reading one class or function, analysis and generated rewrites, plus the System Mapper and self-improvement status. Use when the user asks where something is implemented, how modules depend on each other, or wants an architecture map of an indexed repo.

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。

Documentation-first project planning that produces implementation-ready blueprints and single-shot coding agent prompts. Generates 4 interconnected docs — SPECIFICATION.md, IMPLEMENTATION.md, TASKS.md, BRANDING.md — plus a self-contained PROMPT.md for autonomous execution by any coding agent. Includes interactive tech stack selection, design pattern recommendations, and architecture decisions with trade-off analysis. Trigger when user wants to: plan a project, create specs, architect a system, break work into tasks, choose a tech stack, get design pattern advice, generate a coding agent prompt, or do documentation-first development. Phrases: "plan my project", "spec this out", "architect", "help me plan", "what stack should I use", "generate a prompt", "break this into tasks", "project docs", "I want to build X".

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。

Recommends appropriate software design patterns based on problem descriptions, requirements, or code scenarios. Use when designing software architecture, refactoring code, solving common design problems, or choosing between design approaches. Analyzes the problem context and suggests suitable creational, structural, behavioral, architectural, or concurrency patterns with implementation guidance and trade-off analysis.

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 this skill when an agent needs production image or video understanding with Amazon Rekognition: labels, objects, scenes, OCR, moderation, image properties, Custom Labels or moderation adapters, stored-video analysis, conditional streaming-video workflows for existing eligible accounts, searchable media libraries, confidence evaluation, S3/IAM/event architecture, privacy, biometric consent, cost control, lifecycle management, and 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。