'dag' 검색 결과

Use when building content-heavy sites with Astro, deciding between SSG/SSR/hybrid, choosing client directives (idle/visible/load/media), structuring content collections with type-safe schemas, integrating React/Vue/Solid/Svelte components in the same project, or migrating from Next.js for marketing/docs. Triggers: client:idle vs client:load tradeoffs, content collections schema with zod, image optimization via @astrojs/image, view transitions, server islands, MDX layouts. NOT for SPA-style apps that need full interactivity (use Next/Remix), Astro internals/plugin authoring, or non-content sites where islands provide no benefit.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design roadside signs and large-format ads for businesses, especially automotive dealers. Use for pylon signs, monument signs, lot banners, highway billboards, digital roadside boards, service-lane signage, used-car event signs, lease or finance campaign concepts, and other driver-at-speed messaging where legibility, offer hierarchy, and dealer ad compliance matter. Activate on "road sign", "billboard", "dealership sign", "car dealer ad", "pylon sign", "monument sign", "service lane signage", "automotive billboard", "roadside ad", "dealer lease offer", or "drive-by signage". NOT for fabrication drawings, permitting or local sign-code review, web or social ad campaigns, or full brand identity systems.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Foundational concurrent computation model where actors communicate exclusively through asynchronous message passing

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Design performant animation systems with Framer Motion, GSAP, View Transitions API, and spring physics. Activate on: page transitions, micro-interactions, scroll animations, layout animations, spring physics. NOT for: CSS-only hover effects (use css-in-js-architect), video/canvas rendering (use creative-coding-expert).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design decisions for when and what sounds to use in web apps, mobile apps, and desktop native apps — plus how to create them with AI. Covers UI sound vocabulary (confirms, alerts, transitions, errors), platform conventions (iOS, macOS, Material), creating sounds with AI tools (ElevenLabs, AudioCraft, Stable Audio), Web Audio API for procedural sounds, haptic-audio pairing, and the psychology of satisfying vs. annoying UI sounds. Activate on 'app sounds', 'UI sounds', 'notification sound', 'sound design app', 'earcons', 'sonic branding', 'interaction sounds', 'feedback sounds', 'app audio', 'satisfying sounds'. NOT for professional audio engineering (use sound-engineer), Windows 3.1 retro sounds specifically (use win31-audio-design), music composition (use sound-engineer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Architecture and systems design for building always-on AI agents with episodic memory. Covers the memory hierarchy (core/recall/archival), persistence layers, agent server infrastructure, vector stores, and framework selection. Provides concrete deployment patterns for agents that maintain identity and learn across sessions. Activate on: "always-on agent", "persistent agent architecture", "episodic memory system", "agent memory design", "long-running agent", "stateful agent", "agent that remembers", "MemGPT architecture", "Letta deployment", "/always-on-agent-architecture". NOT for: choosing what data to feed the agent (use always-on-agent-inputs), brainstorming applications (use always-on-agent-applications), safety and privacy concerns (use always-on-agent-safety), general agentic patterns (use agentic-patterns).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Logic-based agent programming language implementing BDI architecture for practical autonomous agent development

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

API gateway and reverse proxy configuration with Kong, Nginx, Traefik, routing, and auth middleware. Activate on: API gateway, reverse proxy, Kong, Nginx, Traefik, load balancer, ingress, auth middleware. NOT for: rate limiting algorithms (use api-rate-limiting-throttling-expert), service mesh (use service-mesh-microservices-expert).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert API designer for REST, GraphQL, gRPC architectures. Activate on: API design, REST API, GraphQL schema, gRPC service, OpenAPI, Swagger, API versioning, endpoint design, rate limiting, OAuth flow. NOT for: database schema (use data-pipeline-engineer), frontend consumption (use web-design-expert), deployment (use devops-automator).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert in 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

App Store and Google Play submission automator with Fastlane, screenshot automation, metadata management, and TestFlight/internal testing. Activate on: app store submission, Fastlane, TestFlight, Google Play Console, screenshot automation, metadata management, app review, code signing, provisioning profiles. NOT for: CI/CD pipeline setup (use github-actions-pipeline-builder), app architecture (use react-native-architect), analytics (use mobile-analytics-crash-reporting-expert).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Extend and modify the admin dashboard, developer portal, and operations console. Use when adding new admin tabs, metrics, monitoring features, or internal tools. Activates for dashboard development, analytics, user management, and internal tooling.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Apply normative BDI reasoning to agents that must detect norms, choose which commitments to internalize, and resolve conflicts by comparing consequences. Use when obligations, prohibitions, or policies collide in autonomous systems. NOT for simple fixed-priority rules, pure constraint satisfaction, or domains where no real normative conflict exists.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Designs digital experiences for ADHD brains using neuroscience research and UX principles. Expert in reducing cognitive load, time blindness solutions, dopamine-driven engagement, and compassionate design patterns. Activate on 'ADHD design', 'cognitive load', 'accessibility', 'neurodivergent UX', 'time blindness', 'dopamine-driven', 'executive function'. NOT for general accessibility (WCAG only), neurotypical UX design, or simple UI styling without ADHD context.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Reference when designing autonomous agent systems or deciding agent freedom/safety/context management/collaboration infrastructure. 10 design principles distilled from Karpathy autoresearch (autonomous ML experiment loop) + AgentHub (agent-native Git DAG). Triggered by "autoresearch", "agenthub", "ratchet" ("래칫"), "context budget", "dumb platform", "program.md", "overnight", "autonomous experiment" ("자율 실험"), "branchless", "single-file constraint" ("단일 파일 제약"), "fixed time budget" ("고정 시간 예산") keywords.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Auto-decompose large tasks into DAG-compatible parallel subtasks

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.