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claude-dev-suite
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claude-dev-suite

1개 GitHub 저장소에서 수집된 702개 skills를 저장소 단위로 보여줍니다.

수집된 skills
702
저장소
1
업데이트
2026년 6월 1일
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저장소와 대표 skills

cyber-physical
전기 엔지니어

Cyber-physical / industrial control system (ICS-SCADA) architecture in general: the Purdue model levels, control loops, IT/OT convergence, OT security (IEC 62443, segmentation, zero-trust for OT), safety, determinism, and redundancy. Architect-level, beyond…

2026년 6월 1일
distributed-ledger
기타 엔지니어

Distributed-ledger / blockchain architecture in general (engine-agnostic): when a ledger beats a database, consensus families (PoW/PoS/BFT), L1 vs L2 (rollups, channels, sidechains), permissioned vs permissionless, UTXO vs account models, and the scalability…

2026년 6월 1일
game-engine-architecture
기타 엔지니어

Game-engine architecture, engine-agnostic: the game loop (fixed vs variable timestep), ECS vs scene-graph/OOP, the render pipeline, core subsystems (physics, audio, animation, assets, memory), and netcode models. Architect- level, beyond any specific engine.…

2026년 6월 1일
agentic-architecture
기타 엔지니어

Architecture of LLM agent systems: orchestration topologies (single agent, supervisor/sub-agents, pipelines, networks), memory/context strategy, the tool layer, and human-in-the-loop/control. Architect-level system design, not prompt wording. USE WHEN:…

2026년 6월 1일
ai-hardware-selection
컴퓨터 하드웨어 엔지니어

Selecting accelerators for AI workloads: GPU vs TPU vs NPU vs FPGA vs CPU, and the metrics that actually decide it — memory capacity & bandwidth, TOPS/ FLOPS, interconnect, and cost/Watt. Architect-level hardware-fit reasoning. USE WHEN: choosing AI…

2026년 6월 1일
edge-inference
컴퓨터 하드웨어 엔지니어

Edge / on-device AI inference architecture: running models on MCUs, NPUs, mobile, and mini-PCs; quantization for edge, TOPS/memory/energy budgets, TinyML, and the latency case for on-device vs cloud. Architect-level. USE WHEN: designing on-device/edge AI,…

2026년 6월 1일
hybrid-edge-cloud
컴퓨터 하드웨어 엔지니어

Hybrid edge-cloud AI architecture: local-first inference with cloud escalation, model cascading, and splitting the workload across device and datacenter to balance latency, cost, privacy, and quality. Architect-level topology. USE WHEN: designing systems that…

2026년 6월 1일
inference-serving-topology
기타 엔지니어

LLM/model inference serving architecture: the engine → serving → orchestration layering (vLLM/SGLang/TensorRT-LLM, Triton, KServe/Ray Serve), KV-cache & continuous batching, prefill-decode disaggregation, and scaling. Architect-level topology, not model…

2026년 6월 1일
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