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

v1.0.0
GitHub

Über diesen Skill

Ideal für erweiterte KI-Agents, die kompositionale neuronale Architekturen und NeuroScript Standard Library-Integration erfordern. 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 für erweiterte KI-Agents, die kompositionale neuronale Architekturen und NeuroScript Standard Library-Integration erfordern. 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.

Warum diese Fähigkeit verwenden

Ermöglicht es Agents, komplexe neuronale Netze mit NeuroScript-Primitiven wie FFN, Residual, MultiHeadAttention und TransformerBlock zu komponieren, indem es den Katalog der NeuroScript Standard Library für erweiterte neuronale Architekturen nutzt. Es ermöglicht die Erstellung von benutzerdefinierten kompositionalen Bibliotheksneuronen und bietet einen Kategoriensuchindex für eine effiziente Navigation. Es verwendet grep, sed und die Listenfunktion von NeuroScript für die dynamische Registrierung und Inspektion von neuronalen Komponenten.

Am besten geeignet für

Ideal für erweiterte KI-Agents, die kompositionale neuronale Architekturen und NeuroScript Standard Library-Integration erfordern.

↓ Handlungsfähige Anwendungsfälle for ns-stdlib

Erstellen von benutzerdefinierten neuronalen Netzen mit NeuroScript-Primitiven
Registrieren und Inspektion von kompositionalen Bibliotheksneuronen
Verwenden des Katalogs der NeuroScript Standard Library für das Design von neuronalen Architekturen

! Sicherheit & Einschränkungen

  • Benötigt die Installation und Konfiguration von NeuroScript
  • Abhängig von spezifischen NeuroScript-Primitiven und kompositionalen Bibliotheksneuronen
  • Begrenzt auf NeuroScript-kompatible neuronale Architekturen

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 imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

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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?

Ideal für erweiterte KI-Agents, die kompositionale neuronale Architekturen und NeuroScript Standard Library-Integration erfordern. 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: Erstellen von benutzerdefinierten neuronalen Netzen mit NeuroScript-Primitiven, Registrieren und Inspektion von kompositionalen Bibliotheksneuronen, Verwenden des Katalogs der NeuroScript Standard Library für das Design von neuronalen Architekturen.

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?

Benötigt die Installation und Konfiguration von NeuroScript. Abhängig von spezifischen NeuroScript-Primitiven und kompositionalen Bibliotheksneuronen. Begrenzt auf NeuroScript-kompatible neuronale Architekturen.

↓ 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.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

ns-stdlib

Install ns-stdlib, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
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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