Consent-Aware Static Analysis for Intentional Complexity
Most code-quality tools assume complexity is accidental.
Production systems know that sometimes complexity is chosen.
Consent-aware static analysis that distinguishes intentional complexity from empty AI-generated code.
AI Slop Detector v2.6.3 is now live, featuring a VS Code extension and a design shift most static analyzers overlook:
consent.
This release isn’t about catching more mistakes.
It’s about separating slop from intent.
The Problem: When “Clean Code” Becomes a Lie
Modern static analysis tools are very good at enforcing uniformity.
They assume:
- complexity = risk
- deviation = mistake
- density = poor design
But real-world systems don’t behave that way.
In production codebases, complexity is often intentional:
- numerical kernels that trade readability for performance
- protocol-heavy edge handling
- bitwise or low-level optimizations
- domain-specific invariants that resist simplification
Most tools flag this complexity without context.
That’s how rules quietly turn into cages.
What v2.6.3 Adds: Explicit Consent
AI Slop Detector v2.6.3 introduces intentional complexity whitelisting.
You can now annotate code like this:
@slop.ignore(reason="Bitwise optimization for deterministic hashing")
def fast_inverse_sqrt(x: float) -> float:
...
This annotation means:
- complexity is explicitly acknowledged
- providing a reason is mandatory (eliminating silent ignores)
- exemptions are tracked and auditable
- reports surface “Whitelisted Complexity” separately from slop
The question shifts from:
“Is this complex?”
to:
“Is this complexity intentional — and documented?”
That distinction matters.
Selective, Not Absolute Ignores
Consent in v2.6.3 is granular, not a global escape hatch.
You can selectively ignore specific dimensions:
-
LDR— Logic Density Ratio -
INFLATION— token / boilerplate inflation -
DDC— Dependency Discipline -
PLACEHOLDER— stub or fake logic signals
All other checks remain active.
Governance stays intact.
Innovation stays possible.
VS Code Extension: Governance at the Point of Creation
v2.6.3 also ships the AI Slop Detector VS Code extension.
Inside the editor, you get:
- optional real-time scanning as you type
- inline warnings with severity signals
- a status bar “Sovereign Gate” indicator
- one-click pre-commit hooks to block slop before it enters the repo
No dashboards.
No detached reports.
Just feedback at the moment decisions are made.
[Screenshot: VS Code extension showing inline warnings and Sovereign Gate status bar]
How This Differs from Traditional Static Analysis
| Feature | Traditional Static Analysis | AI Slop Detector v2.6.3 |
|---|---|---|
| Complexity | Flagged as error | Intent validated |
| Context | Ignored | Mandatory (reason required) |
| Governance | Implicit rules | Explicit consent |
| Feedback timing | Post-commit | Real-time (VS Code) |
| Auditability | Limited | Whitelisted complexity tracked |
From Detection to Governance
Most tools stop at classification:
“This looks bad.”
AI Slop Detector goes further:
“This is empty.”
“This is dense.”
“This is complex — and intentionally so.”
That’s the difference between policing code and governing systems.
Or, as a guiding principle:
Rules should be the soil for the dream to grow — not the cage that kills it.
Design & Evolution Notes
This release is part of a longer trajectory:
- static analysis → semantic intent signals
- pattern detection → consent tracking
- rule enforcement → auditable decision paths
For deeper context, see the design and evolution documents linked below.
Repository & Documentation
flamehaven01
/
AI-SLOP-Detector
Stop shipping AI slop. Detects empty functions, fake documentation, and inflated comments in AI-generated code. Production-ready.
AI-SLOP Detector v2.6.2
Production-grade static analyzer for detecting AI-generated code quality issues with evidence-based validation.
Detects six critical categories of AI-generated code problems with actionable, context-aware questions.
Quick Navigation: 🚀 Quick Start • ✨ What's New • 🏗️ Architecture • 📊 Core Features • ⚙️ Configuration • 🔧 CLI Usage • 🚦 CI/CD Integration • 👨‍💻 Development
Quick Start
# Install from PyPI
pip install ai-slop-detector
# Analyze a single file
slop-detector mycode.py
# Scan entire project
slop-detector --project ./src
# CI/CD Integration (Soft mode - PR comments only)
slop-detector --project ./src --ci-mode soft --ci-report
# CI/CD Integration (Hard mode - fail build on issues)
slop-detector --project ./src --ci-mode hard --ci-report
# Generate JSON report
slop-detector mycode.py --json --output report.json
What's New in v2.6.2
Evidence-Based Validation
"Trust, but Verify" - Now Enforced:
- âś… Integration Test Requirement: Claims like
production-ready,scalable, orenterprise-gradenow FAIL if no integration…
- Core engine + CI examples
- VS Code Extension (Marketplace)
- Design docs & evolution notes
Who This Is For
- teams shipping AI-assisted code at scale
- reviewers tired of “looks fine” PRs
- engineers who believe governance should support creativity, not erase it
If you’ve ever thought:
“Yes, this is complex — and it needs to be.”
This release is for you.
Question for Readers
How do you currently distinguish intentional complexity from accidental mess in code reviews?
Static rules?
Reviewer intuition?
Tooling support?
Drop a comment below — I’m genuinely curious how other teams handle this.
Top comments (1)
Try it yourself
If you want to experiment with consent-driven code review:
📦 VS Code Extension
Install directly from the marketplace:
→ marketplace.visualstudio.com/items...
đź“‹ What's New (v2.6.3)
Recent updates include better annotation detection and governance tracking:
→ github.com/flamehaven01/AI-SLOP-De...
The tool is open for feedback—I'm actively iterating based on real-world usage.