Invariant Analyzer Skill
SkillDev toolsIdentify and verify loop invariants for correctness proofs
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Then ask your AI: use the Invariant Analyzer Skill skill
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/algorithms-optimization/skills/invariant-analyzer/SKILL.md and read by ahel’s review.
Purpose
Identify and verify loop invariants to help construct correctness proofs for algorithms.
Capabilities
- Automatic loop invariant inference
- Invariant verification against code
- Precondition/postcondition extraction
- Generate formal proof structure
- Identify missing invariants
Target Processes
- correctness-proof-testing
- algorithm-implementation
Invariant Analysis Framework
Loop Invariant Properties
- Initialization: True before first iteration
- Maintenance: If true before iteration, true after
- Termination: Provides useful property at end
Common Invariant Patterns
- Range invariants: "for all i in [0, k), property P(i) holds"
- Accumulator invariants: "sum equals sum of a[0..k-1]"
- Pointer invariants: "left < right and all elements < left are processed"
- State invariants: "data structure maintains property X"
Input Schema
{
"type": "object",
"properties": {
"code": { "type": "string" },
"language": { "type": "string" },
"loopIndex": { "type": "integer" },
"expectedInvariant": { "type": "string" }
},
"required": ["code"]
}
Output Schema
{
"type": "object",
"properties": {
"success": { "type": "boolean" },
"invariants": { "type": "array" },
"preconditions": { "type": "array" },
"postconditions": { "type": "array" },
"proofOutline": { "type": "string" }
},
"required": ["success"]
}
Signals
- GitHub stars
- 2k
- Forks
- 112
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
invariant-analyzer- Source
- github.com/a5c-ai/babysitter