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

このスキルについて

複合ニューラルアーキテクチャとNeuroScript Standard Libraryの統合を必要とする高度なAIエージェントに最適。 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

複合ニューラルアーキテクチャとNeuroScript Standard Libraryの統合を必要とする高度なAIエージェントに最適。 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.

このスキルを使用する理由

エージェントがNeuroScriptのプリミティブ（FFN、残差、MultiHeadAttention、TransformerBlockなど）を使用して複雑なニューラルネットワークを構成する能力を提供し、NeuroScript Standard Libraryカタログを使用して高度なニューラルアーキテクチャを実現します。カスタムの複合ライブラリニューロンの作成を可能にし、効率的なナビゲーションのためにカテゴリインデックスを提供します。grep、sed、およびNeuroScriptのリスト機能を使用して、ニューラルコンポーネントの動的登録と検査を実行します。

おすすめ

複合ニューラルアーキテクチャとNeuroScript Standard Libraryの統合を必要とする高度なAIエージェントに最適。

↓ 実現可能なユースケース for ns-stdlib

NeuroScriptのプリミティブを使用してカスタムニューラルネットワークを構成する
複合ライブラリニューロンの登録と検査
NeuroScript Standard Libraryカタログを使用してニューラルアーキテクチャを設計する

! セキュリティと制限

  • NeuroScriptのインストールと設定が必要
  • 特定のNeuroScriptのプリミティブと複合ライブラリニューロンに依存
  • NeuroScriptと互換性のあるニューラルアーキテクチャのみ

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.

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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?

複合ニューラルアーキテクチャとNeuroScript Standard Libraryの統合を必要とする高度なAIエージェントに最適。 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: NeuroScriptのプリミティブを使用してカスタムニューラルネットワークを構成する, 複合ライブラリニューロンの登録と検査, NeuroScript Standard Libraryカタログを使用してニューラルアーキテクチャを設計する.

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?

NeuroScriptのインストールと設定が必要. 特定のNeuroScriptのプリミティブと複合ライブラリニューロンに依存. NeuroScriptと互換性のあるニューラルアーキテクチャのみ.

↓ 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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