feat: add Matrix 3D glitch detector (#491)

bin/glitch_patterns.py

@@ -0,0 +1,297 @@
+"""
+Glitch pattern definitions for 3D world anomaly detection.
+
+Defines known visual artifact categories commonly found in 3D web worlds,
+particularly The Matrix environments. Each pattern includes detection
+heuristics and severity ratings.
+"""
+
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Optional
+
+
+class GlitchSeverity(Enum):
+    CRITICAL = "critical"
+    HIGH = "high"
+    MEDIUM = "medium"
+    LOW = "low"
+    INFO = "info"
+
+
+class GlitchCategory(Enum):
+    FLOATING_ASSETS = "floating_assets"
+    Z_FIGHTING = "z_fighting"
+    MISSING_TEXTURES = "missing_textures"
+    CLIPPING = "clipping"
+    BROKEN_NORMALS = "broken_normals"
+    SHADOW_ARTIFACTS = "shadow_artifacts"
+    LIGHTMAP_ERRORS = "lightmap_errors"
+    LOD_POPPING = "lod_popping"
+    WATER_REFLECTION = "water_reflection"
+    SKYBOX_SEAM = "skybox_seam"
+
+
+@dataclass
+class GlitchPattern:
+    """Definition of a known glitch pattern with detection parameters."""
+    category: GlitchCategory
+    name: str
+    description: str
+    severity: GlitchSeverity
+    detection_prompts: list[str]
+    visual_indicators: list[str]
+    confidence_threshold: float = 0.6
+
+    def to_dict(self) -> dict:
+        return {
+            "category": self.category.value,
+            "name": self.name,
+            "description": self.description,
+            "severity": self.severity.value,
+            "detection_prompts": self.detection_prompts,
+            "visual_indicators": self.visual_indicators,
+            "confidence_threshold": self.confidence_threshold,
+        }
+
+
+# Known glitch patterns for Matrix 3D world scanning
+MATRIX_GLITCH_PATTERNS: list[GlitchPattern] = [
+    GlitchPattern(
+        category=GlitchCategory.FLOATING_ASSETS,
+        name="Floating Object",
+        description="Object not properly grounded or anchored to the scene geometry. "
+                    "Common in procedurally placed assets or after physics desync.",
+        severity=GlitchSeverity.HIGH,
+        detection_prompts=[
+            "Identify any objects that appear to float above the ground without support.",
+            "Look for furniture, props, or geometry suspended in mid-air with no visible attachment.",
+            "Check for objects whose shadows do not align with the surface below them.",
+        ],
+        visual_indicators=[
+            "gap between object base and surface",
+            "shadow detached from object",
+            "object hovering with no structural support",
+        ],
+        confidence_threshold=0.65,
+    ),
+    GlitchPattern(
+        category=GlitchCategory.Z_FIGHTING,
+        name="Z-Fighting Flicker",
+        description="Two coplanar surfaces competing for depth priority, causing "
+                    "visible flickering or shimmering textures.",
+        severity=GlitchSeverity.MEDIUM,
+        detection_prompts=[
+            "Look for surfaces that appear to shimmer, flicker, or show mixed textures.",
+            "Identify areas where two textures seem to overlap and compete for visibility.",
+            "Check walls, floors, or objects for surface noise or pattern interference.",
+        ],
+        visual_indicators=[
+            "shimmering surface",
+            "texture flicker between two patterns",
+            "noisy flat surfaces",
+            "moire-like patterns on planar geometry",
+        ],
+        confidence_threshold=0.55,
+    ),
+    GlitchPattern(
+        category=GlitchCategory.MISSING_TEXTURES,
+        name="Missing or Placeholder Texture",
+        description="A surface rendered with a fallback checkerboard, solid magenta, "
+                    "or the default engine placeholder texture.",
+        severity=GlitchSeverity.CRITICAL,
+        detection_prompts=[
+            "Look for bright magenta, checkerboard, or solid-color surfaces that look out of place.",
+            "Identify any surfaces that appear as flat untextured colors inconsistent with the scene.",
+            "Check for black, white, or magenta patches where detailed textures should be.",
+        ],
+        visual_indicators=[
+            "magenta/pink solid color surface",
+            "checkerboard pattern",
+            "flat single-color geometry",
+            "UV-debug texture visible",
+        ],
+        confidence_threshold=0.7,
+    ),
+    GlitchPattern(
+        category=GlitchCategory.CLIPPING,
+        name="Geometry Clipping",
+        description="Objects passing through each other or intersecting in physically "
+                    "impossible ways due to collision mesh errors.",
+        severity=GlitchSeverity.HIGH,
+        detection_prompts=[
+            "Look for objects that visibly pass through other objects (walls, floors, furniture).",
+            "Identify characters or props embedded inside geometry where they should not be.",
+            "Check for intersecting meshes where solid objects overlap unnaturally.",
+        ],
+        visual_indicators=[
+            "object passing through wall or floor",
+            "embedded geometry",
+            "overlapping solid meshes",
+            "character limb inside furniture",
+        ],
+        confidence_threshold=0.6,
+    ),
+    GlitchPattern(
+        category=GlitchCategory.BROKEN_NORMALS,
+        name="Broken Surface Normals",
+        description="Inverted or incorrect surface normals causing faces to appear "
+                    "inside-out, invisible from certain angles, or lit incorrectly.",
+        severity=GlitchSeverity.MEDIUM,
+        detection_prompts=[
+            "Look for surfaces that appear dark or black on one side while lit on the other.",
+            "Identify objects that seem to vanish when viewed from certain angles.",
+            "Check for inverted shading where lit areas should be in shadow.",
+        ],
+        visual_indicators=[
+            "dark/unlit face on otherwise lit model",
+            "invisible surface from one direction",
+            "inverted shadow gradient",
+            "inside-out appearance",
+        ],
+        confidence_threshold=0.5,
+    ),
+    GlitchPattern(
+        category=GlitchCategory.SHADOW_ARTIFACTS,
+        name="Shadow Artifact",
+        description="Broken, detached, or incorrectly rendered shadows that do not "
+                    "match the casting geometry or scene lighting.",
+        severity=GlitchSeverity.LOW,
+        detection_prompts=[
+            "Look for shadows that do not match the shape of nearby objects.",
+            "Identify shadow acne: banding or striped patterns on surfaces.",
+            "Check for floating shadows detached from any visible caster.",
+        ],
+        visual_indicators=[
+            "shadow shape mismatch",
+            "shadow acne bands",
+            "detached floating shadow",
+            "Peter Panning (shadow offset from base)",
+        ],
+        confidence_threshold=0.5,
+    ),
+    GlitchPattern(
+        category=GlitchCategory.LOD_POPPING,
+        name="LOD Transition Pop",
+        description="Visible pop-in when level-of-detail models switch abruptly, "
+                    "causing geometry or textures to change suddenly.",
+        severity=GlitchSeverity.LOW,
+        detection_prompts=[
+            "Look for areas where mesh detail changes abruptly at visible boundaries.",
+            "Identify objects that appear to morph or shift geometry suddenly.",
+            "Check for texture resolution changes that create visible seams.",
+        ],
+        visual_indicators=[
+            "visible mesh simplification boundary",
+            "texture resolution jump",
+            "geometry pop-in artifacts",
+        ],
+        confidence_threshold=0.45,
+    ),
+    GlitchPattern(
+        category=GlitchCategory.LIGHTMAP_ERRORS,
+        name="Lightmap Baking Error",
+        description="Incorrect or missing baked lighting causing dark spots, light "
+                    "leaks, or mismatched illumination on static geometry.",
+        severity=GlitchSeverity.MEDIUM,
+        detection_prompts=[
+            "Look for unusually dark patches on walls or ceilings that should be lit.",
+            "Identify bright light leaks through solid geometry seams.",
+            "Check for mismatched lighting between adjacent surfaces.",
+        ],
+        visual_indicators=[
+            "dark splotch on lit surface",
+            "bright line at geometry seam",
+            "lighting discontinuity between adjacent faces",
+        ],
+        confidence_threshold=0.5,
+    ),
+    GlitchPattern(
+        category=GlitchCategory.WATER_REFLECTION,
+        name="Water/Reflection Error",
+        description="Incorrect reflections, missing water surfaces, or broken "
+                    "reflection probe assignments.",
+        severity=GlitchSeverity.MEDIUM,
+        detection_prompts=[
+            "Look for reflections that do not match the surrounding environment.",
+            "Identify water surfaces that appear solid or incorrectly rendered.",
+            "Check for mirror surfaces showing wrong scene geometry.",
+        ],
+        visual_indicators=[
+            "reflection mismatch",
+            "solid water surface",
+            "incorrect environment map",
+        ],
+        confidence_threshold=0.5,
+    ),
+    GlitchPattern(
+        category=GlitchCategory.SKYBOX_SEAM,
+        name="Skybox Seam",
+        description="Visible seams or color mismatches at the edges of skybox cubemap faces.",
+        severity=GlitchSeverity.LOW,
+        detection_prompts=[
+            "Look at the edges of the sky for visible seams or color shifts.",
+            "Identify discontinuities where skybox faces meet.",
+            "Check for texture stretching at skybox corners.",
+        ],
+        visual_indicators=[
+            "visible line in sky",
+            "color discontinuity at sky edge",
+            "sky texture seam",
+        ],
+        confidence_threshold=0.45,
+    ),
+]
+
+
+def get_patterns_by_severity(min_severity: GlitchSeverity) -> list[GlitchPattern]:
+    """Return patterns at or above the given severity level."""
+    severity_order = [
+        GlitchSeverity.INFO,
+        GlitchSeverity.LOW,
+        GlitchSeverity.MEDIUM,
+        GlitchSeverity.HIGH,
+        GlitchSeverity.CRITICAL,
+    ]
+    min_idx = severity_order.index(min_severity)
+    return [p for p in MATRIX_GLITCH_PATTERNS if severity_order.index(p.severity) >= min_idx]
+
+
+def get_pattern_by_category(category: GlitchCategory) -> Optional[GlitchPattern]:
+    """Return the pattern definition for a specific category."""
+    for p in MATRIX_GLITCH_PATTERNS:
+        if p.category == category:
+            return p
+    return None
+
+
+def build_vision_prompt(patterns: list[GlitchPattern] | None = None) -> str:
+    """Build a composite vision analysis prompt from pattern definitions."""
+    if patterns is None:
+        patterns = MATRIX_GLITCH_PATTERNS
+
+    sections = []
+    for p in patterns:
+        prompt_text = " ".join(p.detection_prompts)
+        indicators = ", ".join(p.visual_indicators)
+        sections.append(
+            f"[{p.category.value.upper()}] {p.name} (severity: {p.severity.value})\n"
+            f"  {p.description}\n"
+            f"  Look for: {prompt_text}\n"
+            f"  Visual indicators: {indicators}"
+        )
+
+    return (
+        "Analyze this 3D world screenshot for visual glitches and artifacts. "
+        "For each detected issue, report the category, description of what you see, "
+        "approximate location in the image (x%, y%), and confidence (0.0-1.0).\n\n"
+        "Known glitch patterns to check:\n\n" + "\n\n".join(sections)
+    )
+
+
+if __name__ == "__main__":
+    import json
+    print(f"Loaded {len(MATRIX_GLITCH_PATTERNS)} glitch patterns:\n")
+    for p in MATRIX_GLITCH_PATTERNS:
+        print(f"  [{p.severity.value:8s}] {p.category.value}: {p.name}")
+    print(f"\nVision prompt preview:\n{build_vision_prompt()[:500]}...")

bin/matrix_glitch_detector.py

@@ -0,0 +1,549 @@
+#!/usr/bin/env python3
+"""
+Matrix 3D World Glitch Detector
+
+Scans a 3D web world for visual artifacts using browser automation
+and vision AI analysis. Produces structured glitch reports.
+
+Usage:
+    python matrix_glitch_detector.py <url> [--angles 4] [--output report.json]
+    python matrix_glitch_detector.py --demo  # Run with synthetic test data
+
+Ref: timmy-config#491
+"""
+
+import argparse
+import base64
+import json
+import os
+import sys
+import time
+import uuid
+from dataclasses import dataclass, field, asdict
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Optional
+
+# Add parent for glitch_patterns import
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+from glitch_patterns import (
+    GlitchCategory,
+    GlitchPattern,
+    GlitchSeverity,
+    MATRIX_GLITCH_PATTERNS,
+    build_vision_prompt,
+    get_patterns_by_severity,
+)
+
+
+@dataclass
+class DetectedGlitch:
+    """A single detected glitch with metadata."""
+    id: str
+    category: str
+    name: str
+    description: str
+    severity: str
+    confidence: float
+    location_x: Optional[float] = None  # percentage across image
+    location_y: Optional[float] = None  # percentage down image
+    screenshot_index: int = 0
+    screenshot_angle: str = "front"
+    timestamp: str = ""
+
+    def __post_init__(self):
+        if not self.timestamp:
+            self.timestamp = datetime.now(timezone.utc).isoformat()
+
+
+@dataclass
+class ScanResult:
+    """Complete scan result for a 3D world URL."""
+    scan_id: str
+    url: str
+    timestamp: str
+    total_screenshots: int
+    angles_captured: list[str]
+    glitches: list[dict] = field(default_factory=list)
+    summary: dict = field(default_factory=dict)
+    metadata: dict = field(default_factory=dict)
+
+    def to_json(self, indent: int = 2) -> str:
+        return json.dumps(asdict(self), indent=indent)
+
+
+def generate_scan_angles(num_angles: int) -> list[dict]:
+    """Generate camera angle configurations for multi-angle scanning.
+
+    Returns a list of dicts with yaw/pitch/label for browser camera control.
+    """
+    base_angles = [
+        {"yaw": 0, "pitch": 0, "label": "front"},
+        {"yaw": 90, "pitch": 0, "label": "right"},
+        {"yaw": 180, "pitch": 0, "label": "back"},
+        {"yaw": 270, "pitch": 0, "label": "left"},
+        {"yaw": 0, "pitch": -30, "label": "front_low"},
+        {"yaw": 45, "pitch": -15, "label": "front_right_low"},
+        {"yaw": 0, "pitch": 30, "label": "front_high"},
+        {"yaw": 45, "pitch": 0, "label": "front_right"},
+    ]
+
+    if num_angles <= len(base_angles):
+        return base_angles[:num_angles]
+    return base_angles + [
+        {"yaw": i * (360 // num_angles), "pitch": 0, "label": f"angle_{i}"}
+        for i in range(len(base_angles), num_angles)
+    ]
+
+
+def capture_screenshots(url: str, angles: list[dict], output_dir: Path) -> list[Path]:
+    """Capture screenshots of a 3D web world from multiple angles.
+
+    Uses browser_vision tool when available; falls back to placeholder generation
+    for testing and environments without browser access.
+    """
+    output_dir.mkdir(parents=True, exist_ok=True)
+    screenshots = []
+
+    for i, angle in enumerate(angles):
+        filename = output_dir / f"screenshot_{i:03d}_{angle['label']}.png"
+
+        # Attempt browser-based capture via browser_vision
+        try:
+            result = _browser_capture(url, angle, filename)
+            if result:
+                screenshots.append(filename)
+                continue
+        except Exception:
+            pass
+
+        # Generate placeholder screenshot for offline/test scenarios
+        _generate_placeholder_screenshot(filename, angle)
+        screenshots.append(filename)
+
+    return screenshots
+
+
+def _browser_capture(url: str, angle: dict, output_path: Path) -> bool:
+    """Capture a screenshot via browser automation.
+
+    This is a stub that delegates to the browser_vision tool when run
+    in an environment that provides it. In CI or offline mode, returns False.
+    """
+    # Check if browser_vision is available via environment
+    bv_script = os.environ.get("BROWSER_VISION_SCRIPT")
+    if bv_script and Path(bv_script).exists():
+        import subprocess
+        cmd = [
+            sys.executable, bv_script,
+            "--url", url,
+            "--screenshot", str(output_path),
+            "--rotate-yaw", str(angle["yaw"]),
+            "--rotate-pitch", str(angle["pitch"]),
+        ]
+        proc = subprocess.run(cmd, capture_output=True, text=True, timeout=30)
+        return proc.returncode == 0 and output_path.exists()
+    return False
+
+
+def _generate_placeholder_screenshot(path: Path, angle: dict):
+    """Generate a minimal 1x1 PNG as a placeholder for testing."""
+    # Minimal valid PNG (1x1 transparent pixel)
+    png_data = (
+        b"\x89PNG\r\n\x1a\n"
+        b"\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00\x00\x01"
+        b"\x08\x06\x00\x00\x00\x1f\x15\xc4\x89"
+        b"\x00\x00\x00\nIDATx\x9cc\x00\x01\x00\x00\x05\x00\x01"
+        b"\r\n\xb4\x00\x00\x00\x00IEND\xaeB`\x82"
+    )
+    path.write_bytes(png_data)
+
+
+def analyze_with_vision(
+    screenshot_paths: list[Path],
+    angles: list[dict],
+    patterns: list[GlitchPattern] | None = None,
+) -> list[DetectedGlitch]:
+    """Send screenshots to vision AI for glitch analysis.
+
+    In environments with a vision model available, sends each screenshot
+    with the composite detection prompt. Otherwise returns simulated results.
+    """
+    if patterns is None:
+        patterns = MATRIX_GLITCH_PATTERNS
+
+    prompt = build_vision_prompt(patterns)
+    glitches = []
+
+    for i, (path, angle) in enumerate(zip(screenshot_paths, angles)):
+        # Attempt vision analysis
+        detected = _vision_analyze_image(path, prompt, i, angle["label"])
+        glitches.extend(detected)
+
+    return glitches
+
+
+def _vision_analyze_image(
+    image_path: Path,
+    prompt: str,
+    screenshot_index: int,
+    angle_label: str,
+) -> list[DetectedGlitch]:
+    """Analyze a single screenshot with vision AI.
+
+    Uses the vision_analyze tool when available; returns empty list otherwise.
+    """
+    # Check for vision API configuration
+    api_key = os.environ.get("VISION_API_KEY") or os.environ.get("OPENAI_API_KEY")
+    api_base = os.environ.get("VISION_API_BASE", "https://api.openai.com/v1")
+
+    if api_key:
+        try:
+            return _call_vision_api(
+                image_path, prompt, screenshot_index, angle_label, api_key, api_base
+            )
+        except Exception as e:
+            print(f"  [!] Vision API error for {image_path.name}: {e}", file=sys.stderr)
+
+    # No vision backend available
+    return []
+
+
+def _call_vision_api(
+    image_path: Path,
+    prompt: str,
+    screenshot_index: int,
+    angle_label: str,
+    api_key: str,
+    api_base: str,
+) -> list[DetectedGlitch]:
+    """Call a vision API (OpenAI-compatible) for image analysis."""
+    import urllib.request
+    import urllib.error
+
+    image_data = base64.b64encode(image_path.read_bytes()).decode()
+
+    payload = json.dumps({
+        "model": os.environ.get("VISION_MODEL", "gpt-4o"),
+        "messages": [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": prompt},
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": f"data:image/png;base64,{image_data}",
+                            "detail": "high",
+                        },
+                    },
+                ],
+            }
+        ],
+        "max_tokens": 4096,
+    }).encode()
+
+    req = urllib.request.Request(
+        f"{api_base}/chat/completions",
+        data=payload,
+        headers={
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {api_key}",
+        },
+    )
+
+    with urllib.request.urlopen(req, timeout=60) as resp:
+        result = json.loads(resp.read())
+
+    content = result["choices"][0]["message"]["content"]
+    return _parse_vision_response(content, screenshot_index, angle_label)
+
+
+def _add_glitch_from_dict(
+    item: dict,
+    glitches: list[DetectedGlitch],
+    screenshot_index: int,
+    angle_label: str,
+):
+    """Convert a dict from vision API response into a DetectedGlitch."""
+    cat = item.get("category", item.get("type", "unknown"))
+    conf = float(item.get("confidence", item.get("score", 0.5)))
+
+    glitch = DetectedGlitch(
+        id=str(uuid.uuid4())[:8],
+        category=cat,
+        name=item.get("name", item.get("label", cat)),
+        description=item.get("description", item.get("detail", "")),
+        severity=item.get("severity", _infer_severity(cat, conf)),
+        confidence=conf,
+        location_x=item.get("location_x", item.get("x")),
+        location_y=item.get("location_y", item.get("y")),
+        screenshot_index=screenshot_index,
+        screenshot_angle=angle_label,
+    )
+    glitches.append(glitch)
+
+
+def _parse_vision_response(
+    text: str, screenshot_index: int, angle_label: str
+) -> list[DetectedGlitch]:
+    """Parse vision AI response into structured glitch detections."""
+    glitches = []
+
+    # Try to extract JSON from the response
+    json_blocks = []
+    in_json = False
+    json_buf = []
+
+    for line in text.split("\n"):
+        stripped = line.strip()
+        if stripped.startswith("```"):
+            if in_json and json_buf:
+                try:
+                    json_blocks.append(json.loads("\n".join(json_buf)))
+                except json.JSONDecodeError:
+                    pass
+                json_buf = []
+            in_json = not in_json
+            continue
+        if in_json:
+            json_buf.append(line)
+
+    # Flush any remaining buffer
+    if in_json and json_buf:
+        try:
+            json_blocks.append(json.loads("\n".join(json_buf)))
+        except json.JSONDecodeError:
+            pass
+
+    # Also try parsing the entire response as JSON
+    try:
+        parsed = json.loads(text)
+        if isinstance(parsed, list):
+            json_blocks.extend(parsed)
+        elif isinstance(parsed, dict):
+            if "glitches" in parsed:
+                json_blocks.extend(parsed["glitches"])
+            elif "detections" in parsed:
+                json_blocks.extend(parsed["detections"])
+            else:
+                json_blocks.append(parsed)
+    except json.JSONDecodeError:
+        pass
+
+    for item in json_blocks:
+        # Flatten arrays of detections
+        if isinstance(item, list):
+            for sub in item:
+                if isinstance(sub, dict):
+                    _add_glitch_from_dict(sub, glitches, screenshot_index, angle_label)
+        elif isinstance(item, dict):
+            _add_glitch_from_dict(item, glitches, screenshot_index, angle_label)
+
+    return glitches
+
+
+def _infer_severity(category: str, confidence: float) -> str:
+    """Infer severity from category and confidence when not provided."""
+    critical_cats = {"missing_textures", "clipping"}
+    high_cats = {"floating_assets", "broken_normals"}
+
+    cat_lower = category.lower()
+    if any(c in cat_lower for c in critical_cats):
+        return "critical" if confidence > 0.7 else "high"
+    if any(c in cat_lower for c in high_cats):
+        return "high" if confidence > 0.7 else "medium"
+    return "medium" if confidence > 0.6 else "low"
+
+
+def build_report(
+    url: str,
+    angles: list[dict],
+    screenshots: list[Path],
+    glitches: list[DetectedGlitch],
+) -> ScanResult:
+    """Build the final structured scan report."""
+    severity_counts = {}
+    category_counts = {}
+
+    for g in glitches:
+        severity_counts[g.severity] = severity_counts.get(g.severity, 0) + 1
+        category_counts[g.category] = category_counts.get(g.category, 0) + 1
+
+    report = ScanResult(
+        scan_id=str(uuid.uuid4()),
+        url=url,
+        timestamp=datetime.now(timezone.utc).isoformat(),
+        total_screenshots=len(screenshots),
+        angles_captured=[a["label"] for a in angles],
+        glitches=[asdict(g) for g in glitches],
+        summary={
+            "total_glitches": len(glitches),
+            "by_severity": severity_counts,
+            "by_category": category_counts,
+            "highest_severity": max(severity_counts.keys(), default="none"),
+            "clean_screenshots": sum(
+                1
+                for i in range(len(screenshots))
+                if not any(g.screenshot_index == i for g in glitches)
+            ),
+        },
+        metadata={
+            "detector_version": "0.1.0",
+            "pattern_count": len(MATRIX_GLITCH_PATTERNS),
+            "reference": "timmy-config#491",
+        },
+    )
+
+    return report
+
+
+def run_demo(output_path: Optional[Path] = None) -> ScanResult:
+    """Run a demonstration scan with simulated detections."""
+    print("[*] Running Matrix glitch detection demo...")
+
+    url = "https://matrix.example.com/world/alpha"
+    angles = generate_scan_angles(4)
+    screenshots_dir = Path("/tmp/matrix_glitch_screenshots")
+
+    print(f"[*] Capturing {len(angles)} screenshots from: {url}")
+    screenshots = capture_screenshots(url, angles, screenshots_dir)
+    print(f"[*] Captured {len(screenshots)} screenshots")
+
+    # Simulate detections for demo
+    demo_glitches = [
+        DetectedGlitch(
+            id=str(uuid.uuid4())[:8],
+            category="floating_assets",
+            name="Floating Chair",
+            description="Office chair floating 0.3m above floor in sector 7",
+            severity="high",
+            confidence=0.87,
+            location_x=35.2,
+            location_y=62.1,
+            screenshot_index=0,
+            screenshot_angle="front",
+        ),
+        DetectedGlitch(
+            id=str(uuid.uuid4())[:8],
+            category="z_fighting",
+            name="Wall Texture Flicker",
+            description="Z-fighting between wall panel and decorative overlay",
+            severity="medium",
+            confidence=0.72,
+            location_x=58.0,
+            location_y=40.5,
+            screenshot_index=1,
+            screenshot_angle="right",
+        ),
+        DetectedGlitch(
+            id=str(uuid.uuid4())[:8],
+            category="missing_textures",
+            name="Placeholder Texture",
+            description="Bright magenta surface on door frame — missing asset reference",
+            severity="critical",
+            confidence=0.95,
+            location_x=72.3,
+            location_y=28.8,
+            screenshot_index=2,
+            screenshot_angle="back",
+        ),
+        DetectedGlitch(
+            id=str(uuid.uuid4())[:8],
+            category="clipping",
+            name="Desk Through Wall",
+            description="Desk corner clipping through adjacent wall geometry",
+            severity="high",
+            confidence=0.81,
+            location_x=15.0,
+            location_y=55.0,
+            screenshot_index=3,
+            screenshot_angle="left",
+        ),
+    ]
+
+    print(f"[*] Detected {len(demo_glitches)} glitches")
+    report = build_report(url, angles, screenshots, demo_glitches)
+
+    if output_path:
+        output_path.write_text(report.to_json())
+        print(f"[*] Report saved to: {output_path}")
+
+    return report
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Matrix 3D World Glitch Detector — scan for visual artifacts",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  %(prog)s https://matrix.example.com/world/alpha
+  %(prog)s https://matrix.example.com/world/alpha --angles 8 --output report.json
+  %(prog)s --demo
+        """,
+    )
+    parser.add_argument("url", nargs="?", help="URL of the 3D world to scan")
+    parser.add_argument(
+        "--angles", type=int, default=4, help="Number of camera angles to capture (default: 4)"
+    )
+    parser.add_argument("--output", "-o", type=str, help="Output file path for JSON report")
+    parser.add_argument("--demo", action="store_true", help="Run demo with simulated data")
+    parser.add_argument(
+        "--min-severity",
+        choices=["info", "low", "medium", "high", "critical"],
+        default="info",
+        help="Minimum severity to include in report",
+    )
+    parser.add_argument("--verbose", "-v", action="store_true", help="Verbose output")
+
+    args = parser.parse_args()
+
+    if args.demo:
+        output = Path(args.output) if args.output else Path("glitch_report_demo.json")
+        report = run_demo(output)
+        print(f"\n=== Scan Summary ===")
+        print(f"URL: {report.url}")
+        print(f"Screenshots: {report.total_screenshots}")
+        print(f"Glitches found: {report.summary['total_glitches']}")
+        print(f"By severity: {report.summary['by_severity']}")
+        return
+
+    if not args.url:
+        parser.error("URL required (or use --demo)")
+
+    scan_id = str(uuid.uuid4())[:8]
+    print(f"[*] Matrix Glitch Detector — Scan {scan_id}")
+    print(f"[*] Target: {args.url}")
+
+    # Generate camera angles
+    angles = generate_scan_angles(args.angles)
+    print(f"[*] Capturing {len(angles)} screenshots...")
+
+    # Capture screenshots
+    screenshots_dir = Path(f"/tmp/matrix_glitch_{scan_id}")
+    screenshots = capture_screenshots(args.url, angles, screenshots_dir)
+    print(f"[*] Captured {len(screenshots)} screenshots")
+
+    # Filter patterns by severity
+    min_sev = GlitchSeverity(args.min_severity)
+    patterns = get_patterns_by_severity(min_sev)
+
+    # Analyze with vision AI
+    print(f"[*] Analyzing with vision AI ({len(patterns)} patterns)...")
+    glitches = analyze_with_vision(screenshots, angles, patterns)
+
+    # Build and save report
+    report = build_report(args.url, angles, screenshots, glitches)
+
+    if args.output:
+        Path(args.output).write_text(report.to_json())
+        print(f"[*] Report saved: {args.output}")
+    else:
+        print(report.to_json())
+
+    print(f"\n[*] Done — {len(glitches)} glitches detected")
+
+
+if __name__ == "__main__":
+    main()

docs/glitch-detection.md

@@ -0,0 +1,179 @@
+# 3D World Glitch Detection — Matrix Scanner
+
+**Reference:** timmy-config#491
+**Label:** gemma-4-multimodal
+**Version:** 0.1.0
+
+## Overview
+
+The Matrix Glitch Detector scans 3D web worlds for visual artifacts and
+rendering anomalies. It uses browser automation to capture screenshots from
+multiple camera angles, then sends them to a vision AI model for analysis
+against a library of known glitch patterns.
+
+## Detected Glitch Categories
+
+| Category | Severity | Description |
+|---|---|---|
+| Floating Assets | HIGH | Objects not grounded — hovering above surfaces |
+| Z-Fighting | MEDIUM | Coplanar surfaces flickering/competing for depth |
+| Missing Textures | CRITICAL | Placeholder colors (magenta, checkerboard) |
+| Clipping | HIGH | Geometry passing through other objects |
+| Broken Normals | MEDIUM | Inside-out or incorrectly lit surfaces |
+| Shadow Artifacts | LOW | Detached, mismatched, or acne shadows |
+| LOD Popping | LOW | Abrupt level-of-detail transitions |
+| Lightmap Errors | MEDIUM | Dark splotches, light leaks, baking failures |
+| Water/Reflection | MEDIUM | Incorrect environment reflections |
+| Skybox Seam | LOW | Visible seams at cubemap face edges |
+
+## Installation
+
+No external dependencies required — pure Python 3.10+.
+
+```bash
+# Clone the repo
+git clone https://forge.alexanderwhitestone.com/Timmy_Foundation/timmy-config.git
+cd timmy-config
+```
+
+## Usage
+
+### Basic Scan
+
+```bash
+python bin/matrix_glitch_detector.py https://matrix.example.com/world/alpha
+```
+
+### Multi-Angle Scan
+
+```bash
+python bin/matrix_glitch_detector.py https://matrix.example.com/world/alpha \
+    --angles 8 \
+    --output glitch_report.json
+```
+
+### Demo Mode
+
+```bash
+python bin/matrix_glitch_detector.py --demo
+```
+
+### Options
+
+| Flag | Default | Description |
+|---|---|---|
+| `url` | (required) | URL of the 3D world to scan |
+| `--angles N` | 4 | Number of camera angles to capture |
+| `--output PATH` | stdout | Output file for JSON report |
+| `--min-severity` | info | Minimum severity: info/low/medium/high/critical |
+| `--demo` | off | Run with simulated detections |
+| `--verbose` | off | Enable verbose output |
+
+## Report Format
+
+The JSON report includes:
+
+```json
+{
+  "scan_id": "uuid",
+  "url": "https://...",
+  "timestamp": "ISO-8601",
+  "total_screenshots": 4,
+  "angles_captured": ["front", "right", "back", "left"],
+  "glitches": [
+    {
+      "id": "short-uuid",
+      "category": "floating_assets",
+      "name": "Floating Chair",
+      "description": "Office chair floating 0.3m above floor",
+      "severity": "high",
+      "confidence": 0.87,
+      "location_x": 35.2,
+      "location_y": 62.1,
+      "screenshot_index": 0,
+      "screenshot_angle": "front",
+      "timestamp": "ISO-8601"
+    }
+  ],
+  "summary": {
+    "total_glitches": 4,
+    "by_severity": {"critical": 1, "high": 2, "medium": 1},
+    "by_category": {"floating_assets": 1, "missing_textures": 1, ...},
+    "highest_severity": "critical",
+    "clean_screenshots": 0
+  },
+  "metadata": {
+    "detector_version": "0.1.0",
+    "pattern_count": 10,
+    "reference": "timmy-config#491"
+  }
+}
+```
+
+## Vision AI Integration
+
+The detector supports any OpenAI-compatible vision API. Set these
+environment variables:
+
+```bash
+export VISION_API_KEY="your-api-key"
+export VISION_API_BASE="https://api.openai.com/v1"  # optional
+export VISION_MODEL="gpt-4o"  # optional, default: gpt-4o
+```
+
+For browser-based capture with `browser_vision`:
+
+```bash
+export BROWSER_VISION_SCRIPT="/path/to/browser_vision.py"
+```
+
+## Glitch Patterns
+
+Pattern definitions live in `bin/glitch_patterns.py`. Each pattern includes:
+
+- **category** — Enum matching the glitch type
+- **detection_prompts** — Instructions for the vision model
+- **visual_indicators** — What to look for in screenshots
+- **confidence_threshold** — Minimum confidence to report
+
+### Adding Custom Patterns
+
+```python
+from glitch_patterns import GlitchPattern, GlitchCategory, GlitchSeverity
+
+custom = GlitchPattern(
+    category=GlitchCategory.FLOATING_ASSETS,
+    name="Custom Glitch",
+    description="Your description",
+    severity=GlitchSeverity.MEDIUM,
+    detection_prompts=["Look for..."],
+    visual_indicators=["indicator 1", "indicator 2"],
+)
+```
+
+## Testing
+
+```bash
+python -m pytest tests/test_glitch_detector.py -v
+# or
+python tests/test_glitch_detector.py
+```
+
+## Architecture
+
+```
+bin/
+  matrix_glitch_detector.py  — Main CLI entry point
+  glitch_patterns.py         — Pattern definitions and prompt builder
+tests/
+  test_glitch_detector.py    — Unit and integration tests
+docs/
+  glitch-detection.md        — This documentation
+```
+
+## Limitations
+
+- Browser automation requires a headless browser environment
+- Vision AI analysis depends on model availability and API limits
+- Placeholder screenshots are generated when browser capture is unavailable
+- Detection accuracy varies by scene complexity and lighting conditions

tests/test_glitch_detector.py

@@ -0,0 +1,281 @@
+#!/usr/bin/env python3
+"""
+Tests for Matrix 3D Glitch Detector (timmy-config#491).
+
+Covers: glitch_patterns, matrix_glitch_detector core logic.
+"""
+
+import json
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+
+# Ensure bin/ is importable
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "bin"))
+
+from glitch_patterns import (
+    GlitchCategory,
+    GlitchPattern,
+    GlitchSeverity,
+    MATRIX_GLITCH_PATTERNS,
+    build_vision_prompt,
+    get_pattern_by_category,
+    get_patterns_by_severity,
+)
+
+from matrix_glitch_detector import (
+    DetectedGlitch,
+    ScanResult,
+    _infer_severity,
+    _parse_vision_response,
+    build_report,
+    generate_scan_angles,
+    run_demo,
+)
+
+
+class TestGlitchPatterns(unittest.TestCase):
+    """Tests for glitch_patterns module."""
+
+    def test_pattern_count(self):
+        """Verify we have a reasonable number of defined patterns."""
+        self.assertGreaterEqual(len(MATRIX_GLITCH_PATTERNS), 8)
+
+    def test_all_patterns_have_required_fields(self):
+        """Every pattern must have category, name, description, severity, prompts."""
+        for p in MATRIX_GLITCH_PATTERNS:
+            self.assertIsInstance(p.category, GlitchCategory)
+            self.assertTrue(p.name)
+            self.assertTrue(p.description)
+            self.assertIsInstance(p.severity, GlitchSeverity)
+            self.assertGreater(len(p.detection_prompts), 0)
+            self.assertGreater(len(p.visual_indicators), 0)
+            self.assertGreater(p.confidence_threshold, 0)
+            self.assertLessEqual(p.confidence_threshold, 1.0)
+
+    def test_pattern_to_dict(self):
+        """Pattern serialization should produce a dict with expected keys."""
+        p = MATRIX_GLITCH_PATTERNS[0]
+        d = p.to_dict()
+        self.assertIn("category", d)
+        self.assertIn("name", d)
+        self.assertIn("severity", d)
+        self.assertEqual(d["category"], p.category.value)
+
+    def test_get_patterns_by_severity(self):
+        """Severity filter should return only patterns at or above threshold."""
+        high_patterns = get_patterns_by_severity(GlitchSeverity.HIGH)
+        self.assertTrue(all(p.severity.value in ("high", "critical") for p in high_patterns))
+        self.assertGreater(len(high_patterns), 0)
+
+        all_patterns = get_patterns_by_severity(GlitchSeverity.INFO)
+        self.assertEqual(len(all_patterns), len(MATRIX_GLITCH_PATTERNS))
+
+    def test_get_pattern_by_category(self):
+        """Lookup by category should return the correct pattern."""
+        p = get_pattern_by_category(GlitchCategory.FLOATING_ASSETS)
+        self.assertIsNotNone(p)
+        self.assertEqual(p.category, GlitchCategory.FLOATING_ASSETS)
+
+        missing = get_pattern_by_category("nonexistent_category_value")
+        self.assertIsNone(missing)
+
+    def test_build_vision_prompt(self):
+        """Vision prompt should contain pattern names and be non-trivial."""
+        prompt = build_vision_prompt()
+        self.assertGreater(len(prompt), 200)
+        self.assertIn("Floating Object", prompt)
+        self.assertIn("Z-Fighting", prompt)
+        self.assertIn("Missing", prompt)
+
+    def test_build_vision_prompt_subset(self):
+        """Vision prompt with subset should only include specified patterns."""
+        subset = MATRIX_GLITCH_PATTERNS[:3]
+        prompt = build_vision_prompt(subset)
+        self.assertIn(subset[0].name, prompt)
+        self.assertNotIn(MATRIX_GLITCH_PATTERNS[-1].name, prompt)
+
+
+class TestGlitchDetector(unittest.TestCase):
+    """Tests for matrix_glitch_detector module."""
+
+    def test_generate_scan_angles_default(self):
+        """Default 4 angles should return front, right, back, left."""
+        angles = generate_scan_angles(4)
+        self.assertEqual(len(angles), 4)
+        labels = [a["label"] for a in angles]
+        self.assertIn("front", labels)
+        self.assertIn("right", labels)
+        self.assertIn("back", labels)
+        self.assertIn("left", labels)
+
+    def test_generate_scan_angles_many(self):
+        """Requesting more angles than base should still return correct count."""
+        angles = generate_scan_angles(12)
+        self.assertEqual(len(angles), 12)
+        # Should still have the standard ones
+        labels = [a["label"] for a in angles]
+        self.assertIn("front", labels)
+
+    def test_generate_scan_angles_few(self):
+        """Requesting fewer angles should return fewer."""
+        angles = generate_scan_angles(2)
+        self.assertEqual(len(angles), 2)
+
+    def test_detected_glitch_dataclass(self):
+        """DetectedGlitch should serialize cleanly."""
+        g = DetectedGlitch(
+            id="test001",
+            category="floating_assets",
+            name="Test Glitch",
+            description="A test glitch",
+            severity="high",
+            confidence=0.85,
+            location_x=50.0,
+            location_y=30.0,
+            screenshot_index=0,
+            screenshot_angle="front",
+        )
+        self.assertEqual(g.id, "test001")
+        self.assertTrue(g.timestamp)  # Auto-generated
+
+    def test_infer_severity_critical(self):
+        """Missing textures should infer critical/high severity."""
+        sev = _infer_severity("missing_textures", 0.9)
+        self.assertEqual(sev, "critical")
+        sev_low = _infer_severity("missing_textures", 0.5)
+        self.assertEqual(sev_low, "high")
+
+    def test_infer_severity_floating(self):
+        """Floating assets should infer high/medium severity."""
+        sev = _infer_severity("floating_assets", 0.8)
+        self.assertEqual(sev, "high")
+        sev_low = _infer_severity("floating_assets", 0.5)
+        self.assertEqual(sev_low, "medium")
+
+    def test_infer_severity_default(self):
+        """Unknown categories should default to medium/low."""
+        sev = _infer_severity("unknown_thing", 0.7)
+        self.assertEqual(sev, "medium")
+        sev_low = _infer_severity("unknown_thing", 0.3)
+        self.assertEqual(sev_low, "low")
+
+    def test_parse_vision_response_json_array(self):
+        """Should parse a JSON array response."""
+        response = json.dumps([
+            {
+                "category": "floating_assets",
+                "name": "Float Test",
+                "description": "Chair floating",
+                "confidence": 0.9,
+                "severity": "high",
+                "location_x": 40,
+                "location_y": 60,
+            }
+        ])
+        glitches = _parse_vision_response(response, 0, "front")
+        self.assertEqual(len(glitches), 1)
+        self.assertEqual(glitches[0].category, "floating_assets")
+        self.assertAlmostEqual(glitches[0].confidence, 0.9)
+
+    def test_parse_vision_response_wrapped(self):
+        """Should parse a response with 'glitches' wrapper key."""
+        response = json.dumps({
+            "glitches": [
+                {
+                    "category": "z_fighting",
+                    "name": "Shimmer",
+                    "confidence": 0.6,
+                }
+            ]
+        })
+        glitches = _parse_vision_response(response, 1, "right")
+        self.assertEqual(len(glitches), 1)
+        self.assertEqual(glitches[0].category, "z_fighting")
+
+    def test_parse_vision_response_empty(self):
+        """Should return empty list for non-JSON text."""
+        glitches = _parse_vision_response("No glitches found.", 0, "front")
+        self.assertEqual(len(glitches), 0)
+
+    def test_parse_vision_response_code_block(self):
+        """Should extract JSON from markdown code blocks."""
+        response = '```json\n[{"category": "clipping", "name": "Clip", "confidence": 0.7}]\n```'
+        glitches = _parse_vision_response(response, 0, "front")
+        self.assertEqual(len(glitches), 1)
+
+    def test_build_report(self):
+        """Report should have correct summary statistics."""
+        angles = generate_scan_angles(4)
+        screenshots = [Path(f"/tmp/ss_{i}.png") for i in range(4)]
+        glitches = [
+            DetectedGlitch(
+                id="a", category="floating_assets", name="Float",
+                description="", severity="high", confidence=0.8,
+                screenshot_index=0, screenshot_angle="front",
+            ),
+            DetectedGlitch(
+                id="b", category="missing_textures", name="Missing",
+                description="", severity="critical", confidence=0.95,
+                screenshot_index=1, screenshot_angle="right",
+            ),
+        ]
+        report = build_report("https://test.com", angles, screenshots, glitches)
+
+        self.assertEqual(report.total_screenshots, 4)
+        self.assertEqual(len(report.glitches), 2)
+        self.assertEqual(report.summary["total_glitches"], 2)
+        self.assertEqual(report.summary["by_severity"]["critical"], 1)
+        self.assertEqual(report.summary["by_severity"]["high"], 1)
+        self.assertEqual(report.summary["by_category"]["floating_assets"], 1)
+        self.assertEqual(report.metadata["reference"], "timmy-config#491")
+
+    def test_build_report_json_roundtrip(self):
+        """Report JSON should parse back correctly."""
+        angles = generate_scan_angles(2)
+        screenshots = [Path(f"/tmp/ss_{i}.png") for i in range(2)]
+        report = build_report("https://test.com", angles, screenshots, [])
+        json_str = report.to_json()
+        parsed = json.loads(json_str)
+        self.assertEqual(parsed["url"], "https://test.com")
+        self.assertEqual(parsed["total_screenshots"], 2)
+
+    def test_run_demo(self):
+        """Demo mode should produce a report with simulated glitches."""
+        with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as f:
+            output_path = Path(f.name)
+
+        try:
+            report = run_demo(output_path)
+            self.assertEqual(len(report.glitches), 4)
+            self.assertGreater(report.summary["total_glitches"], 0)
+            self.assertTrue(output_path.exists())
+
+            # Verify the saved JSON is valid
+            saved = json.loads(output_path.read_text())
+            self.assertIn("scan_id", saved)
+            self.assertIn("glitches", saved)
+        finally:
+            output_path.unlink(missing_ok=True)
+
+
+class TestIntegration(unittest.TestCase):
+    """Integration-level tests."""
+
+    def test_full_pipeline_demo(self):
+        """End-to-end demo pipeline should complete without errors."""
+        report = run_demo()
+        self.assertIsNotNone(report.scan_id)
+        self.assertTrue(report.timestamp)
+        self.assertGreater(report.total_screenshots, 0)
+
+    def test_patterns_cover_matrix_themes(self):
+        """Patterns should cover the main Matrix glitch themes."""
+        category_values = {p.category.value for p in MATRIX_GLITCH_PATTERNS}
+        expected = {"floating_assets", "z_fighting", "missing_textures", "clipping", "broken_normals"}
+        self.assertTrue(expected.issubset(category_values))
+
+
+if __name__ == "__main__":
+    unittest.main()