「systems」の検索結果

Use when working with 2D-specific systems — TileMaps, parallax scrolling, 2D lights and shadows, canvas layers, particles 2D, custom drawing, and 2D meshes in Godot 4.3+

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 managing dependencies between systems — autoloads, service locators, @export injection, and scene injection patterns

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。

Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI 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。

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay 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。

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

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 adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive 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。

Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).

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 persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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Generates a variable reward system with randomized rewards, daily bonuses, mystery box mechanics, and ethical engagement caps. Use when user wants daily spins, mystery boxes, random rewards, or gamification reward 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。

Code generator skills that produce production-ready Swift code for common app components. Use when user wants to add logging, analytics, onboarding, review prompts, networking, authentication, paywalls, settings, persistence, error monitoring, CI/CD pipelines, localization, push notifications, deep linking, testing, accessibility, widgets, feature flags, app icons, image caching, pagination, HTTP caching, share cards, social export, subscription lifecycle, referral systems, watermarks, streak tracking, milestone celebrations, what's new screens, lapsed user re-engagement, usage insights, variable rewards, consent flows, account deletion, permission priming, force updates, state restoration, debug menus, offline queues, feedback forms, announcement banners, quick win sessions, Spotlight indexing, App Clips, screenshot automation, background processing, app extensions, data export, or SwiftUI preview sample data and variant matrices.

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Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data fr

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 knowledge for Azure Managed Lustre development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when mounting AML filesystems, linking Blob, using AKS CSI, setting quotas, or tuning Lustre performance, and other Azure Managed Lustre related development tasks. Not for Azure NetApp Files (use azure-netapp-files), Azure Virtual Machines (use azure-virtual-machines), Azure Virtual Network (use azure-virtual-network), Azure Virtual Network Manager (use azure-virtual-network-manager).

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。

Define KPIs, metrics, and tracking systems to measure brand health, awareness, perception, and equity over time. Use when the user says "brand measurement", "brand metrics", "how do we measure the brand", "brand KPIs", "brand health tracking", "brand awareness metrics", "brand equity measurement", "is our brand working", "brand ROI", "brand performance", "brand tracking", "brand health score", "prove brand value", "brand dashboard", or needs to show stakeholders whether brand investment is delivering results.

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。

Test the boundary between two systems by the contract, not the implementation. Use for APIs, integrations, and shared interfaces.

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 lead generation systems using Alex Hormozi's Core Four framework (warm outreach, content, cold outreach, paid ads), lead magnets, and Rule of 100. Use when designing customer acquisition, scaling advertising, choosing marketing channels for revenue stage, building referral programs, or fixing low lead flow. Covers lead magnets, LTGP:CAC math, More Better New scaling, and Lead Getters (referrals, employees, agencies, affiliates).

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Cialdini's seven principles of compliance (the classic six — reciprocation, commitment-consistency, social proof, liking, authority, scarcity — plus Unity, added 2016) and Pre-Suasion. Use when crafting ethical persuasion (marketing, sales, negotiation), defending against manipulation, or designing systems that need cooperation. Includes replication-crisis caveats and known factual corrections (Bickman parking-meter numbers, Hofling modern replication, Drive Carefully condition disambiguation, Genovese myth).

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 referral programs and viral growth loops. Use when asked to create referral systems, viral mechanics, word-of-mouth campaigns, or growth loops. Trigger phrases: "referral program", "viral loop", "refer a friend", "word of mouth", "viral coefficient", "referral incentive", "growth loop", "invite system", "ambassador program".

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Guides using eRPC (electron-tRPC) for inter-process communication (IPC) between Electron main and renderer processes. Triggered when handling Electron IPC or tRPC-based 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。

Knowledge base from "Solving a Million-Step LLM Task with Zero Errors" (Meyerson, Paolo, Dailey, Shahrzad, Francon, Hayes, Qiu, Hodjat, Miikkulainen — arXiv:2511.09030). Use when designing long-horizon, multi-step agentic LLM pipelines that need very high reliability, applying Maximal Agentic Decomposition (MAD), first-to-ahead-by-k voting, or red-flagging error correction, estimating cost/reliability scaling laws for multi-agent systems, or studying Massively Decomposed Agentic Processes (MDAPs).

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 authorized assessment of federated identity systems including SAML, OIDC, OAuth2 flows, SSO misconfiguration, and token confusion issues.

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Use when starting any task, before invoking any other skill or GSD command, to route between Superpowers skills and GSD commands from project state and task scope. Fires first when both systems are installed.

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。

Your software-engineering brain trust. Named personas across 11 cells covering cloud architecture, reliability/SRE/observability, data and storage, security, FinOps, languages and runtimes, systems programming, web and frontend…

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 self-contained HTML visual explanations for systems, code changes, plans, data, and technical concepts. Use for diagrams, architecture overviews, diff or plan reviews, project recaps, comparison tables, slide decks, and other visual explanations.

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Your product + design brain trust and decision partner. Named personas across 8 cells spanning product strategy, discovery/research, growth/metrics, design foundations/usability, design leadership/craft, UX/content, design systems, and a…

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。