Best use case
invariant-analyzer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Identify and verify loop invariants for correctness proofs
Teams using invariant-analyzer 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/invariant-analyzer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How invariant-analyzer Compares
| Feature / Agent | invariant-analyzer | 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?
Identify and verify loop invariants for 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
# Invariant Analyzer Skill
## 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
1. **Initialization**: True before first iteration
2. **Maintenance**: If true before iteration, true after
3. **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
```json
{
"type": "object",
"properties": {
"code": { "type": "string" },
"language": { "type": "string" },
"loopIndex": { "type": "integer" },
"expectedInvariant": { "type": "string" }
},
"required": ["code"]
}
```
## Output Schema
```json
{
"type": "object",
"properties": {
"success": { "type": "boolean" },
"invariants": { "type": "array" },
"preconditions": { "type": "array" },
"postconditions": { "type": "array" },
"proofOutline": { "type": "string" }
},
"required": ["success"]
}
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