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Build a tower defense: enemies pathing along lanes, wave spawning, towers that auto-target and fire, an economy, and lives. Use for a tower-defense/wave-defense game, or balancing waves and economy.

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Use Godot 4.7 physics bodies and detection in 2D and 3D: RigidBody, StaticBody, Area, and CharacterBody; collision layers vs masks; contact/overlap signals; and raycasts (RayCast nodes and direct space-state queries). Use when configuring collision layers/masks, detecting overlaps with Area2D/Area3D, applying forces to a RigidBody, or casting rays in a Godot project (.tscn with physics bodies), including 3D projects running Jolt Physics, the default 3D engine for projects created in 4.6+.

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Animate in Godot 4.7 three ways: AnimationPlayer for keyframed clips (incl. call and signal tracks), AnimationTree with state machines and blend spaces for character animation, and Tween for short procedural/UI tweens via create_tween(). Use when working with AnimationPlayer/AnimationTree nodes in a .tscn, blending character states, sprite-sheet animation, or code-driven Tweens.

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Design and harden Roblox client/server networking with RemoteEvent, RemoteFunction, and UnreliableRemoteEvent; server authority, argument and Instance validation, rate limits, proximity/ownership checks, targeted replication, streaming, lifecycle, prediction, and reconciliation. Use for Roblox remotes, exploits, request spam, multiplayer replication, network ownership, high-frequency cosmetic updates, or server/client desynchronization.

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Build production Roblox interfaces with ScreenGui/PlayerGui lifecycle, responsive UDim2 layouts, safe insets, reusable editable Instances, cross-device input and selection, restrained motion, cleanup, and multi-viewport verification. Use for Roblox HUDs, inventories, shops, settings, ability bars, ScrollingFrames, AutomaticSize, GuiService, gamepad focus, touch UI, or UI that clips, resets on respawn, looks generic, or was generated as one giant LocalScript.

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Write game shaders from cross-engine fundamentals — the vertex→fragment pipeline, coordinate spaces, UV math, and common 2D/3D effects (tint, UV scroll, dissolve, outline, fresnel rim, vignette) in GLSL with HLSL equivalents. Use when the user mentions shaders, fragment/pixel shader, vertex shader, UV, GLSL, HLSL, or effects like dissolve, outline, or rim light.

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Diagnose Day-2 AKS GPU and KAITO incidents using profile-aware, read-only evidence. WHEN: 'Insufficient nvidia.com/gpu', GPU pod Pending, model-load OOM, DCGM/VRAM, KAITO Workspace not ready, or GPU autoscaling. DO NOT USE FOR: setup (airunway-aks-setup), non-GPU incidents (aks-troubleshooting), standalone VM quota (azure-quotas), or generic cost (cost-analysis or cost-optimization from the optional azure-cost plugin).

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Analyze actual Azure spend, bill changes, and AKS or AI costs. WHEN: "Azure cost breakdown", "most expensive resources", "why did my bill increase", "cost spike", "unexpected charge", "Cosmos DB charges", "Foundry model costs", "AKS cluster cost", "namespace cost", "enable AKS cost analysis", "idle AKS capacity". DO NOT USE FOR: forecasts, pricing estimates, rightsizing, commitments, or budgets.

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Design Thinking learning curriculum covering nine progressive modules across the full Problem, Solution, and Implementation Space methods plus a shared manufacturing reference scenario for teaching and practice

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Mural workspace, room, mural, and widget workflows via the Mural REST API exposed through a Python CLI. Use when you need to read or write Mural content or automate widget creation.

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Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.

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Design and build the visual layer of a long-form talking-head video as one persistent world the camera moves through, instead of a sequence of separate graphics. Use when asked to edit a video, add motion graphics, design the visuals for a script, plan an edit, or fix an edit judged confusing, text-heavy, hard to follow, or "clearly made by an AI". Covers the persistent world, camera grammar, the spotlight rule, spatial open loops, ornament and token discipline, transcript timing, and the verification gates.

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Converts DOCX/PDF/PPTX and saved HTML/HTM to high-quality Markdown with automatic post-processing. Fixes pandoc grid tables, simple tables, image paths, CJK bold spacing, attribute noise, and code blocks; for PDFs also strips OCR garbage blocks, repeated headers/footers/watermarks, and absolute image paths from pymupdf4llm output. Benchmarked best-in-class (7.6/10) against Docling, MarkItDown, Pandoc raw, and Mammoth. Trigger on "convert document", "docx to markdown", "parse word", "doc to markdown", "解析word", "转换文档", "HTML to Markdown".

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Bridges Claude Code and Codex sessions when native tools can't reach the target. Use for 跨产品通信 / 脚本回帖 / 送达排查: cross-product messages, hooks or scripts posting to a session, verifying delivery (Held messages included), shared-work ownership, or an inbound peer assertion. Not for same-product or parent/subagent messaging (use native tools), spawning agents, moving full history, or cross-machine pairing (deprecated).

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Multi-path parallel product analysis with cross-model test-time compute scaling. Spawns parallel agents (Claude Code agent teams + Codex CLI) to explore product from multiple perspectives, then synthesizes findings into actionable optimization plans. Can invoke competitors-analysis for competitive benchmarking. Use when "product audit", "self-review", "发布前审查", "产品分析", "analyze our product", "UX audit", or "信息架构审计".

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。

Compress a document, thread, transcript, or pasted text into a scannable TL;DR.

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Answer in the ADHD-friendly Attention-kind style for the rest of this chat.

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Answer in the Rundown briefing style (TL;DR + checklist + numbered choices).

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Stress-test a decision or claim through a sharp, fair Q&A — the questions a good lawyer or skeptical friend would ask before you commit. Use when asked to cross-examine me, ask me hard questions about this, interrogate my plan, or make me defend this. Produces a sequenced line of probing questions (from clarifying to challenging to the killer question), space to answer, and a debrief on where your answers were strong, evasive, or exposed a gap — so weaknesses surface in private before they surface in public.

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Use when asked 大模型备案怎么做, 生成式人工智能服务备案还是登记, 算法备案流程, AI 生成内容标识怎么加, 我们接了已备案的大模型还要备案吗, or prepare a generative AI product for launch in mainland China. Produces the obligations that apply (generative AI service filing, registration for apps built on a filed model, algorithm filing, AI-generated content labelling), the documents and security self-assessment to prepare, a launch checklist, and the questions for the provincial cyberspace administration, each marked to confirm.

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Run a real brainstorm — divergent generation without judgment, then convergent selection with explicit criteria — instead of listing ten obvious ideas and calling it creativity. Use when asked to brainstorm, generate ideas or options, explore a solution space, or name something. Produces a genuinely wide option set (including the weird tail), then a shortlist selected against named criteria with the rejects preserved.

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Get your attention back with a 30-day protocol that assumes you'll break it — a screen-time ledger without moralizing, friction engineering (what to delete, grayscale, where the phone sleeps), planned relapses, and honest replacement activities for the boredom that shows up on day 3. Use when someone says 'my screen time is 7 hours', 'I want a dumbphone', 'digital detox', 'I can't read books anymore', or 'my attention span is gone'. Produces the ledger, a personal friction plan, and the 30-day protocol with expected failure points.

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Decide when and how your product and communications must (or should) label AI-generated content, and write the disclosure policy — surface-by-surface rules, exact label wording, and the review trigger for regulations like the EU AI Act's transparency obligations. Use when asked 'do we have to label AI content', 'write our AI disclosure policy', 'are we covered for the AI Act', or when marketing/support/product start shipping AI-generated output. Produces a disclosure policy with a per-surface matrix and ready-to-use label copy. Not legal advice.

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Specify the tracing, metrics, and alerting for an AI agent or LLM feature in production. Use when asked what to log for an LLM app, design agent tracing or spans, define quality and cost monitors, or answer 'how do we know if the agent is misbehaving?'. Produces an observability spec with a trace schema, metric definitions with owners and alert thresholds, sampling and retention policy, and a privacy note for logged content.

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。