name: ns-stdlib description: NeuroScript standard library catalog. Lists all primitive and composite neurons with signatures, shapes, parameters, and categories. Use when looking up available neurons, checking signatures, or finding which neuron to use. allowed-tools: Read, Grep, Glob, Bash

NeuroScript Standard Library Catalog

Available Primitives (live)

!grep -A1 'self\.register(' src/stdlib_registry.rs | grep '"' | sed 's/.*"\([^"]*\)".*/\1/' | sort

Composite Library Neurons (live)

!for f in stdlib/FFN.ns stdlib/Residual.ns stdlib/MultiHeadAttention.ns stdlib/TransformerBlock.ns stdlib/TransformerStack.ns stdlib/MetaNeurons.ns; do [ -f "$f" ] && echo "=== $f ===" && ./target/release/neuroscript list "$f" 2>/dev/null; done

Category Index

| Category | Primitives | Use For | |----------|-----------|---------| | Core | Linear, Bias, Scale, MatMul, Einsum | Dense layers, linear transforms | | Activations | GELU, ReLU, Tanh, Sigmoid, SiLU, Softmax, Mish, PReLU, ELU | Non-linearities | | Normalization | LayerNorm, RMSNorm, GroupNorm, BatchNorm, InstanceNorm | Stabilizing training | | Regularization | Dropout, DropPath, DropConnect | Preventing overfitting | | Convolution | Conv1d, Conv2d, Conv3d, DepthwiseConv, SeparableConv, TransposedConv | Spatial feature extraction | | Pooling | MaxPool, AvgPool, AdaptiveAvgPool, GlobalAvgPool, AdaptiveMaxPool, GlobalMaxPool | Spatial reduction | | Embeddings | Embedding, PositionalEncoding, LearnedPositionalEmbedding, RotaryEmbedding | Token/position encoding | | Structural | Identity, Fork, Fork3, ForkN, Add, Multiply, Concat, Reshape, Transpose, Flatten, Split, Slice, Pad | Routing and reshaping (implicit fork preferred for splitting) | | Attention | ScaledDotProductAttention, MultiHeadSelfAttention | Attention mechanisms | | Debug | Log | Debugging tensor flow |

Decision Tree: Which Neuron?

Need to transform features? → Linear(in_dim, out_dim) Need non-linearity? → GELU() (default), ReLU() (legacy), SiLU() (modern) Need normalization? → LayerNorm(dim) (transformer), RMSNorm(dim) (efficient), BatchNorm(dim) (CNN) Need residual connection? → in -> (main, skip) + processing + Add() (implicit fork) Need N-way split? → in -> (a, b, c, ...) (implicit fork — any number of outputs) Need to concatenate? → Concat(dim=-1) — takes 2 inputs via named ports Need attention? → MultiHeadSelfAttention(d_model, heads) (complete) or compose from ScaledDotProductAttention(d_k) Need convolution? → Conv2d(in_ch, out_ch, kernel) (standard), SeparableConv(...) (efficient) Need position info? → PositionalEncoding(seq, dim) (sinusoidal), RotaryEmbedding(dim, seq) (modern)

Standard Library Composites

The stdlib/ directory provides higher-level neurons built from primitives:

• FFN.ns — Feed-forward networks: FFN(dim, expansion), FFNWithHidden(in, hidden, out)
• TransformerBlock.ns — SimpleTransformerBlock(dim), TransformerBlock(dim, heads, d_ff)
• TransformerStack.ns — TransformerStack2(d, heads, d_ff), SequentialTransformer(d, heads, d_ff)
• MetaNeurons.ns — ParallelFFN(dim) and routing patterns

See references/primitives-by-category.md for full signatures. See references/composite-library.md for stdlib neuron details. See references/impl-format.md for how impl references map to Python.