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Expert guidance for Next.js Cache Components and Partial Prerendering (PPR). **PROACTIVE ACTIVATION**: Use this skill automatically when working in Next.js projects that have `cacheComponents: true` in their next.config.ts/next.config.js. When this config is detected, proactively apply Cache Components patterns and best practices to all React Server Component implementations. **DETECTION**: At the start of a session in a Next.js project, check for `cacheComponents: true` in next.config. If enabled, this skill's patterns should guide all component authoring, data fetching, and caching decisions. **USE CASES**: Implementing 'use cache' directive, configuring cache lifetimes with cacheLife(), tagging cached data with cacheTag(), invalidating caches with updateTag()/revalidateTag(), optimizing static vs dynamic content boundaries, debugging cache issues, and reviewing Cache Component implementations.

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Expert testing decision trees for GdUnit4: unit vs scene vs CI gates, headless runners, snapshots, and mock networks. Use when choosing test layers, wiring CI, or validating signals/physics without beginner assert catalogs. Keywords: GdUnit4, GdUnitTestSuite, headless CI, snapshot test, mock network, scene integration test, TDD.

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 blueprint for open world games including chunk-based streaming (load/unload regions dynamically), floating origin (prevent precision jitter beyond 5000 units), HLOD (hierarchical LOD for distant meshes), persistent state (track entity changes across unloaded chunks), POI discovery systems (compass, markers), and threaded loading (prevent stutters). Use for RPGs, sandboxes, or exploration games. Trigger keywords: open_world, chunk_streaming, floating_origin, HLOD, persistent_state, POI_discovery, threaded_loading.

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 patterns for Godot 3D PBR materials using StandardMaterial3D including albedo, metallic/roughness workflows, normal maps, ORM texture packing, transparency modes, and shader conversion. Use when creating realistic 3D surfaces, PBR workflows, or material optimization. Trigger keywords: StandardMaterial3D, BaseMaterial3D, albedo_texture, metallic, metallic_texture, roughness, roughness_texture, normal_texture, normal_enabled, orm_texture, transparency, alpha_scissor, alpha_hash, cull_mode, ShaderMaterial, shader parameters.

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。

Start (or restart) a local multi-node Temps cluster using Docker-in-Docker — one control plane + 3 worker nodes, each a privileged DinD container running its own dockerd + `temps agent`, wired with the real multi-host overlay (VXLAN, compute_cidr allocation) via `tools/dev-cluster/` in whichever checkout/worktree you run it from. Invoke when the user says "start the temps cluster", "spin up a multi-node dev cluster", "test this with multiple workers", "bring up worker nodes locally", "docker in dind cluster", or wants to verify cross-node behavior (node targeting, cluster DNS, overlay networking, node join/mTLS) that a single-node `start-temps` server cannot exercise. Distinct from `start-temps` (single-node, native binary, port-slot based) and `start-temps-ee` (single-node EE binary) — this is the only path that actually has more than one node.

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 designing a new Godot feature or system — guides scene tree planning, node type selection, and architectural decisions

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Use when a new Godot system or feature has open design decisions — interrogates them in batched rounds, scope first, each question with a recommended answer, and records the answers before any design or code. Triggers on "grill me", "ask me first", "question me on the design", "what do I need to decide before building". Not for choosing between nodes or APIs, and not for bug fixes.

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。

Investment Committee memo drafting for China-focused PE/IB deals. Adapted from the original ic-memo skill for Chinese market context, terminology, and decision frameworks. Triggers on "A股投委会", "IC Memo", "investment memo China", "投委会材料", "IC memo A-share", or "committee memo [company]".

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Build Baidu ERNIE-Image / ERNIE-Image-Turbo workflows, primarily TEXT-TO-IMAGE. Pick ERNIE when you need precise multilingual text rendering, posters/signage, manga/anime multi-panel layouts, or strong instruction following for complex multi-object scenes. Also supports denoise-based image-to-image refine (NOT instruction-grounded editing; use Qwen-Image-Edit or Flux Kontext for "change X in this photo" edits).

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。

Pick the right ComfyUI startup flags for VRAM, attention, caching, and speed. The full decision matrix for OOM (--novram / --cache-none / --disable-smart-memory), shared-VRAM creep on Windows (--reserve-vram N), model-switching with big text encoders (--cache-none), high-VRAM throughput (--gpu-only / --highvram), and attention-backend selection (--use-sage-attention for speed, --use-pytorch-cross-attention as the highest-quality / Z-Image-safe fallback). Also the acceleration-stack + Blackwell/RTX 5000 (sm_120) notes. Use when a graph OOMs (especially long video like LTX 2 / WAN), when the GPU spills into shared VRAM and slows to a crawl, when switching between models eats all RAM, when Z-Image produces black/garbled output under Sage, or when deciding which attention backend to launch with. Flag names verified against upstream comfy/cli_args.py; see Sources.

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 text, images, video, and audio, and make typed decisions from the terminal.

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。

Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

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。

Designing for visionOS — spatial layout and ergonomics (60pt eye targets, field-of-view placement, dynamic scale), eyes-and-hands input with hover rules, motion and visual comfort (the 0.2 Hz rule, vection, depth-cue agreement), immersion strategy, spatial sound, and the video-format decision guide. Use when designing or reviewing any visionOS app, window, volume, or immersive experience.

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。

SwiftUI's actual mental model — view identity, lifetime, and dependencies (the Demystify canon), state ownership decision rules, Observation's per-property tracking, body-performance discipline, and the main-actor concurrency contract. Use when state resets mysteriously, views re-render too often, animations glitch between branches, choosing @State vs @Bindable vs plain property, or debugging "why did body run."

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。

Beta testing strategy for iOS/macOS apps. Covers TestFlight program setup, beta tester recruitment, feedback collection methodology, user interviews, signal-vs-noise interpretation, and go/no-go launch readiness decisions. Use when planning a beta, setting up TestFlight, collecting user feedback, or deciding if ready to launch.

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。

Guides you through comprehensive iOS/Swift app planning and analysis. Use for new apps (concept to architecture) or existing apps (audit current state, plan improvements, evaluate tech stack). Covers product planning, technical decisions, UI/UX design, and distribution strategy.

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。

SwiftData skills — class inheritance patterns for hierarchical models, type-based querying, polymorphic relationships, and inheritance-vs-enum decisions. Use when designing SwiftData model hierarchies.

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 ML, Create ML, Vision framework, Natural Language framework, on-device ML integration. Use when user wants image classification, text analysis, object detection, sound classification, model optimization, or custom model integration. Covers Core ML vs Foundation Models decision.

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。

UI typography on Apple platforms — text styles and Dynamic Type, the San Francisco family (Pro/Rounded/Compact/Mono/New York + width axis), optical sizes, tracking vs kerning, leading adjustments, and custom-font scaling. Use when choosing fonts, building type hierarchy, fixing truncation/legibility, or making custom fonts respect Dynamic Type.

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。

When the user wants help with revenue operations, lead lifecycle management, or marketing-to-sales handoff processes. Also use when the user mentions 'RevOps,' 'revenue operations,' 'lead scoring,' 'lead routing,' 'MQL,' 'SQL,' 'pipeline stages,' 'deal desk,' 'CRM automation,' 'marketing-to-sales handoff,' or 'data hygiene.' For cold outreach emails, see cold-email. For email drip campaigns, see email-sequence. For pricing decisions, see pricing-strategy.

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。

Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.

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When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' or 'consumer behavior.' This skill provides 70+ mental models organized for marketing application.

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。

When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' or 'monetization.' This skill covers pricing research, tier structure, and packaging strategy.

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。