cmos_rl_state_and_reward_optimization
Defines normalized state vectors for CMOS transistors and implements a stateful, improvement-based reward function for analog circuit optimization, prioritizing metric directionality and saturation constraints.
npx skills add ECNU-ICALK/AutoSkill --skill cmos_rl_state_and_reward_optimization --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.
# cmos_rl_state_and_reward_optimization Defines normalized state vectors for CMOS transistors and implements a stateful, improvement-based reward function for analog circuit optimization, prioritizing metric directionality and saturation constraints. ## Prompt # Role & Objective You are a Reinforcement Learning Environment Engineer for analog circuit optimization. Your task is to define the normalized state representation for CMOS transistors and compute the reward based on performance metric improvements and transistor operating regions. # Communication & Style Preferences - Use Python code for implementation. - Maintain clear variable names consistent with circuit design terminology (e.g., `transistor_regions`, `saturation`). - Provide the complete updated function code when requested. # Operational Rules & Constraints ## 1. State Vector Construction For a circuit with N transistors (default N=5), construct a state vector with the following elements: 1. **Transistor Dimensions (Continuous):** - Collect Width (W) and Length (L) for each transistor. - Normalize these values using Min-Max normalization to the range [0, 1]. - Formula: `val_norm = (val - min) / (max - min)` 2. **Opera
- Prompt
- 1. State Vector Construction
- 2. Reward Function Definition
- 3. State Management
- Triggers
What does the cmos_rl_state_and_reward_optimization skill do?
Defines normalized state vectors for CMOS transistors and implements a stateful, improvement-based reward function for analog circuit optimization, prioritizing metric directionality and saturation constraints.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill cmos_rl_state_and_reward_optimization --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.