Kwansub Yun

Posted on Jan 16 • Edited on Mar 8

🧩 AI Slop Detector v2.6.3 is live — now on VS Code

#opensource #devops #codereview #vscode

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

Catches the slop that AI produces — before it reaches production.

The problem isn't that AI writes code.
The problem is the specific class of defects AI reliably introduces:
unimplemented stubs, disconnected pipelines, phantom imports, and buzzword-heavy noise.

The code speaks for itself.

Navigation: Quick Start • What's New v2.9.3 • What It Detects • Scoring Model • Self-Calibration • History Tracking • CI/CD • Docs • Changelog

Quick Start

pip install ai-slop-detector

slop-detector mycode.py               # single file
slop-detector --project ./src         # entire project
slop-detector mycode.py --json        # machine-readable output
slop-detector --project . --ci-mode hard --ci-report  # CI gate

# Optional extras
pip install "ai-slop-detector[js]"     # JS/TS tree-sitter analysis
pip install "ai-slop-detector[ml]"     # ML secondary signal
pip install "ai-slop-detector[ml-data]"  # real training data pipeline

# uvx (no install required)
uvx ai-slop-detector mycode.py