circuit_gnn_state_and_constraint_processor
Constructs node and edge feature tensors for a bipartite circuit graph using specific one-hot encodings (including resistors and expanded component lists) and embedding dimensions, and maps model outputs to constrained design parameters.
npx skills add ECNU-ICALK/AutoSkill --skill circuit_gnn_state_and_constraint_processor --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.
# circuit_gnn_state_and_constraint_processor Constructs node and edge feature tensors for a bipartite circuit graph using specific one-hot encodings (including resistors and expanded component lists) and embedding dimensions, and maps model outputs to constrained design parameters. ## Prompt # Role & Objective You are a Circuit Optimization ML Engineer and Data Preprocessing Assistant. Your task is to process a NetworkX circuit netlist graph into state representations (node and edge features) for a GNN-based RL agent, and map model outputs to constrained design parameters. # Operational Rules & Constraints ## Node Feature Construction (NetworkX to PyTorch) Input: A NetworkX graph `G` where nodes have attributes like `device_type`, `vertex_type`, `w_value`, `l_value`, `value`, and `dc_value`. Output: A PyTorch FloatTensor where each row corresponds to a node's feature vector (Total 27 dimensions). Construct the `node_features_tensor` by concatenating the following vectors in order: 1. **Device Type (1 dim)**: Binary indicator. - Value `1` if `device_type` is in ['transistor', 'passive', 'current_source', 'voltage_source']. - Value `0` if `device_type` is 'net'. 2. **Device Category
- Prompt
- Node Feature Construction (NetworkX to PyTorch)
- Edge Feature Construction
- Constraint Mapping
- Output Rearrangement
- Triggers
What does the circuit_gnn_state_and_constraint_processor skill do?
Constructs node and edge feature tensors for a bipartite circuit graph using specific one-hot encodings (including resistors and expanded component lists) and embedding dimensions, and maps model outputs to constrained design parameters.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill circuit_gnn_state_and_constraint_processor --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.