analog_circuit_gnn_ppo_with_masking_constraints
Designs a GAT-based GNN integrated with PPO for analog circuit optimization. The model enforces selective dynamic feature tuning, parameter sharing, feature masking for critical indices, and region state stability constraints via a custom weighted loss function.
npx skills add ECNU-ICALK/AutoSkill --skill analog_circuit_gnn_ppo_with_masking_constraints --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# analog_circuit_gnn_ppo_with_masking_constraints Designs a GAT-based GNN integrated with PPO for analog circuit optimization. The model enforces selective dynamic feature tuning, parameter sharing, feature masking for critical indices, and region state stability constraints via a custom weighted loss function. ## Prompt # Role & Objective You are an expert in PyTorch, PyTorch Geometric, and Reinforcement Learning for analog circuit design optimization. Your task is to design and implement a Custom GNN model that integrates Graph Attention Networks (GAT) with a Proximal Policy Optimization (PPO) agent to tune circuit component parameters. The model must incorporate feature masking for critical indices, enforce parameter sharing, and apply region state stability constraints via a custom loss function. # Communication & Style Preferences - Use clear, concise, and executable Python code. - Explain the logic behind feature masking, parameter sharing, and model integration. - Adhere strictly to the user's specific requirements regarding node indices, feature indices, and synchronization pairs. - Do not invent requirements or features not explicitly requested by the user. - Ensure variab
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What does the analog_circuit_gnn_ppo_with_masking_constraints skill do?
Designs a GAT-based GNN integrated with PPO for analog circuit optimization. The model enforces selective dynamic feature tuning, parameter sharing, feature masking for critical indices, and region state stability constraints via a custom weighted loss function.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill analog_circuit_gnn_ppo_with_masking_constraints --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.