2d-games
免费2D game development principles. Sprites, tilemaps, physics, camera.
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Spot patterns appearing in 3+ domains to find universal principles
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
When the same pattern appears in 3+ domains, it's probably a universal principle worth extracting.
Core principle: Find patterns in how patterns emerge.
| Pattern Appears In | Abstract Form | Where Else? |
|---|---|---|
| CPU/DB/HTTP/DNS caching | Store frequently-accessed data closer | LLM prompt caching, CDN |
| Layering (network/storage/compute) | Separate concerns into abstraction levels | Architecture, organization |
| Queuing (message/task/request) | Decouple producer from consumer with buffer | Event systems, async processing |
| Pooling (connection/thread/object) | Reuse expensive resources | Memory management, resource governance |
Pattern spotted: Rate limiting in API throttling, traffic shaping, circuit breakers, admission control
Abstract form: Bound resource consumption to prevent exhaustion
Variation points: What resource, what limit, what happens when exceeded
New application: LLM token budgets (same pattern - prevent context window exhaustion)
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2D game development principles. Sprites, tilemaps, physics, camera.
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3D game development principles. Rendering, shaders, physics, cameras.
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Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.
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Use when implementing or managing persistent, hierarchical memory systems for AI agents. Covers cross-session state, fact supersession, and self-managed memory tools to enable long-term recall and adaptive agent behavior.
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Use when monitoring, tracing, or debugging agentic workflows in production. Keywords: observability, tracing, OpenTelemetry, Langfuse, latency, token cost, loop detection, telemetry.
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Use when building self-correcting retrieval systems for AI agents. Keywords: RAG, retrieval, Corrective RAG, Self-RAG, query decomposition, reranking, hallucination, grounding.
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