「rd」の検索結果

Detects and fixes color contrast violations using WCAG 2.1 guidelines and perceptual analysis. Expert in contrast ratio calculation, color blindness simulation, and providing accessible alternatives. Activate on "check contrast", "color accessibility", "WCAG audit", "readability check", "contrast ratio", "hard to read", "can't see text". NOT for general color theory (use color-theory-palette-harmony-expert), brand color selection (use web-design-expert), or non-visual accessibility (use ux-friction-analyzer).

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 in color theory, palette harmony, and perceptual color science for computational photo composition. Specializes in earth-mover distance optimization, warm/cool alternation, diversity-aware palette selection, and hue-based photo sequencing. Activate on "color palette", "color harmony", "warm cool", "earth mover distance", "Wasserstein", "LAB space", "hue sorted", "palette matching". NOT for basic RGB manipulation (use standard image processing), single-photo color grading (use native-app-designer), UI color schemes (use vaporwave-glassomorphic-ui-designer), or color blindness simulation (accessibility specialists).

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 for bounded reasoning, partial planning, commitment policy, deliberation cost, and reconsideration-threshold design instead of assuming free deliberation. NOT for exhaustive planning, static optimization, or domains where reconsideration cost is irrelevant.

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。

Implement AI chatbot analytics and conversation monitoring. Use when adding conversation metrics, tracking AI usage, measuring user engagement with chat, or building conversation dashboards. Activates for AI analytics, token tracking, conversation categorization, and chat performance.

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。

JADE platform for building FIPA-compliant multi-agent systems with standardized agent communication

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。

Jason multi-agent platform implementing AgentSpeak(L) for practical BDI agent programming and deployment

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。

Safety, privacy, cost management, and frank advice for building always-on AI agents with episodic memory. Covers data hygiene, privacy risk surfaces, runaway cost prevention, scope creep, psychological effects of persistent AI companions, and responsible deployment patterns. This is the skill that tells you what can go wrong and how to prevent it. Activate on: "agent safety", "always-on agent privacy", "agent cost control", "persistent agent risks", "AI companion safety", "agent data hygiene", "runaway agent costs", "parasocial AI risk", "/always-on-agent-safety". NOT for: architecture design (use always-on-agent-architecture), input design (use always-on-agent-inputs), application brainstorming (use always-on-agent-applications), healthcare compliance specifically (use hipaa-compliance).

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。

Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool 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。

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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 cognitive task analysis to expert work that depends on perceptual cues, branching judgment, and recurring monitoring loops. Use when decomposing expert capability into agent structure, simulation design, or validation interviews. NOT for ordinary step-by-step SOP capture or simple pipelines with no tacit cue layer.

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 AgentSpeak(L)-style BDI agents with context-guarded plans, selection functions, and intention stacks. Use for interruptible autonomy, agent policy, and multi-agent orchestration in dynamic environments. NOT for simple rule engines, static planners, or centralized workflows.

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。

Choosing and operating an HTTP API versioning strategy that doesn't break clients — Stripe's date-based pinned versions, the Deprecation/Sunset header pair (RFC 9745 + RFC 8594), URI vs header vs media-type approaches, and the version-transformer pattern. Grounded in Stripe's published architecture and IETF RFCs.

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。

Safely auto-detect the current machine's hardware (GPU, VRAM, system RAM, OS) with read-only commands, then recommend the top 3 local AI models to run — most powerful that fits, best for coding, and best small-and-fast — each with download links. Use when someone asks "what AI models can my computer run" or "what's the best local model for my rig".

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。

ALWAYS invoke this skill when delegating, planning, or managing any development task. Do not implement features, fix bugs, or start coding directly without first loading and following this orchestrator pipeline. Use when implementing features, fixing bugs, managing tasks, reviewing code, orchestrating work, or any task that needs structured delegation. Trigger on words like orchestrate, manage, implement, build, develop, organize, delegate, team lead, pipeline, or project manage.

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。

Self-learning pre/post-task hooks and model/task routing from the Ruflo CLI, on a ruOS desktop. Use to add coordination, formatting, and learning around Claude Code operations, or to route work by complexity/cost.

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。

Core statistical methodology and pedagogical approach for all Inquiro analyses. Always loaded to maintain teaching quality and reproducibility standards.

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。

Set up and manage social media tracking across Twitter, Instagram, Reddit, and TikTok using Xpoz. Add, remove, and view tracked keywords, users, subreddits, and hashtags. Use when asked to "track mentions", "monitor brand", "set up tracking", "add tracked item", "what am I tracking", "stop tracking", "remove tracked", or "social monitoring".

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。

Export Twitter/X data to CSV for analysis using Xpoz. Search by keywords, author, date range, and download complete datasets (up to 500K rows). Use when asked to "export tweets", "download Twitter data", "get tweets as CSV", "Twitter dataset", or "bulk tweet download".

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。

Auto-activate for litestar_security, SecurityPlugin, SecurityConfig, SecureController, CurrentUser, Principal, SecurityContext, or requires_role/scope/tenant/capability/assurance. Not for basic Litestar Guards.

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。

Auto-activate for Litestar, Controller, Router, @get/@post, FromPath, MsgspecDTO, OpenAPIConfig, Provide, FromDishka, Guard, ASGIMiddleware, HTTPException, ChannelsPlugin, or WebSocket. Not for standalone libs.

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。

Auto-activate for test_*.py, conftest.py, litestar.testing, TestClient, AsyncTestClient, create_test_client, create_async_test_client, anyio, Guard mocks, or DI overrides. Not for generic pytest.

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。

复杂探索资产化 / Complex Exploration：当用户面对复杂、不确定、多轮迭代的产品策略、Roadmap、商业化定价、竞品定位、复杂 PRD 前置探索、项目复盘或方法论沉淀任务时使用。它先判断任务类型，暴露隐含假设，重构真正问题，规划探索路径和中间产物，并在结束后沉淀认知、结构、方法论、工具和影响力资产。不用于简单润色、翻译、摘要、明确执行任务；问题只需早期认知校准时优先用 ai-collaboration-calibration，已有方案要压力测试时用 grill-me，系统专题研究用 research-topic-compiler。

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。

客户需求发现与澄清助手：当用户是销售、客户成功或售前，拿到客户模糊、宽泛、缺少产品边界的需求，需要先在内部最多五轮补齐关键事实，再生成一次性客户澄清问题清单、需求摘要与通用技术可行性判断时使用；当确认需求将落入 StyleWork 时加载可选产品与 UI 上下文，并可在信息足够或明确要求时输出带假设标记的轻量 Demo。不要用于直接报价、承诺交期、替代正式 PRD 或在信息不足时假装已有能力。

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。

方案到交付 Workflow：当用户显式调用 `$solution-to-delivery`，或明确要求运行“方案到交付”完整流程时使用。 把已确认产品方案转成经过独立 Review 的 PRD、适用的 UI/HTML/截图、版本拆分和 Product Delivery Package；当前 Agent Runtime 只做 Package 发布 dry-run，真实写入保持 authorization_required。

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。