extract_circuit_netlist_edge_features
Extracts structured edge features from a bipartite circuit netlist graph, handling device/net ordering, terminal extraction, color mapping, and parallel edge detection.
npx skills add ECNU-ICALK/AutoSkill --skill extract_circuit_netlist_edge_features --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.
# extract_circuit_netlist_edge_features Extracts structured edge features from a bipartite circuit netlist graph, handling device/net ordering, terminal extraction, color mapping, and parallel edge detection. ## Prompt # Role & Objective You are a Python/NetworkX specialist. Your task is to write a function `get_edge_features(G)` that extracts specific features from a NetworkX MultiGraph representing a circuit netlist. The graph is bipartite with 'device components' (nodes with vertex_type in ['NMOS', 'PMOS', 'R', 'L', 'C', 'I', 'V']) and 'nets'. # Operational Rules & Constraints 1. **Input**: A NetworkX MultiGraph `G`. 2. **Output**: A list of dictionaries, where each dictionary represents the features of one edge. 3. **Edge Normalization**: Iterate through `G.edges(data=True)`. Identify the device node by checking if `vertex_type` is in the device list `['NMOS', 'PMOS', 'R', 'L', 'C', 'I', 'V']`. If `u` is the net and `v` is the device, swap them to ensure the pair is processed as `(device, net)`. 4. **Terminal Name Extraction**: Extract the terminal name from the edge data's `label` attribute. The terminal name is the first character of this string (e.g., 'D7' -> 'D'). 5. **Edge
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What does the extract_circuit_netlist_edge_features skill do?
Extracts structured edge features from a bipartite circuit netlist graph, handling device/net ordering, terminal extraction, color mapping, and parallel edge detection.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill extract_circuit_netlist_edge_features --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.