Best use case
proof-assistant is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Assist in constructing algorithm correctness proofs
Teams using proof-assistant 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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/proof-assistant/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How proof-assistant Compares
| Feature / Agent | proof-assistant | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Assist in constructing algorithm correctness proofs
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 Assistant Skill
## Purpose
Assist in constructing formal correctness proofs for algorithms using standard proof techniques.
## Capabilities
- Proof structure templates (induction, contradiction, etc.)
- Step-by-step proof guidance
- Termination argument generation
- Proof review and validation
- Identify proof gaps
## Target Processes
- correctness-proof-testing
- algorithm-implementation
## Proof Techniques
### Mathematical Induction
- Base case identification
- Inductive hypothesis formulation
- Inductive step construction
### Proof by Contradiction
- Assumption negation
- Logical derivation
- Contradiction identification
### Loop Invariant Proofs
- Invariant specification
- Three-part proof (init, maintenance, termination)
### Structural Induction
- For recursive data structures
- Base case (leaf/empty)
- Inductive case (composite)
## Input Schema
```json
{
"type": "object",
"properties": {
"algorithm": { "type": "string" },
"code": { "type": "string" },
"proofType": {
"type": "string",
"enum": ["induction", "contradiction", "invariant", "structural"]
},
"claim": { "type": "string" },
"partialProof": { "type": "string" }
},
"required": ["algorithm", "claim"]
}
```
## Output Schema
```json
{
"type": "object",
"properties": {
"success": { "type": "boolean" },
"proof": { "type": "string" },
"structure": { "type": "array" },
"gaps": { "type": "array" },
"suggestions": { "type": "array" }
},
"required": ["success"]
}
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