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game-development
Game development orchestrator. Routes to platform-specific skills based on project needs.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Game development orchestrator. Routes to platform-specific skills based on project needs.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Persistent dietary safety layer for DoorDash CLI (dd-cli) ordering. Stores a personal/household dietary profile (allergens with severity tiers, diets, dislikes) that every cart is vetted against before checkout, with a deterministic tripwire — the vetting step saves the full cart contents to a vetted-cart dump, and a PreToolUse hook re-greps that dump against the anaphylaxis-severity allergen list before any checkout URL is allowed. Use when the user mentions allergies or dietary restrictions, when ordering food for someone with restrictions, or on every dd-cli cart flow while this skill is installed. A tripwire, not medical-grade — the human checkout page is the final check.
Group food ordering through the DoorDash CLI (dd-cli) from a persistent team roster. One request ("lunch for the team") fans out into a single merged cart with every line attributed to its eater via a person-to-cart-item-id ledger, per-person cost split with fee proration, payer rotation history, and a checkout gate that re-derives allergen conflicts against the roster live. Use when ordering for multiple people — team lunch, incident-response food, "collect orders from the thread" — or for /whose-turn payer rotation questions. Handles paste-a-thread intake: paste a Slack/chat thread and it builds the order ledger from it.
Accountability layer for agent-driven DoorDash ordering. Works with the doordash-audit-log hook (which appends every dd-cli invocation to an append-only audit log) and the /doordash-report command to answer "what has my AI been ordering and what did it cost" from data instead of memory. Use when the user asks about their DoorDash spending, ordering patterns, what the agent did in past sessions, or wants jq recipes for querying the audit log. Covers log schema, query patterns, rotation, and privacy guidance.
Named, context-bound saved DoorDash orders ("post-gym", "late-night deploy") recalled through the DoorDash CLI (dd-cli) with a mandatory cart-diff before any checkout link is handed over. Use when the user names a saved order ("order my post-gym bowl", "the usual", "my Friday ramen"), wants to save the order they just placed as a playbook, or asks to list/remove saved orders. Persists name→order-uuid playbooks across sessions, catches silent menu/price drift on every recall, and self-heals stale playbooks when a restaurant's menu changes. Requires dd-cli (macOS Apple Silicon, waitlist-gated).
Hard spending policy for agent-driven DoorDash ordering through the DoorDash CLI (dd-cli). Per-order ceiling, daily/weekly/monthly caps, cooldown between orders, and blocked hours — enforced deterministically by routing every cart-mutation and checkout through the dd-guard wrapper script, which prices the cart, checks the policy against a persistent spend ledger, and refuses out-of-policy checkouts with exit code 2. Use when the user wants budget limits on agent food ordering, asks "how much have I spent on DoorDash", wants to set spending caps, or whenever building carts / checking out with dd-cli while this skill is installed. Pairs with a PreToolUse hook that denies raw dd-cli checkout calls that bypass the wrapper.
Segment a SKU portfolio on value (ABC) and demand variability (XYZ), produce the 9-box with a planning policy per cell, and reallocate planner attention accordingly. Use when the user mentions ABC analysis, inventory segmentation, SKU rationalization, stok segmentasyonu, envanter sınıflandırma, or asks which items deserve forecasting effort. Differentiator - ABC alone is treated as half an answer; policy lives in the value x variability combination.
| name | game-development |
| description | Game development orchestrator. Routes to platform-specific skills based on project needs. |
| allowed-tools | Read, Write, Edit, Glob, Grep, Bash |
Orchestrator skill that provides core principles and routes to specialized sub-skills.
You are working on a game development project. This skill teaches the PRINCIPLES of game development and directs you to the right sub-skill based on context.
| If the game targets... | Use Sub-Skill |
|---|---|
| Web browsers (HTML5, WebGL) | game-development/web-games |
| Mobile (iOS, Android) | game-development/mobile-games |
| PC (Steam, Desktop) | game-development/pc-games |
| VR/AR headsets | game-development/vr-ar |
| If the game is... | Use Sub-Skill |
|---|---|
| 2D (sprites, tilemaps) | game-development/2d-games |
| 3D (meshes, shaders) | game-development/3d-games |
| If you need... | Use Sub-Skill |
|---|---|
| GDD, balancing, player psychology | game-development/game-design |
| Multiplayer, networking | game-development/multiplayer |
| Visual style, asset pipeline, animation | game-development/game-art |
| Sound design, music, adaptive audio | game-development/game-audio |
Every game, regardless of platform, follows this pattern:
INPUT → Read player actions
UPDATE → Process game logic (fixed timestep)
RENDER → Draw the frame (interpolated)
Fixed Timestep Rule:
| Pattern | Use When | Example |
|---|---|---|
| State Machine | 3-5 discrete states | Player: Idle→Walk→Jump |
| Object Pooling | Frequent spawn/destroy | Bullets, particles |
| Observer/Events | Cross-system communication | Health→UI updates |
| ECS | Thousands of similar entities | RTS units, particles |
| Command | Undo, replay, networking | Input recording |
| Behavior Tree | Complex AI decisions | Enemy AI |
Decision Rule: Start with State Machine. Add ECS only when performance demands.
Abstract input into ACTIONS, not raw keys:
"jump" → Space, Gamepad A, Touch tap
"move" → WASD, Left stick, Virtual joystick
Why: Enables multi-platform, rebindable controls.
| System | Budget |
|---|---|
| Input | 1ms |
| Physics | 3ms |
| AI | 2ms |
| Game Logic | 4ms |
| Rendering | 5ms |
| Buffer | 1.67ms |
Optimization Priority:
| AI Type | Complexity | Use When |
|---|---|---|
| FSM | Simple | 3-5 states, predictable behavior |
| Behavior Tree | Medium | Modular, designer-friendly |
| GOAP | High | Emergent, planning-based |
| Utility AI | High | Scoring-based decisions |
| Type | Best For |
|---|---|
| AABB | Rectangles, fast checks |
| Circle | Round objects, cheap |
| Spatial Hash | Many similar-sized objects |
| Quadtree | Large worlds, varying sizes |
| Don't | Do |
|---|---|
| Update everything every frame | Use events, dirty flags |
| Create objects in hot loops | Object pooling |
| Cache nothing | Cache references |
| Optimize without profiling | Profile first |
| Mix input with logic | Abstract input layer |
→ Start with game-development/web-games for framework selection
→ Then game-development/2d-games for sprite/tilemap patterns
→ Reference game-development/game-design for level design
→ Start with game-development/mobile-games for touch input and stores
→ Use game-development/game-design for puzzle balancing
→ game-development/vr-ar for comfort and immersion
→ game-development/3d-games for rendering
→ game-development/multiplayer for networking
Remember: Great games come from iteration, not perfection. Prototype fast, then polish.