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Extract structured edge features from a bipartite circuit netlist (NetworkX MultiGraph), normalizing device/net ordering, mapping terminal colors, and detecting
This skill provides a concrete implementation pattern for extracting per-edge feature dictionaries from a NetworkX MultiGraph that models circuit netlists. It normalizes edge direction so devices are first, extracts terminal names from edge labels, maps terminals to display colors, and detects parallel (device, net) connections. The output is a list of dictionaries suitable for downstream analysis, visualization, or ML feature pipelines.
Use this skill when you have a circuit netlist represented as a NetworkX graph and you need: edge-level features for visualization, dataset preparation for graph ML, or identifying parallel connections and terminal roles in schematic analysis. Trigger when processing netlist graphs or preparing features for model input.
Best suited to agents that can run Python and reason about graphs (Copilot-style or Python-capable assistants). Practical for Code-focused agents that can execute NetworkX code and integrate results into analysis pipelines.
Prompt-only skill that instructs an agent to write a NetworkX function for extracting edge features from bipartite circuit netlist graphs. Well-structured with clear anti-patterns and constraints, but extremely niche — only useful for VLSI/circuit design engineers working with specific graph representations. No scripts included; purely a code-generation prompt with no security concerns.
Clean skill, no security issues. Part of the ECNU AutoSkill bank (LLM-generated skills). Well-written prompt with good anti-patterns section, but very narrow audience limits usefulness. Frontmatter is complete and properly structured.
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