ns-stdlib — compiler ns-stdlib, neuroscript-rs, community, compiler, ide skills, deep-learning, domain-specific-language, neural-networks, neuroscript, pytorch, Claude Code

v1.0.0
GitHub

Sobre este Skill

Ideal for Advanced AI Agents requiring compositional neural architectures and NeuroScript Standard Library integration. 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.

# Core Topics

compiler deep-learning domain-specific-language neural-networks neuroscript pytorch
severeon severeon
[0]
[0]
Updated: 3/12/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 7/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution
Review Score
7/11
Quality Score
27
Canonical Locale
en
Detected Body Locale
en

Ideal for Advanced AI Agents requiring compositional neural architectures and NeuroScript Standard Library integration. 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.

Por que usar essa habilidade

Empowers agents to compose complex neural networks using NeuroScript primitives like FFN, Residual, MultiHeadAttention, and TransformerBlock, leveraging the NeuroScript Standard Library Catalog for advanced neural architectures. It enables the creation of custom composite library neurons and provides a category index for efficient navigation. Utilizes grep, sed, and NeuroScript's list functionality for dynamic registration and inspection of neural components.

Melhor para

Ideal for Advanced AI Agents requiring compositional neural architectures and NeuroScript Standard Library integration.

↓ Casos de Uso Práticos for ns-stdlib

Composing custom neural networks with NeuroScript primitives
Registering and inspecting composite library neurons
Utilizing the NeuroScript Standard Library Catalog for neural architecture design

! Segurança e Limitações

  • Requires NeuroScript installation and configuration
  • Dependent on specific NeuroScript primitives and composite library neurons
  • Limited to NeuroScript-compatible neural architectures

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is ns-stdlib?

Ideal for Advanced AI Agents requiring compositional neural architectures and NeuroScript Standard Library integration. 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.

How do I install ns-stdlib?

Run the command: npx killer-skills add severeon/neuroscript-rs/ns-stdlib. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for ns-stdlib?

Key use cases include: Composing custom neural networks with NeuroScript primitives, Registering and inspecting composite library neurons, Utilizing the NeuroScript Standard Library Catalog for neural architecture design.

Which IDEs are compatible with ns-stdlib?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for ns-stdlib?

Requires NeuroScript installation and configuration. Dependent on specific NeuroScript primitives and composite library neurons. Limited to NeuroScript-compatible neural architectures.

↓ How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add severeon/neuroscript-rs/ns-stdlib. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use ns-stdlib immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Imported Repository Instructions

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

Supporting Evidence

ns-stdlib

Install ns-stdlib, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly
Imported Repository Instructions
The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.
Supporting Evidence

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

CategoryPrimitivesUse For
CoreLinear, Bias, Scale, MatMul, EinsumDense layers, linear transforms
ActivationsGELU, ReLU, Tanh, Sigmoid, SiLU, Softmax, Mish, PReLU, ELUNon-linearities
NormalizationLayerNorm, RMSNorm, GroupNorm, BatchNorm, InstanceNormStabilizing training
RegularizationDropout, DropPath, DropConnectPreventing overfitting
ConvolutionConv1d, Conv2d, Conv3d, DepthwiseConv, SeparableConv, TransposedConvSpatial feature extraction
PoolingMaxPool, AvgPool, AdaptiveAvgPool, GlobalAvgPool, AdaptiveMaxPool, GlobalMaxPoolSpatial reduction
EmbeddingsEmbedding, PositionalEncoding, LearnedPositionalEmbedding, RotaryEmbeddingToken/position encoding
StructuralIdentity, Fork, Fork3, ForkN, Add, Multiply, Concat, Reshape, Transpose, Flatten, Split, Slice, PadRouting and reshaping (implicit fork preferred for splitting)
AttentionScaledDotProductAttention, MultiHeadSelfAttentionAttention mechanisms
DebugLogDebugging 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.

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