proof-theory

Problem-solving strategies for proof theory in mathematical logic

422 stars

Best use case

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

Problem-solving strategies for proof theory in mathematical logic

Teams using proof-theory 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/proof-theory/SKILL.md --create-dirs "https://raw.githubusercontent.com/vibeeval/vibecosystem/main/skills/math/mathematical-logic/proof-theory/SKILL.md"

Manual Installation

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

How proof-theory Compares

Feature / Agentproof-theoryStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Problem-solving strategies for proof theory in mathematical logic

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

# Proof Theory

## When to Use

Use this skill when working on proof-theory problems in mathematical logic.

## Decision Tree


1. **Proof Strategy Selection**
   - Direct proof: assume premises, derive conclusion
   - Proof by contradiction: assume negation, derive false
   - Proof by cases: split on disjunction
   - Induction: base case + inductive step

2. **Structural Induction**
   - Define well-founded ordering on structures
   - Base: prove for minimal elements
   - Step: assume for smaller, prove for current
   - `z3_solve.py prove "induction_principle"`

3. **Cut Elimination**
   - Gentzen's Hauptsatz: cuts can be eliminated
   - Subformula property: only subformulas appear
   - Useful for proof normalization

4. **Completeness/Soundness Check**
   - Soundness: if provable then valid
   - Completeness: if valid then provable
   - `z3_solve.py prove "soundness_theorem"`

5. **Proof Verification**
   - Check each step follows from rules
   - Verify dependencies are satisfied
   - `math_scratchpad.py verify "proof_steps"`


## Tool Commands

### Z3_Induction_Base
```bash
uv run python -m runtime.harness scripts/cc_math/z3_solve.py prove "P(0)"
```

### Z3_Induction_Step
```bash
uv run python -m runtime.harness scripts/cc_math/z3_solve.py prove "ForAll([n], Implies(P(n), P(n+1)))"
```

### Z3_Soundness
```bash
uv run python -m runtime.harness scripts/cc_math/z3_solve.py prove "Implies(derivable(phi), valid(phi))"
```

### Math_Verify
```bash
uv run python -m runtime.harness scripts/cc_math/math_scratchpad.py verify "proof_structure"
```

## Cognitive Tools Reference

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

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