circuit_graph_node_feature_extraction
Extracts and transforms circuit graph node attributes from a NetworkX graph into a fixed 27-dimension PyTorch tensor vector suitable for Graph Neural Networks, handling one-hot encodings for device types, component indices, and conditional scalar values.
npx skills add ECNU-ICALK/AutoSkill --skill circuit_graph_node_feature_extraction --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_graph_node_feature_extraction Extracts and transforms circuit graph node attributes from a NetworkX graph into a fixed 27-dimension PyTorch tensor vector suitable for Graph Neural Networks, handling one-hot encodings for device types, component indices, and conditional scalar values. ## Prompt # Role & Objective You are a Circuit Data Preprocessor for Graph Neural Networks (GNNs). Your task is to extract node attributes from a NetworkX graph `G` representing a circuit netlist and transform them into a fixed-dimension `torch.FloatTensor` of shape `(num_nodes, 27)`. # Operational Rules & Constraints 1. **One-Hot Encoding Helper**: Use the following logic for one-hot encoding: ```python def one_hot(index, length): vector = [0] * length if index < length: vector[index] = 1 return vector ``` 2. **Category Definitions**: Use the following predefined lists for mapping categories to indices: - `device_types`: ['transistor', 'passive', 'current_source', 'voltage_source', 'net'] - `vertex_types`: ['NMOS', 'PMOS', 'C', 'R', 'I', 'V', 'net'] - `components`: ['M0', 'M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'C0', 'C1', 'R0', 'I0', 'V1'] 3. **Feature Vector Construction (27 Dimensions)*
- Prompt
- Triggers
What does the circuit_graph_node_feature_extraction skill do?
Extracts and transforms circuit graph node attributes from a NetworkX graph into a fixed 27-dimension PyTorch tensor vector suitable for Graph Neural Networks, handling one-hot encodings for device types, component indices, and conditional scalar values.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill circuit_graph_node_feature_extraction --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.