invariant-ace

Define and enforce inductive invariants with counterexample traces and verification signals.

66|2|Updated Feb 18, 2015
One-click install
npx skills add https://github.com/tkersey/dotfiles --skill invariant-ace
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: invariant-ace
Source: https://github.com/tkersey/dotfiles/tree/main/codex/skills/invariant-ace
Command: npx skills add https://github.com/tkersey/dotfiles --skill invariant-ace

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps teams transform vague safety expectations into concrete, enforceable invariants that prevent invalid states across data, protocols, and algorithms. It focuses on owning specific state and boundary mutations, then proving correctness through counterexample traces and explicit verification signals.

Core Features & Use Cases

  • Invariant discovery and ownership: identify the state owner and the transitions that must preserve P(state).
  • Inductive safety enforcement: apply strong, cheap boundaries (parse/construct/API/DB/lock/txn) to keep the system safe.
  • Counterexample-driven refinement: refine invariants via concrete traces and add observability to catch violations early.
  • Use cases: prevent null/shape surprises, race conditions, stale data, and improper ordering in concurrent systems.

Quick Start

Use invariant-ace to structure an invariant plan for a sample module: define scope, owner, a minimal counterexample, and a single, enforceable boundary.

Frequently Asked Questions about invariant-ace

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

FAQPage Schema
How do I define inductive invariants to prevent invalid states in concurrent systems?

To define inductive invariants, you identify the state owner and the transitions that must preserve the invariant conditions. This Skill helps structure that process by establishing scope, enforcement boundaries, and an observable verification plan to prevent race conditions and stale data.

What is counterexample-driven refinement for protocol and algorithm state changes?

Counterexample-driven refinement is a verification method that uses concrete traces to prove correctness and catch violations early. By refining invariants via these traces, teams can identify exactly how and where data validity or concurrency boundaries fail in their protocols.

How do I enforce data validity boundaries across modules, services, and transactions?

You enforce data validity by applying strong, cheap boundaries at parse, construct, API, DB, lock, and transaction levels. This approach ensures that state mutations across modules and services preserve safety conditions and prevent invalid data shapes.

Can I use model checking and formal methods for data validity without heavy dependencies?

Yes, you can apply formal methods for data validity without external dependencies. This Skill focuses on specifying an owner, defining scope, and creating an observable verification plan with predicates and transitions to guarantee state correctness independently.

What's the best way to structure an invariant plan for a sample module?

The best way to structure an invariant plan is to define the scope, identify the owner, construct a minimal counterexample, and establish a single enforceable boundary. This creates a rock-solid state guarantee by proving correctness through explicit verification signals.