4,283 件 ・ 関連度順
概要と使いどころ
Develop or review dygo metadata loading, YAML decoding, JSON Schemas, validation, naming, Field types, registries, routes, and persisted metadata contracts. Use for framework-level metadata behavior rather than authoring one Business App Entity.
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, implement, or review security-sensitive dygo behavior across auth, sessions, Permissions, secrets, APIs, database writes, files, Jobs, and Studio. Use when security boundaries are a primary concern.
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。
Develop or review dygo PostgreSQL connectivity, schema planning, additive migration, prune, metadata persistence, Records, transactions, and schema snapshots. Use for framework database behavior and high-risk data lifecycle changes.
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。
Generate comprehensive test plans, manual test cases, regression test suites, and bug reports for QA engineers. Includes Figma MCP integration for design 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。
Builds production-quality UIs. Use when building or modifying user-facing interfaces. Use when creating components, implementing layouts, managing state, or when the output needs to look and feel production-quality rather than AI-generated.
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。
Records decisions and documentation. Use when making architectural decisions, changing public APIs, shipping features, or when you need to record context that future engineers and agents will need to understand the codebase.
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 to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing. Uses GPT-5.2 by default for state-of-the-art software engineering.
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。
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
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。
Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems.
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。
Optimize content for AI search and LLM citations across AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and similar systems. Use when improving AI visibility, answer engine optimization, or citation readiness.
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。
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
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。
Map the most influential Reddit venues for a brand's AI visibility using Xpoz. Finds which subreddits and threads AI engines actually cite when answering buyer questions, where those questions get asked, and which live threads are worth answering. Use when asked to "get cited by AI through Reddit", "which subreddits matter for my brand", "Reddit influence map", "where should we engage on Reddit", or "why does ChatGPT cite Reddit for my category".
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。
AI Agent 成本工程 — 基於 Claude Code 的成本追蹤、prompt cache 最佳化、token 預算控制逆向分析。Use when: 優化 token 消耗、設計成本控制機制、分析 cache 效率、選擇模型配置。
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。
Harness Engineering 設計模式 — 基於 Claude Code 原始碼逆向分析的 12 條可遷移原則。Use when: 設計 agent 系統架構、實作 tool orchestration、設計 context 管理策略、建構 agent loop。
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。
Audit and improve Generative Engine Optimization as a measurable discovery-to-citation pipeline. Use for /geo, AI-search visibility, citation readiness, and GEO benchmark work.
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, implement, or evaluate GEODE's GEO capability with source-grounded stage diagnosis, frozen measurement contracts, and no-model preflight before live engine runs.
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。
环境安装与准备：把部署环境从零安装就绪——mindiesd 编译安装（本地昇腾直装 / SSH 推远端容器 / Docker 镜像直装）与三方推理框架全栈安装（vLLM-Omni 源码构建、DiffSynth-Engine 部署、 LightX2V editable 部署），并负责模型权重确认与下载（下载前先确认远端是否已存在）。不含特性使能与验证 （framework-integration）与 profiling（profiling-collect）；SSH 工具由 remote-access 提供。 当用户需要安装 MindIE-SD、源码构建/直装 vLLM-Omni 或 LightX2V（editable + PLATFORM=ascend_npu）、 或确认/下载模型权重时使用此技能； 即使用户只提到"把代码推到服务器""在容器里装 vllm 全栈""准备 lightx2v 调优环境"而未说昇腾， 只要上下文涉及环境安装与准备都应触发。由 model-auto-optimization S0 与 dev-workflow 部署阶段指引加载。
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。
Comprehensive reference for inter-process communication mechanisms -- every way processes can talk on a computer. Covers sockets (TCP, UDP, Unix domain), WebSockets, SSE, pipes (named/anonymous), shared memory (mmap, shm_open), message queues, signals, D-Bus, XPC (macOS), gRPC, REST, stdin/stdout, file-based coordination, and clipboard. Performance benchmarks, platform availability, and code examples for each. Special focus on which IPC works best for AI agent coordination. Activate on: "IPC", "inter-process communication", "process communication", "how do I talk between processes", "Unix socket vs TCP", "shared memory", "named pipe", "WebSocket vs SSE", "gRPC vs REST", "agent coordination IPC", "XPC service", "message passing", "stdout pipe", "D-Bus", "mmap". NOT for: distributed systems design (use distributed-systems), network protocol design (use networking), message queue infrastructure like Kafka (use data-pipeline-engineer).
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。
Builds a candidate-controlled preparation plan for a software-engineering interview target, timeline, and practice evidence. Use for planning round coverage, story coherence, mock cadence, and debrief-driven adjustments. NOT for hiring predictions, candidate ranking, surveillance, or replacing round-specific coaching.
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。
iOS Core Data architect for persistent storage, CloudKit sync, schema migrations, and SwiftData migration. Activate on: Core Data, NSManagedObject, CloudKit sync, Core Data migration, NSFetchedResultsController, NSPersistentContainer, SwiftData migration path. NOT for: SwiftUI state management (use swiftui-data-flow-expert), server databases (use data-pipeline-engineer), SQLite direct (use mobile-offline-sync-architect).
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。
Build image generation pipelines with Stable Diffusion, FLUX, ControlNet, LoRA, and ComfyUI workflows. Activate on: image generation pipeline, ComfyUI workflow, ControlNet, LoRA training, diffusion model. NOT for: video generation (ai-video-production-master), image classification (computer-vision-pipeline).
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。
Optimize web images for performance with Next.js Image, responsive srcset, AVIF/WebP, lazy loading, and blur placeholders. Activate on: image optimization, LCP improvement, responsive images, AVIF, blur placeholder, next/image. NOT for: image generation/editing (use image-gen skills), SVG icon systems (use design-system-creator).
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 algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).
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。
Build face recognition systems with InsightFace, ArcFace, enrollment pipelines, HDBSCAN clustering, and privacy-compliant architecture. Activate on: face recognition, face enrollment, face clustering, identity verification, facial search. NOT for: general object detection (computer-vision-pipeline), emotion analysis (ai-engineer).
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。