natural-transformations

Problem-solving strategies for natural transformations in category theory

422 stars

Best use case

natural-transformations is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Problem-solving strategies for natural transformations in category theory

Teams using natural-transformations should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/natural-transformations/SKILL.md --create-dirs "https://raw.githubusercontent.com/vibeeval/vibecosystem/main/skills/math/category-theory/natural-transformations/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/natural-transformations/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How natural-transformations Compares

Feature / Agentnatural-transformationsStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Problem-solving strategies for natural transformations in category theory

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Natural Transformations

## When to Use

Use this skill when working on natural-transformations problems in category theory.

## Decision Tree


1. **Verify Naturality**
   - eta: F => G is natural transformation between functors F, G: C -> D
   - For each f: A -> B in C, diagram commutes:
     G(f) . eta_A = eta_B . F(f)
   - Write Lean 4: `theorem nat : η.app B ≫ G.map f = F.map f ≫ η.app A := η.naturality`

2. **Component Analysis**
   - eta_A: F(A) -> G(A) for each object A
   - Each component is morphism in target category D
   - Lean 4: `def η : F ⟶ G where app := fun X => ...`

3. **Natural Isomorphism**
   - Each component eta_A is isomorphism
   - Functors F and G are naturally isomorphic
   - Notation: F ≅ G (NatIso in Mathlib)

4. **Functor Category**
   - [C, D] has functors as objects
   - Natural transformations as morphisms
   - Vertical composition: Lean 4 `CategoryTheory.NatTrans.vcomp`
   - Horizontal composition: `CategoryTheory.NatTrans.hcomp`

5. **Yoneda Lemma Application**
   - Nat(Hom(A, -), F) ~ F(A) naturally in A
   - Lean 4: `CategoryTheory.yonedaEquiv`
   - Fully embeds C into [C^op, Set]
   - See: `.claude/skills/lean4-nat-trans/SKILL.md` for exact syntax


## Tool Commands

### Lean4_Naturality
```bash
# Lean 4: theorem nat : η.app B ≫ G.map f = F.map f ≫ η.app A := η.naturality
```

### Lean4_Nat_Trans
```bash
# Lean 4: def η : F ⟶ G where app := fun X => component_X
```

### Lean4_Yoneda
```bash
# Lean 4: CategoryTheory.yonedaEquiv -- Yoneda lemma
```

### Lean4_Build
```bash
lake build  # Compiler-in-the-loop verification
```

## Cognitive Tools Reference

See `.claude/skills/math-mode/SKILL.md` for full tool documentation.

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