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invariant-generator

Infer loop invariants for C code verification using abstract interpretation.

17|2|Updated Feb 16, 2026
Search Tags:#verification#induction#formal methods#invariants#loop analysis#program-analysis
One-click install
npx skills add https://github.com/rainoftime/pl-skills --skill invariant-generator
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: invariant-generator
Source: https://github.com/rainoftime/pl-skills/tree/main/invariant-generator
Command: npx skills add https://github.com/rainoftime/pl-skills --skill invariant-generator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires abstract-interpretation-engine, z3, and includes references (resource) and scripts (resource) components.

What problem does it solve?

This Skill automates the challenging task of inferring loop invariants, which are crucial for proving program correctness and enabling automated verification.

Core Features & Use Cases

  • Automated Invariant Inference: Analyzes loops to generate candidate invariants.
  • Verification Support: Provides essential invariants for techniques like Hoare logic verification.
  • Use Case: When verifying a complex algorithm with nested loops, use this Skill to automatically discover the necessary loop invariants, significantly reducing manual effort and potential errors.

Quick Start

Use the invariant-generator skill to infer loop invariants for the provided C code snippet.

Frequently Asked Questions about invariant-generator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automatically infer loop invariants for C code verification?▼

You can automatically infer loop invariants for C code by analyzing loops to generate candidate invariants using abstract interpretation and counterexample-guided refinement. This significantly reduces manual effort when proving program correctness.

What is the best way to generate inductive assertions for nested loops?▼

Generating inductive assertions for nested loops is best handled by automated invariant inference tools. These tools analyze loop structures to discover necessary invariants, minimizing potential manual errors in formal methods verification.

Do I need an abstract interpretation engine to generate sound loop invariants?▼

Yes, generating sound loop invariants typically requires an abstract interpretation engine and a solver like z3. These dependencies provide the necessary mathematical foundation for analyzing loops and validating program correctness.

Can I use automated invariant generation for Hoare logic verification?▼

Yes, you can use automated invariant generation to support Hoare logic verification. The automatically inferred invariants provide the essential inductive assertions required to prove program correctness through formal methods.

What techniques are used for counterexample-guided refinement in program analysis?▼

Counterexample-guided refinement in program analysis uses abstract interpretation to generate candidate invariants and then refines them based on counterexamples. This technique ensures sound and precise invariant generation for loop analysis.

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