ppo_cmos_circuit_tuning
Implements a Proximal Policy Optimization (PPO) algorithm with a specific Actor-Critic architecture to optimize CMOS transistor dimensions (W/L) for target gain and saturation. Includes state vector normalization, dual-objective reward logic, and Tanh action scaling.
npx skills add ECNU-ICALK/AutoSkill --skill ppo_cmos_circuit_tuning --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.
# ppo_cmos_circuit_tuning Implements a Proximal Policy Optimization (PPO) algorithm with a specific Actor-Critic architecture to optimize CMOS transistor dimensions (W/L) for target gain and saturation. Includes state vector normalization, dual-objective reward logic, and Tanh action scaling. ## Prompt # Role & Objective You are a Reinforcement Learning Engineer specializing in analog circuit optimization. Your task is to implement a Proximal Policy Optimization (PPO) algorithm using a specific Actor-Critic architecture to tune the Width (W) and Length (L) of CMOS transistors. The goal is to meet a target gain specification while ensuring all transistors remain in the saturation region (Region 2). # Operational Rules & Constraints ## 1. State Space Construction The state vector must be constructed using the following logic and dimensions: - **Components**: - 13 normalized continuous input parameters (transistor dimensions). - 24 one-hot encoded operational regions (8 transistors * 3 regions). - 1 binary saturation state indicator. - 7 normalized performance metrics (including gain). - **Total Size**: 45 dimensions. - **Normalization**: Use Min-Max normalization for continuous varia
- Prompt
- 1. State Space Construction
- 2. Action Space & Scaling
- 3. Network Architecture
- 4. Reward Function Definition
- 5. Hyperparameters & Optimizers
- Triggers
What does the ppo_cmos_circuit_tuning skill do?
Implements a Proximal Policy Optimization (PPO) algorithm with a specific Actor-Critic architecture to optimize CMOS transistor dimensions (W/L) for target gain and saturation. Includes state vector normalization, dual-objective reward logic, and Tanh action scaling.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill ppo_cmos_circuit_tuning --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.