「interface」の検索結果

Systems engineering covering requirements management, the V-model lifecycle, integration planning, verification and validation (V&V), interface control, trade studies, and the NASA Systems Engineering Handbook. Spans the full lifecycle from concept through operations and disposal. Includes requirements decomposition, traceability matrices, configuration management, and technical performance measures. Use when managing complex system development, writing requirements, planning integration, or conducting V&V activities.

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Computational thinking as a problem-solving discipline independent of programming languages. Covers the four pillars (decomposition, pattern recognition, abstraction, algorithm design), computational problem-solving methodology (understand, decompose, generalize, formalize, verify), abstraction levels (from hardware through user interface), modeling and simulation, automata and formal languages (DFA, NFA, regular expressions, context-free grammars, Turing machines), computational complexity classes (P, NP, NP-complete, undecidable), and constructionist pedagogy (learning by building, Logo, Scratch, physical computing). Use when approaching unfamiliar problems, teaching problem-solving strategies, analyzing problem complexity, or bridging between domain knowledge and computational solutions.

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USB and CAN connectivity for Zephyr RTOS. Covers USB device stack configuration (CDC ACM, HID, MSC), CAN controller integration, and professional USB-to-CAN adapter patterns including buffering and protocol packetization. Trigger when adding USB interfaces, implementing CAN bus communication, or building hardware diagnostic tools.

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Industrial communication protocols for Zephyr RTOS. Covers Modbus RTU (serial), Modbus TCP (Ethernet/Wi-Fi), and CANopen basics. Trigger when building factory automation controllers, industrial sensors, or medical equipment interfaces.

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Audit a MaaFramework/Maa-series project's Git history to learn how Pipeline JSON, interface options, Python AgentServer CustomAction code, and related data tables evolved. Use when asked to review a Maa project from initial commit through a target commit, map `action: Custom` pipeline nodes to `@AgentServer.custom_action(...)` implementations, find pipeline/custom/option patterns or breakages, and produce a report plus skill-improvement recommendations.

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生成 MaaFramework Pipeline 项目的可交互状态机图谱。当用户想"看 Pipeline 节点关系"、"画状态机图"、"画 Mermaid 图"、"看 node 间关系"、"找孤立节点 / orphan node"、"看 Python 怎么驱动 Pipeline"、"看 interface.json 入口"、"调试跨文件 next 引用"、"重构前理清架构" 时使用。**任何涉及 MaaFramework 节点关系可视化的需求都用这个 skill**,即便用户没明确说"画图"。

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Add runtime UI options (select/checkbox/switch/input) to MaaFramework option surfaces such as `assets/interface.json` or `assets/resource/tasks/**/*.json`. Use when adding a user-facing toggle, selector, checkbox, or input; wiring options to `pipeline_override`; aligning option paths with Python `context.get_node_data()` or CustomAction params; or reviewing option behavior across Pipeline JSON and Python.

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Interactive assistant for intercepting, debugging, analyzing and reviewing Claude Code API requests using mitmproxy. Guides setup, certificate configuration, and active traffic inspection via web or CLI interface. Supports learning Claude Code internals, debugging issues, and optimizing API usage.

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Brain-Computer Interface with colored operad security boundaries. Maximally isolated EEG/neural data processing via VM→Container→Process enclosure with GF(3) conservation for Agent-O-Rama neural pathways.

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Analyze specific pytest failures using authoritative contracts and a cited product/test evidence chain. Use for failing test names or pytest output, distinguishing product, interface, oracle, and harness defects without automatically changing expectations or forcing an architectural redesign.

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Review changed user-interface or CLI presentation behavior for accessibility failures. Use for an independent accessibility perspective on a defined change set.

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Analyze the causal system-wide impact of a proposed change across behavior, interfaces, state/data, runtime dependencies, tests, documentation, controls, people/processes, and model/prompt/context behavior. Use before planning when lexical reference search is insufficient to establish blast radius and verification obligations.

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Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.

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Measure whether a change made the tree simpler, on numbers rather than impressions — line counts, branching, coupling, interface width, rule count, standing exemptions and model-visible prose — across one diff or a whole stack of pull requests. Use before claiming a pass reduced complexity, when deciding which pull request in the stack added the machinery, and to read a function's branch count, which no v2 gate reports.

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AgentHero AI - Hierarchical multi-agent orchestration system with PM coordination, file-based state management, and interactive menu interface. Use when managing complex multi-agent workflows, coordinating parallel sub-agents, or organizing large project tasks with multiple specialists. All created agents use aghero- prefix.

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Batteries-included agent component for React/Next.js from ui.inference.sh. One component with runtime, tools, streaming, approvals, and widgets built in. Capabilities: drop-in agent, human-in-the-loop, client-side tools, form filling. Use for: building AI chat interfaces, agentic UIs, SaaS copilots, assistants. Triggers: agent component, agent ui, chat agent, shadcn agent, react agent," agentic ui, ai assistant ui, copilot ui, inference ui, human in the loop

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Validate AgentConfig definitions for the Agent Framework. Use when creating or modifying agent configurations to ensure correct structure, valid tool references, and proper sub-agent composition. Validates TypeScript interfaces and Python Pydantic models.

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Frontend specialist creating distinctive, production-grade interfaces

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Give your OpenClaw agent a homelab. Use when managing Docker containers, installing marketplace apps, or any Docker-related tasks on behalf of the user. Scope-based API key permissions — agent can only do what the key allows. On Linux, Docker requires sudo — without root access, WAGMIOS is the only safe interface for agent homelab control. Supports multi-machine management — one agent can manage multiple WAGMIOS instances across different hosts, each with its own scoped key. Requires X-API-Key header on every request (user provides at runtime). Includes Docker installation check and startup validation.

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Mobile development intelligence. 12 domains, 9 stacks, 250+ entries. Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check mobile code. Domains: pattern, package, error, perf, test, security, interface, arch, idiom, anti, tooling, dependency. Stacks: flutter, react-native, swiftui, jetpack-compose, kotlin-multiplatform, capacitor, expo, native-ios, native-android. Integrations: arch_system generator.

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Go development intelligence. 12 domains, 10 stacks, 300+ entries. Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check Go code. Domains: pattern, package, error, perf, test, concurrency, interface, arch, idiom, anti, tooling, module. Stacks: web-gin, web-echo, web-fiber, web-stdlib, cli, grpc, database, microservice, cloud-native, data-pipeline. Integrations: arch_system generator.

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PHP development intelligence. 12 domains, 8 stacks, 300+ entries. Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check PHP code. Domains: pattern, package, error, perf, test, security, interface, arch, idiom, anti, tooling, dependency. Stacks: laravel, symfony, codeigniter, slim, api-platform, livewire, database, queue. Integrations: arch_system generator.

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Audit or design generative AI interfaces against three diagnostic pillars (deterministic-stochastic balance, interaction density, visual cohesion). Treats taste as a measurable control system, not subjective preference. Use when: 'taste audit', 'score this UI', 'design quality', 'interaction density', 'visual cohesion', 'refiner layer', 'correction cost', 'why does this feel off', 'polish this', 'design review', or building any user-facing AI product.

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Anti-slop frontend skill for landing pages, portfolios, and redesigns. The agent reads the brief, infers the right design direction, and ships interfaces that do not look templated. Real design systems when applicable, audit-first on redesigns, strict pre-flight 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。