ai-slop-detector 3.7.1

Evidence-based static analyzer for detecting AI-generated code quality issues with context-aware validation

AI-SLOP Detector

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

Not a style linter. A structural-risk scanner for AI-assisted code.

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.

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Navigation: What Is It? • Quick Start • How It Works • What It Detects • Scoring • Key Features • Calibration • Security • CI/CD • Config • VS Code • Changelog • Release Notes

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What Is AI-SLOP Detector?

AI-SLOP Detector is an evidence-based static analyzer purpose-built to catch the specific class of defects that AI code generation reliably introduces — before they reach production.

Unlike general linters that flag style and convention, it targets AI slop: structurally plausible code that is functionally empty, disconnected, or misleading.

• 27 adversarial pattern checks — stubs, phantom imports, disconnected pipelines, buzzword inflation, clone clusters
• 4D scoring model — LDR (logic density), ICR (inflation), DDC (dependency coupling), Purity (critical severity) combined via geometric mean
• Self-calibrating — every scan is recorded per-project; at every 10 multi-run files milestone the calibration check fires automatically; weights update only when 5 improvement events and 5 fp_candidate events have accumulated per class (project-scoped, domain-anchored grid search, no manual command required)
• Git-aware noise filter — uses commit SHA to distinguish real improvements from measurement noise
• Domain-aware bootstrap — --init auto-detects project domain (8 profiles: general, scientific/ml, scientific/numerical, web/api, library/sdk, cli/tool, bio, finance) and pre-seeds weights accordingly; override with --domain
• JS/TS analysis — optional [js] extra activates JSAnalyzer v2.8.0 with tree-sitter AST + regex fallback for .js/.jsx/.ts/.tsx files
• Go analysis — optional [go] extra activates GoAnalyzer v1.0.0 with regex-based detection for .go files; detects empty funcs, panic-as-error, fmt.Print debug, ignored errors
• CI/CD gates — soft / hard / quarantine modes; GitHub Actions ready
• VS Code extension — real-time inline diagnostics, debounced lint-on-type, ML score in status bar

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Quick Start

pip install ai-slop-detector

slop-detector --init                       # bootstrap .slopconfig.yaml + .gitignore
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[go]"       # Go tree-sitter analysis
pip install "ai-slop-detector[ml]"       # ML secondary signal

# No install required
uvx ai-slop-detector mycode.py

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How It Works

flowchart LR
    A[📄 Source File] --> R[FileRole\nClassifier]
    R --> B[AST Parser]
    B --> C[27 Pattern Checks]
    B --> D[LDR · ICR · DDC\n+ Purity Metrics]
    C --> E[GQG Scorer\nWeighted Geometric Mean]
    D --> E
    E --> F{deficit_score}
    F -->|< 30| G[✅ CLEAN]
    F -->|30–50| H[⚠️ SUSPICIOUS]
    F -->|50–70| I[🔶 INFLATED_SIGNAL]
    F -->|≥ 70| J[🚨 CRITICAL_DEFICIT]
    E --> H2[history.db]
    H2 --> K[Self-Calibrator\nauto-tune weights]

Every file goes through four independent measurement axes (LDR, ICR, DDC, Purity) and 27 pattern checks. Results are combined via a weighted geometric mean — a near-zero in any single dimension pulls the overall score down regardless of other dimensions. Every scan is recorded to history (per project); at every 10 multi-run files milestone the calibrator fires — weights apply only when >= 5 improvement events and >= 5 fp_candidate events per class have accumulated.

Full specification: docs/HOW_IT_WORKS.md · docs/MATH_MODELS.md

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What It Detects

27 patterns across 5 categories. Full catalog: docs/PATTERNS.md

Category	Patterns	Signal
Placeholder	empty_except, not_implemented, pass_placeholder, ellipsis_placeholder, return_none_placeholder, return_constant_stub, todo_comment, fixme_comment, hack_comment, xxx_comment, interface_only_class	Unfinished / scaffolded code
Structural	bare_except, mutable_default_arg, star_import, global_statement	Anti-patterns
Cross-Language	js_push, java_equals, ruby_each, go_print, csharp_length, php_strlen	Wrong-language syntax
Python Advanced	god_function, dead_code, deep_nesting, lint_escape, function_clone_cluster, placeholder_variable_naming	Structural complexity + evasion
Phantom	phantom_import	Hallucinated packages

Four metric axes per file:

Metric	What it measures
LDR (Logic Density Ratio)	logic_lines / total_lines — code vs. whitespace/comments
ICR (Inflation Check)	jargon_density × complexity_modifier — buzzword weight
DDC (Dependency Check)	used_imports / total_imports — import utilization
Purity	exp(-0.5 × n_critical_patterns) — AND-gate on critical pattern severity

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Scoring Model

purity        = exp(-0.5 × n_critical_patterns)
quality (GQG) = exp( Σ wᵢ·ln(dimᵢ) / Σ wᵢ )   — weighted geometric mean
deficit_score = 100 × (1 − quality) + pattern_penalty

Score	Status
≥ 70	CRITICAL_DEFICIT
≥ 50	INFLATED_SIGNAL
≥ 30	SUSPICIOUS
< 30	CLEAN

Default weights: ldr=0.40 · inflation=0.30 · ddc=0.20 · purity=0.10 — sum is 1.00; GQG divides by total_w so exact normalization is not required (all four calibrated via --self-calibrate in v3.2.0+) Project aggregation uses SR9 conservative weighting: 0.6 × min + 0.4 × mean

Full specification: docs/MATH_MODELS.md

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Key Features

Bootstrap — domain-aware, one command to start

slop-detector --init                   # auto-detect domain, generate .slopconfig.yaml
slop-detector --init --domain web/api       # explicit domain override

--init detects your project domain from file patterns (8 built-in profiles: general, scientific/ml, scientific/numerical, web/api, library/sdk, cli/tool, bio, finance) and pre-seeds the weight profile accordingly. Also secures .slopconfig.yaml in .gitignore by default.

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JS/TS Analysis — optional tree-sitter path

pip install "ai-slop-detector[js]"
slop-detector --project ./src         # now includes .js/.jsx/.ts/.tsx files

Activates JSAnalyzer v2.8.0 with tree-sitter AST (regex fallback when not installed). Results appear under js_file_results in ProjectAnalysis and JSON output.

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Go Analysis — regex-based, optional tree-sitter-go path

pip install "ai-slop-detector[go]"
slop-detector --project ./src         # now includes .go files

Activates GoAnalyzer v1.0.0. Detects: empty function stubs, panic() as error handling, fmt.Println/Printf debug prints, _ = ignored errors, TODO/FIXME comments, god functions (> 60 lines). Results appear under go_file_results in JSON output.

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Self-Calibration — the tool learns your codebase

slop-detector . --self-calibrate               # see what your history recommends
slop-detector . --self-calibrate --apply-calibration  # write to .slopconfig.yaml

4D grid-search (ldr / inflation / ddc / purity) over your run history. Optimizes all four weight dimensions simultaneously.

• Project-scoped — history.db tags every record with a project_id (sha256 of cwd); calibration signal never mixes across different projects
• Domain-anchored — grid search is constrained to ±0.15 around the current domain weights, preventing drift outside the domain's meaningful weight region
• Drift warnings — CalibrationResult.warnings flags any dimension that shifted > 0.25 from the anchor
• Only applies when confidence gap between top two candidates exceeds 0.10
• Milestone is triggered by files re-scanned (not raw record count), avoiding false triggers on first-time project scans

docs/SELF_CALIBRATION.md →

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History Tracking — longitudinal quality analysis

slop-detector mycode.py --show-history   # per-file trend
slop-detector --history-trends           # 7-day project aggregate
slop-detector --export-history data.jsonl

Every run auto-recorded to ~/.slop-detector/history.db. The history database is the training signal for ML self-calibration. docs/HISTORY_TRACKING.md →

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Claude Code Integration

cp -r claude-skills/slop-detector ~/.claude/skills/
# restart Claude Code, then use /slop, /slop-file, /slop-gate, /slop-spar

Adds a persistent scan → diagnose → patch → re-scan → gate → calibrate quality loop inside Claude Code. /slop scans and prioritizes; /slop-gate gives a CI-style PASS/FAIL; /slop-spar catches calibration drift.

Skill source → · Full docs →

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Empirical Weight Calibration (LEDA)

Most static analyzers ship with hand-tuned thresholds — or none at all. AI-SLOP Detector's 4D weights are empirically synthesized, not guessed. The oracle is human git behavior: a developer committing a flagged fix is an improvement signal; ignoring a flag is a false-positive candidate. Because LDR, DDC, and cyclomatic complexity are AST-derived structural facts, the calibration loop cannot hallucinate its way to a better score — AI measures, the human judges.

flowchart TD
    A[External Repositories\nDogfooding] --> B[LEDA Turbo Protocol\nScan → Auto-Fix → Rescan]
    B --> C{Measure Delta}
    C -->|Git Commit Accepted| D[Improvement Event]
    C -->|Flagged but Ignored| E[False Positive Candidate]
    D --> F[Self-Calibrator\n4D Grid Search]
    E --> F
    F --> G{Confidence Gap ≥ 0.10?}
    G -->|Yes| H[Global Injector\nSynthesizes Weights]
    H --> I[DOMAIN_PROFILES Updated]

1. Dogfooding — leda_turbo.bat runs a Scan → Auto-Fix → Rescan loop over diverse external codebases, safely applying patterns like bare_except and mutable_default_arg.
2. Event Labeling — deficit drop + git commit = improvement_event; flagged and ignored = fp_candidate.
3. Self-Calibration — 4D grid search (±0.15 domain-anchored). Weights update only when the confidence_gap between improvement events and FP candidates exceeds 0.10.
4. Global Synthesis — global_injector.py harvests signals across all dogfooding repos, synthesizes a vote-weighted optimal profile, and injects it into DOMAIN_PROFILES["general"].

LEDA Calibration Docs → · Turbo Protocol →

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Security Considerations

.slopconfig.yaml sensitivity

Your .slopconfig.yaml contains domain_overrides — a precise map of which functions are exempt from complexity rules. This is effectively a codebase weakness surface: it reveals which areas are too complex to refactor right now.

Best practice:

• Run slop-detector --init to generate .slopconfig.yaml and auto-add it to .gitignore
• To share governance config with your team, explicitly remove .slopconfig.yaml from .gitignore
• Open-source repos committing it is fine (transparency over obscurity — see this project's own .slopconfig.yaml)

history.db

History is stored at ~/.slop-detector/history.db (your home directory, outside all repos). It is never committed and accumulates across all projects you scan.

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Adversarial Validation (SPAR-Code) — ground-truth regression guard

fhval spar          # 3-layer adversarial check
fhval spar --layer a   # known-pattern anchors
fhval spar --layer c   # existence probes

Verifies each metric is measuring what it claims. Catches calibration drift before it reaches production.

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Structural Coherence — project-level signal

project = detector.analyze_project("./src")
print(project.structural_coherence)  # 0.0 – 1.0

Experimental. Use for longitudinal comparison within a project, not as an absolute gate. docs/ARCHITECTURE.md →

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CI/CD Integration

pre-commit (runs on every commit):

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/flamehaven01/AI-SLOP-Detector
    rev: v3.7.0
    hooks:
      - id: slop-detector          # hard gate — fails on CRITICAL_DEFICIT >= 70
      # - id: slop-detector-warn   # soft mode — reports only, never blocks
      # - id: slop-detector-patterns  # fast per-file pattern scan

GitHub Actions (runs on every PR):

# .github/workflows/quality-gate.yml
- name: AI-SLOP Gate
  run: |
    pip install ai-slop-detector
    slop-detector --project . --ci-mode hard --ci-report

Enforcement modes:

--ci-mode soft        # informational, never fails build
--ci-mode hard        # fails: deficit_score >= 70, critical_patterns >= 3, inflation >= 1.5, ddc < 0.5
--ci-mode quarantine  # escalates repeat offenders after 3 violations

Full CI/CD Integration Guide →

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Configuration

# .slopconfig.yaml
weights:
  ldr: 0.40
  inflation: 0.30
  ddc: 0.20
  purity: 0.10

patterns:
  god_function:
    domain_overrides:
      - function_pattern: "check_node"   # AST walker — complex by design
        complexity_threshold: 30
        lines_threshold: 200

ignore:
  - "tests/**"
  - "**/__init__.py"

Full Configuration Guide → · Config Examples →

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VS Code Extension

Real-time inline diagnostics, debounced lint-on-type, ML score and Clone Detection in status bar.

What you see	Detail
Status bar score	Warning (42.3) — severity + deficit/100 with 4D tooltip
Inline diagnostics	Pattern issues, jargon, docstring inflation, phantom imports — pinned to line
Workspace QuickPick	All files sorted by deficit score, click to navigate
ML signal	ML: 73% [slop] in summary diagnostic when [ml] extra is installed

Commands (Ctrl+Shift+P > "SLOP"):

Command	Description
Analyze Current File	On-demand single-file scan
Analyze Workspace	Project-wide scan with QuickPick results
Auto-Fix Detected Issues	Apply (or dry-run preview) auto-fixable patterns
Show Gate Decision (SNP)	PASS/HALT with sr9/di2/jsd/ove metrics
Show File History	Per-file deficit score trend
Show History Trends	7-day project-wide daily trend table
Bootstrap .slopconfig.yaml	Domain-aware config generation (--init)
Run Self-Calibration	LEDA 4D weight optimizer with one-click Apply

Install from the VS Code Marketplace or build locally:

cd vscode-extension
npm install
npx vsce package          # produces vscode-slop-detector-3.7.0.vsix
code --install-extension vscode-slop-detector-3.7.0.vsix

Settings (slopDetector.*): pythonPath, lintOnSave, lintOnType, failThreshold (default 50), warnThreshold (default 30), recordHistory.

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Release Highlights

Version	Highlights
v3.7.0	Dogfooding calibration + SKILL.md OSOT repair (10 violations); cli_renderer.py split (730 lines → 4 renderer modules); python_advanced.py split (1150 lines → 5 modules); BUG-1 ddc weight 0.30→0.20; BUG-2 findings filter threshold fix; BUG-3 AST-accurate test counts; BUG-5 block-scoped YAML rewrite in self_calibrator; 314 tests GREEN
v3.6.0	Claude Code Skill (/slop, /slop-file, /slop-gate, /slop-spar); CI gate bugfix (--ci-mode hard now exits non-zero without --ci-report); pre-commit hooks rewritten (python -m entry, 3 hook variants); VS Code Extension v3.6.0 VSIX; docs: Purity row, weight normalization note, [go] extra; 311 tests GREEN
v3.5.0	Domain-aware --init (8 profiles, --domain flag); JS/TS analysis via JSAnalyzer v2.8.0 + [js]; Go analysis via GoAnalyzer v1.0.0 + [go]; self-calibration patches: project-scoped history (project_id), re-scan milestone trigger, domain-anchored grid search (±0.15), CalibrationResult.warnings (drift > 0.25); 308 tests GREEN
v3.4.1	FileRole.STUB (Protocol/ABC stubs skip ldr+patterns); auto-discover .slopconfig.yaml; Python 3.8 CI compat; mypy attr-defined fix
v3.4.0	Per-rule FP rate tracking (LEDA Phase 2A); purity weight ceiling MAX_PURITY_WEIGHT=0.25 (Phase 2B)
v3.3.0	File role classifier (SOURCE/INIT/RE_EXPORT/TEST/MODEL/CORPUS); DDC annotation-only import fix; # noqa: F401 + __all__ re-export recognition
v3.2.1	Auto-calibration at every 10-scan milestone (no manual cmd); P2 git noise filter; P3 per-class thresholds (5+5); calibrate() min_events bugfix; 11/11 e2e GREEN
v3.2.0	4D calibration (purity dimension); --init bootstrap; auto-calibration hints; 44/44 self-scan CLEAN
v3.1.2	data_collector refactor; slopconfig gap fill; 43/43 self-scan CLEAN
v3.1.1	Clone Detection in Core Metrics table; table style unification; VS Code UX
v3.1.0	3 new adversarial patterns (function_clone_cluster, placeholder_variable_naming, return_constant_stub); GQG calibrator alignment; fhval SPAR-Code
v3.0.2	Phantom import 3-tier classification; __init__.py LDR fix; god_function LOW demotion
v3.0.0	Geometric mean scorer (GQG); purity dimension; DCF per-file; structural coherence
v2.9.3	Self-calibration engine; weight grid-search from usage history
v2.9.0	phantom_import CRITICAL detection; history auto-tracking

Full Release Notes → · Changelog →

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Development

git clone https://github.com/flamehaven01/AI-SLOP-Detector.git
cd AI-SLOP-Detector
pip install -e ".[dev]"
pytest tests/ -v --cov
black src/ tests/
ruff check src/ tests/

Development Guide →

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Download Stats

 

Chart updated weekly via GitHub Actions. Monthly installs: pypistats.org (mirrors excluded). Total: pepy.tech (incl. mirrors)

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License

MIT — see LICENSE.

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