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claude-dev-suite
GitHub creator profile

claude-dev-suite

Repository-level view of 702 collected skills across 1 GitHub repositories.

skills collected
702
repositories
1
updated
Jun 1, 2026
repository explorer

Repositories and representative skills

cyber-physical
electrical-engineers

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…

Jun 1, 2026
distributed-ledger
engineers-all-other

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…

Jun 1, 2026
game-engine-architecture
engineers-all-other

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.…

Jun 1, 2026
agentic-architecture
engineers-all-other

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:…

Jun 1, 2026
ai-hardware-selection
computer-hardware-engineers-172061

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…

Jun 1, 2026
edge-inference
computer-hardware-engineers-172061

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,…

Jun 1, 2026
hybrid-edge-cloud
computer-hardware-engineers-172061

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…

Jun 1, 2026
inference-serving-topology
engineers-all-other

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…

Jun 1, 2026
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