AutoSkill 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.

install

source · Clone the upstream repo

git clone https://github.com/ECNU-ICALK/AutoSkill

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/ECNU-ICALK/AutoSkill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/circuit_graph_node_feature_extraction" ~/.claude/skills/ecnu-icalk-autoskill-circuit-graph-node-feature-extraction && rm -rf "$T"

manifest: SkillBank/ConvSkill/english_gpt4_8_GLM4.7/circuit_graph_node_feature_extraction/SKILL.md

source content

circuit_graph_node_feature_extraction

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

representing a circuit netlist and transform them into a fixed-dimension

torch.FloatTensor

of shape

(num_nodes, 27)

Operational Rules & Constraints

One-Hot Encoding Helper: Use the following logic for one-hot encoding:

def one_hot(index, length):
    vector = [0] * length
    if index < length:
        vector[index] = 1
    return vector

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']
Feature Vector Construction (27 Dimensions): For each node in
```
G.nodes(data=True)
```
, construct a
```
feature_vector
```
by concatenating the following elements in order:
- Device Type (1 dim): Binary value.
```
1
```
  if
```
device_type
```
  is 'transistor', 'passive', 'current_source', or 'voltage_source'.
```
0
```
  if 'net'.
- Vertex Type (7 dim): One-hot encoding of
```
vertex_type
```
  using the
```
vertex_types
```
  list.
- Component Index (13 dim): One-hot encoding of the specific node name using the
```
components
```
  list. If
```
vertex_type
```
  is 'net', use all zeros.
- Values (6 dim): Scalar values in order:
```
w_value
```
  ,
```
l_value
```
  ,
```
C_value
```
  ,
```
R_value
```
  ,
```
I_value
```
  ,
```
V_value
```
  .
  - If
```
device_type
```
    == 'transistor': Set
```
w_value
```
    and
```
l_value
```
    from attributes. Others 0.
  - If
```
device_type
```
    == 'passive' and
```
vertex_type
```
    == 'C': Set
```
C_value
```
    from
```
value
```
    attribute. Others 0.
  - If
```
device_type
```
    == 'passive' and
```
vertex_type
```
    == 'R': Set
```
R_value
```
    from
```
value
```
    attribute. Others 0.
  - If
```
device_type
```
    == 'current_source': Set
```
I_value
```
    from
```
dc_value
```
    attribute. Others 0.
  - If
```
device_type
```
    == 'voltage_source': Set
```
V_value
```
    from
```
dc_value
```
    attribute. Others 0.
  - If
```
device_type
```
    == 'net': All values are 0.
Output Structure: Return a
```
torch.FloatTensor
```
of shape
```
(num_nodes, 27)
```
.

Anti-Patterns

Do not return a dictionary mapping node names to features; the output must be a tensor.
Do not infer missing values; default to 0.
Do not change the order of the feature vector dimensions.
Do not include string values in the final feature vectors; all data must be numerical.

Triggers

extract node features for GNN
convert circuit graph to tensor
format circuit graph features
circuit netlist feature extraction
transform circuit attributes to tensor