SE-CoVe: Software Engineering Chain-of-Verification
A Claude Code plugin that catches bugs in AI-generated code by verifying against requirements, not implementation.
Why This Works
When you ask an AI to review its own code, it's biased toward its solution. Meta's Chain-of-Verification research showed that independent verification improves accuracy by 28% - because the verifier checks against requirements, not the (possibly flawed) implementation.
SE-CoVe applies this to code: the verifier never sees the draft solution. It answers verification questions by checking docs, searching your codebase, and reasoning about requirements - then a synthesizer compares findings and corrects errors.
Built for Software Engineering
Unlike general-purpose verification, SE-CoVe is designed specifically for code. Each agent understands 35+ software engineering claim categories across three domains:
Correctness
Behaviors, logic flow, boundary conditions, error handling, API usage patterns
Security
Input validation, authentication, authorization, injection prevention, data exposure
Performance
Time/space complexity, caching, memory management, async patterns, N+1 queries
The verification pipeline includes:
- Confidence scoring on all claims (High/Medium/Low)
- Severity ranking of issues (Critical/High/Medium/Low)
- Parallel verification tracks for thorough analysis
- Domain-specific checklists for security, performance, and error handling
- Evidence quality scoring with source authority ranking
How It Works
Question → Baseline (generate + extract claims)
↓
Planner (create verification tasks)
↓
Executor (verify independently - never sees draft)
↓
Synthesizer (compare + correct)
↓
Verified Solution
Installation
First add the marketplace
/plugin marketplace add vertti/se-cove-claude-plugin
then install the plugin
/plugin install chain-of-verification
Usage
/chain-of-verification:verify <your question>
Tip: Type /ver and press Tab to autocomplete the command.
Options
| Flag | Short | Description |
|---|
--quick | -q | Fast verification (haiku models, 1 executor) |
--thorough | -t | Comprehensive verification (opus models, 4+ parallel executors) |
--focus=X | -f X | Prioritize a specific area (see below) |
Focus Areas
| Focus | What It Checks |
|---|
security | Auth, input validation, injection, data exposure |
performance | Complexity, caching, memory, N+1 queries |
api | Contracts, versioning, error responses, compatibility |
testing | Coverage, edge cases, integration, mocks |
error-handling | Exceptions, recovery, logging, user feedback |
style | Naming, organization, documentation, types |
scalability | Bottlenecks, resource limits, horizontal scaling |
Examples
# Standard verification (default)
/chain-of-verification:verify How do I implement debounced search in React?
# Quick sanity check
/chain-of-verification:verify --quick Is this null check correct?
# Thorough security review (4 parallel executors)
/chain-of-verification:verify --thorough --focus=security Review the authentication flow
# API-focused verification
/chain-of-verification:verify -f api Does this endpoint handle errors correctly?
# Test coverage analysis
/chain-of-verification:verify --focus=testing Are these unit tests comprehensive?
# Error handling review
/chain-of-verification:verify -f error-handling How should this handle failures?
When to Use
Good for: Complex code generation, architectural decisions, bug investigations, library/API questions
Skip for: Trivial changes, simple questions, exploratory coding
References
Based on Meta's Chain-of-Verification research, which demonstrated 28% improvement in factual accuracy through independent verification.
License
MIT
