rl_reward_circuit_mixed_optimization
Implements a reinforcement learning reward function for analog circuit design that handles mixed minimization/maximization objectives using normalized differences, while incorporating transistor saturation tracking to ensure stability.
npx skills add ECNU-ICALK/AutoSkill --skill rl_reward_circuit_mixed_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.
# rl_reward_circuit_mixed_optimization Implements a reinforcement learning reward function for analog circuit design that handles mixed minimization/maximization objectives using normalized differences, while incorporating transistor saturation tracking to ensure stability. ## Prompt Follow this SOP (replace specifics with placeholders like <PROJECT>/<ENV>/<VERSION>): 1) Offline OpenAI-format conversation source. 2) Title: ae325e3c206735467f9648f983310cdd.json#conv_1 3) Use the user questions below as the PRIMARY extraction evidence. 4) Use the full conversation below as SECONDARY context reference. 5) In the full conversation section, assistant/model replies are reference-only and not skill evidence. 6) Primary User Questions (main evidence): 7) My Objective: 8) We aim to optimize the '7' objective performance metric values of 'area', 'power dissipation', 'DC gain', 'Slew rate', '3db frequency', 'Unity gain bandwidth', and 'Phase margin' (multi-objectives) of a pre-determined two stage operational amplifier circuit topology. We need to optimally configure/tune the design device parameters (device dimensions and values, as variables) such as transistor dimensions ['L1', 'L3', 'L5',
- Prompt
- Triggers
- Examples
- Example 1
What does the rl_reward_circuit_mixed_optimization skill do?
Implements a reinforcement learning reward function for analog circuit design that handles mixed minimization/maximization objectives using normalized differences, while incorporating transistor saturation tracking to ensure stability.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill rl_reward_circuit_mixed_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.