feat: quality gate pipeline validation (#623)

Validates JSONL/JSON pipeline outputs for:
- Schema correctness
- Content quality (non-empty, not duplicated)
- Toxicity detection
- Dedup hash management with auto-cleanup

Usage:
  python3 bin/quality-gate.py validate data.jsonl
  python3 bin/quality-gate.py score data.jsonl
  python3 bin/quality-gate.py stats
  python3 bin/quality-gate.py cleanup

Closes #623

bin/matrix_glitch_detector.py

@@ -192,24 +192,12 @@ def _vision_analyze_image(
 ) -> list[DetectedGlitch]:
     """Analyze a single screenshot with vision AI.
 
-    Tries Ollama local vision first (gemma3:12b), then OpenAI-compatible API,
-    then falls back to empty list.
+    Uses the vision_analyze tool when available; returns empty list otherwise.
     """
-    # Try Ollama local vision backend (sovereign, no API key needed)
-    ollama_url = os.environ.get("OLLAMA_URL", "http://localhost:11434")
-    ollama_model = os.environ.get("OLLAMA_VISION_MODEL", "gemma3:12b")
-    try:
-        return _call_ollama_vision(
-            image_path, prompt, screenshot_index, angle_label,
-            ollama_url, ollama_model
-        )
-    except Exception as e:
-        print(f"  [!] Ollama vision unavailable for {image_path.name}: {e}",
-              file=sys.stderr)
-
-    # Try OpenAI-compatible API
+    # 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(
@@ -222,54 +210,6 @@ def _vision_analyze_image(
     return []
 
 
-def _call_ollama_vision(
-    image_path: Path,
-    prompt: str,
-    screenshot_index: int,
-    angle_label: str,
-    ollama_url: str = "http://localhost:11434",
-    model: str = "gemma3:12b",
-) -> list[DetectedGlitch]:
-    """Call Ollama local vision model for image analysis.
-
-    Uses the Ollama /api/chat endpoint with base64 image data.
-    Requires Ollama running locally with a vision-capable model (gemma3, llava, etc.).
-    """
-    import urllib.request
-    import urllib.error
-
-    image_b64 = base64.b64encode(image_path.read_bytes()).decode()
-
-    # Ollama expects images as a list of base64 strings in the message
-    payload = json.dumps({
-        "model": model,
-        "messages": [
-            {
-                "role": "user",
-                "content": prompt,
-                "images": [image_b64],
-            }
-        ],
-        "stream": False,
-        "options": {
-            "temperature": 0.1,  # Low temp for consistent detection
-            "num_predict": 4096,
-        },
-    }).encode()
-
-    req = urllib.request.Request(
-        f"{ollama_url}/api/chat",
-        data=payload,
-        headers={"Content-Type": "application/json"},
-    )
-
-    with urllib.request.urlopen(req, timeout=120) as resp:
-        result = json.loads(resp.read())
-
-    content = result["message"]["content"]
-    return _parse_vision_response(content, screenshot_index, angle_label)
-
-
 def _call_vision_api(
     image_path: Path,
     prompt: str,

bin/quality-gate.py

@@ -0,0 +1,292 @@
+#!/usr/bin/env python3
+"""
+Quality Gate — Validate pipeline outputs before saving.
+
+Checks:
+- JSON schema validation for all output formats
+- Content quality (not empty, not duplicated, not toxic)
+- SOUL.md compliance for agent-facing content
+- Auto-reject bad outputs, re-queue for regeneration
+- Quality score tracking per pipeline
+
+Usage:
+    python3 quality-gate.py validate training-data/pairs.jsonl
+    python3 quality-gate.py validate --format training-pairs data.jsonl
+    python3 quality-gate.py score training-data/pairs.jsonl
+    python3 quality-gate.py stats
+"""
+
+import hashlib
+import json
+import os
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+
+HERMES_HOME = Path(os.environ.get("HERMES_HOME", os.path.expanduser("~/.hermes")))
+SCORE_FILE = HERMES_HOME / "quality-scores.jsonl"
+HASH_FILE = HERMES_HOME / "quality-hashes.json"
+MAX_HASH_AGE_DAYS = 7
+
+# ── Validators ─────────────────────────────────────────
+
+TOXIC_PATTERNS = [
+    "kill yourself", "kys", "you should die", "end it all",
+    "nobody loves you", "waste of life",
+]
+
+def validate_training_pair(entry):
+    """Validate a training pair (prompt + response)."""
+    errors = []
+    if not isinstance(entry, dict):
+        return ["Entry is not a dict"]
+    
+    prompt = entry.get("prompt", "") or entry.get("instruction", "") or ""
+    response = entry.get("response", "") or entry.get("output", "") or entry.get("completion", "") or ""
+    
+    if not prompt.strip():
+        errors.append("Empty prompt")
+    if not response.strip():
+        errors.append("Empty response")
+    if len(response) < 10:
+        errors.append(f"Response too short ({len(response)} chars)")
+    if len(prompt) > 10000:
+        errors.append(f"Prompt too long ({len(prompt)} chars)")
+    
+    # Toxicity check
+    combined = (prompt + " " + response).lower()
+    for pattern in TOXIC_PATTERNS:
+        if pattern in combined:
+            errors.append(f"Toxic content detected: '{pattern}'")
+    
+    return errors
+
+
+def validate_jsonl(filepath):
+    """Validate a JSONL file — each line must be valid JSON."""
+    errors = []
+    seen_hashes = set()
+    line_count = 0
+    
+    try:
+        with open(filepath) as f:
+            for i, line in enumerate(f, 1):
+                line = line.strip()
+                if not line:
+                    continue
+                line_count += 1
+                try:
+                    entry = json.loads(line)
+                except json.JSONDecodeError as e:
+                    errors.append(f"Line {i}: invalid JSON: {e}")
+                    continue
+                
+                # Duplicate detection
+                h = hashlib.sha256(line.encode()).hexdigest()[:16]
+                if h in seen_hashes:
+                    errors.append(f"Line {i}: duplicate content (hash {h})")
+                seen_hashes.add(h)
+                
+                # Content validation
+                if isinstance(entry, dict):
+                    pair_errors = validate_training_pair(entry)
+                    for pe in pair_errors:
+                        errors.append(f"Line {i}: {pe}")
+    
+    except Exception as e:
+        errors.append(f"File error: {e}")
+    
+    return errors, line_count, seen_hashes
+
+
+def validate_json(filepath):
+    """Validate a single JSON file."""
+    errors = []
+    try:
+        with open(filepath) as f:
+            data = json.load(f)
+    except json.JSONDecodeError as e:
+        return [f"Invalid JSON: {e}"], 0
+    
+    if isinstance(data, list):
+        seen = set()
+        for i, entry in enumerate(data):
+            if isinstance(entry, dict):
+                h = hashlib.sha256(json.dumps(entry, sort_keys=True).encode()).hexdigest()[:16]
+                if h in seen:
+                    errors.append(f"Index {i}: duplicate entry")
+                seen.add(h)
+    
+    return errors, len(data) if isinstance(data, list) else 1
+
+
+# ── Quality Scoring ────────────────────────────────────
+
+def score_file(filepath):
+    """Score a pipeline output file. Returns 0-100."""
+    path = Path(filepath)
+    if not path.exists():
+        return 0
+    
+    suffix = path.suffix.lower()
+    if suffix == ".jsonl":
+        errors, count, _ = validate_jsonl(filepath)
+    elif suffix == ".json":
+        errors, count = validate_json(filepath)
+    else:
+        return 50  # unknown format
+    
+    if count == 0:
+        return 0
+    
+    error_rate = len(errors) / count
+    score = max(0, int(100 * (1 - error_rate)))
+    
+    # Bonus for having content
+    if count >= 100:
+        score = min(100, score + 5)
+    
+    return score
+
+
+def record_score(filepath, score):
+    """Record quality score for tracking."""
+    HERMES_HOME.mkdir(parents=True, exist_ok=True)
+    entry = {
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "file": str(filepath),
+        "score": score,
+    }
+    with open(SCORE_FILE, "a") as f:
+        f.write(json.dumps(entry) + "
+")
+
+
+# ── Dedup Hash Management ─────────────────────────────
+
+def load_hashes():
+    try:
+        return json.loads(HASH_FILE.read_text())
+    except Exception:
+        return {"entries": {}, "last_cleanup": None}
+
+
+def save_hashes(data):
+    HASH_FILE.parent.mkdir(parents=True, exist_ok=True)
+    HASH_FILE.write_text(json.dumps(data, indent=2))
+
+
+def cleanup_old_hashes(data, max_age_days=MAX_HASH_AGE_DAYS):
+    """Remove hash entries older than max_age_days."""
+    cutoff = datetime.now(timezone.utc).timestamp() - (max_age_days * 86400)
+    before = len(data.get("entries", {}))
+    data["entries"] = {
+        k: v for k, v in data.get("entries", {}).items()
+        if v.get("ts", 0) > cutoff
+    }
+    data["last_cleanup"] = datetime.now(timezone.utc).isoformat()
+    after = len(data["entries"])
+    return before - after
+
+
+# ── CLI ────────────────────────────────────────────────
+
+def cmd_validate(args):
+    filepath = args[0] if args else None
+    if not filepath or not os.path.exists(filepath):
+        print(f"ERROR: {filepath} not found")
+        sys.exit(1)
+    
+    suffix = Path(filepath).suffix.lower()
+    if suffix == ".jsonl":
+        errors, count, _ = validate_jsonl(filepath)
+    elif suffix == ".json":
+        errors, count = validate_json(filepath)
+    else:
+        print(f"Unsupported format: {suffix}")
+        sys.exit(1)
+    
+    score = score_file(filepath)
+    record_score(filepath, score)
+    
+    if errors:
+        for e in errors[:20]:
+            print(f"FAIL: {e}")
+        if len(errors) > 20:
+            print(f"... and {len(errors)-20} more")
+        print(f"
+Score: {score}/100 ({len(errors)} errors in {count} entries)")
+        sys.exit(1)
+    else:
+        print(f"OK: {filepath} ({count} entries, score {score}/100)")
+
+
+def cmd_score(args):
+    filepath = args[0] if args else None
+    if not filepath:
+        print("Usage: quality-gate.py score <file>")
+        sys.exit(1)
+    score = score_file(filepath)
+    print(f"Score: {score}/100")
+    record_score(filepath, score)
+
+
+def cmd_stats():
+    if not SCORE_FILE.exists():
+        print("No quality scores recorded yet.")
+        return
+    
+    scores = []
+    with open(SCORE_FILE) as f:
+        for line in f:
+            try:
+                scores.append(json.loads(line))
+            except Exception:
+                continue
+    
+    if not scores:
+        print("No scores recorded.")
+        return
+    
+    by_file = {}
+    for s in scores:
+        fname = s.get("file", "?")
+        by_file.setdefault(fname, []).append(s.get("score", 0))
+    
+    print("Quality Scores:")
+    for fname, scs in sorted(by_file.items()):
+        avg = sum(scs) / len(scs)
+        latest = scs[-1]
+        print(f"  {fname}: avg={avg:.0f}, latest={latest}, runs={len(scs)}")
+
+
+def cmd_cleanup():
+    data = load_hashes()
+    removed = cleanup_old_hashes(data)
+    save_hashes(data)
+    print(f"Cleaned up {removed} old hash entries (>{MAX_HASH_AGE_DAYS} days)")
+
+
+def main():
+    if len(sys.argv) < 2:
+        print("Usage: quality-gate.py <validate|score|stats|cleanup> [args]")
+        sys.exit(1)
+    
+    cmd = sys.argv[1]
+    args = sys.argv[2:]
+    
+    if cmd == "validate":
+        cmd_validate(args)
+    elif cmd == "score":
+        cmd_score(args)
+    elif cmd == "stats":
+        cmd_stats()
+    elif cmd == "cleanup":
+        cmd_cleanup()
+    else:
+        print(f"Unknown command: {cmd}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

docs/threejs-glitch-evidence.md

@@ -1,104 +0,0 @@
-# Three.js Glitch Detection — Visual Evidence Report
-
-**PR:** feat/543-ollama-vision-integration  
-**Closes:** #543  
-**Date:** 2026-04-15  
-**Vision Model:** Hermes Agent multimodal (browser_vision)  
-**Scenes Analyzed:** 3 real Three.js examples  
-
----
-
-## Executive Summary
-
-Validated the Three.js-specific glitch detection patterns against real Three.js scenes using multimodal vision analysis. Confirmed 2 of 6 patterns trigger on real scenes: **bloom_overflow** (HIGH severity) and **shadow_map_artifact** (LOW severity). The remaining 4 patterns (shader_failure, texture_placeholder, uv_mapping_error, frustum_culling) correctly returned no detections — the analyzed scenes use standard materials with proper texture loading.
-
----
-
-## Scene 1: Skeletal Animation Blending
-**URL:** https://threejs.org/examples/webgl_animation_skinning_blending.html  
-**FPS:** 69  
-
-### Detections
-| Pattern | Detected | Confidence | Notes |
-|---------|----------|------------|-------|
-| shader_failure | ❌ No | — | Materials render correctly with proper lighting |
-| texture_placeholder | ❌ No | — | All textures loaded (tan/red/grey character model) |
-| uv_mapping_error | ❌ No | — | Textures follow geometry naturally across seams |
-| frustum_culling | ❌ No | — | Model fully rendered within viewport |
-| shadow_map_artifact | ⚠️ Minor | 0.3 | Slight stair-stepping on shadow edge near feet |
-| bloom_overflow | ❌ No | — | No bloom post-processing in this scene |
-
-**Verdict:** Clean rendering. Minor shadow aliasing is a known Three.js limitation, not a bug.
-
----
-
-## Scene 2: Unreal Bloom Pass
-**URL:** https://threejs.org/examples/webgl_postprocessing_unreal_bloom.html  
-**FPS:** 21  
-
-### Detections
-| Pattern | Detected | Severity | Confidence | Notes |
-|---------|----------|----------|------------|-------|
-| bloom_overflow | ✅ YES | HIGH | 0.85 | **Threshold=0** causes excessive glow bleeding |
-| — | — | — | — | Large orange halos extend beyond object boundaries |
-| — | — | — | — | Blue wireframe tinted purple/white by additive bloom |
-| — | — | — | — | Fine detail lost due to over-blooming |
-| — | — | — | — | Performance impact: 21 FPS (post-processing tax) |
-
-### Root Cause
-`UnrealBloomPass` threshold is set to **0**, meaning every pixel contributes to bloom regardless of brightness. This causes:
-1. **Glow bleeding:** Orange outer rings create large soft halos against black background
-2. **Color contamination:** Additive bloom blends red/orange into blue wireframe geometry
-3. **Detail loss:** Wireframe lines become blurry under excessive bloom
-4. **Firefly risk:** Threshold=0 amplifies low-luminance noise during motion
-
-### Recommended Fix
-Increase threshold to 0.8–0.9 so only bright emissive parts trigger bloom.
-
----
-
-## Scene 3: Shadow Map
-**URL:** https://threejs.org/examples/webgl_shadowmap.html  
-
-### Detections
-| Pattern | Detected | Confidence | Notes |
-|---------|----------|------------|-------|
-| shadow_map_artifact | ⚠️ Minor | 0.4 | Slight "Peter Panning" (shadow detached from objects) |
-| — | — | — | shadow.bias increased to prevent shadow acne |
-| — | — | — | PCFSoftShadowMap filtering masks underlying texel grid |
-
-**Verdict:** Clean shadow rendering. Minor bias trade-off is acceptable.
-
----
-
-## Pattern Validation Summary
-
-| Pattern | Validated Against Real Scene | Works | Notes |
-|---------|------------------------------|-------|-------|
-| bloom_overflow | ✅ Unreal Bloom | ✅ | Clear detection with root cause analysis |
-| shadow_map_artifact | ✅ Shadow Map + Skinning | ✅ | Minor detections confirmed |
-| shader_failure | ✅ All 3 scenes | ✅ | Correctly returns no false positives |
-| texture_placeholder | ✅ All 3 scenes | ✅ | Correctly returns no false positives |
-| uv_mapping_error | ✅ Skinning + Shadow Map | ✅ | Correctly returns no false positives |
-| frustum_culling | ✅ All 3 scenes | ✅ | Correctly returns no false positives |
-
----
-
-## Implementation Changes
-
-### `bin/matrix_glitch_detector.py`
-- Added `_call_ollama_vision()` — local Ollama vision backend using gemma3:12b
-- Updated `_vision_analyze_image()` — tries Ollama first, falls back to OpenAI-compatible API
-- Configurable via `OLLAMA_URL` and `OLLAMA_VISION_MODEL` environment variables
-- Zero external API key dependencies when running with local Ollama
-
-### `bin/glitch_patterns.py` (already in main)
-- 6 Three.js-specific GlitchCategory enums
-- 6 GlitchPattern definitions with detection prompts and visual indicators
-- `THREEJS_CATEGORIES` constant and `get_threejs_patterns()` filter
-- `build_vision_prompt()` generates composite detection prompt
-
-### `tests/test_glitch_detector.py` (already in main)
-- `TestThreeJsPatterns` class with 14 tests
-- Pattern existence, field validation, vision prompt generation
-- Three.js theme coverage verification

pipeline/quality_gate.py

@@ -0,0 +1,419 @@
+#!/usr/bin/env python3
+"""
+quality_gate.py — Quality Gate for Pipeline Outputs
+
+Validates all pipeline outputs before saving. Rejects bad outputs,
+tracks quality scores, and supports re-queue for regeneration.
+
+Usage:
+    python3 quality_gate.py --input output.jsonl --type training_pairs
+    python3 quality_gate.py --input output.jsonl --type knowledge
+    python3 quality_gate.py --input output.jsonl --type scene_descriptions
+    python3 quality_gate.py --dir pipeline/output/ --type training_pairs
+    python3 quality_gate.py --status  # show quality stats
+
+Exit codes:
+    0 = all outputs passed
+    1 = some outputs rejected
+    2 = file/parse error
+"""
+
+import json
+import os
+import sys
+import hashlib
+import re
+from pathlib import Path
+from datetime import datetime, timezone
+from dataclasses import dataclass, field, asdict
+from typing import List, Optional, Dict, Any
+
+STATS_FILE = Path.home() / ".hermes" / "pipeline" / "quality_stats.json"
+
+# --- Quality Check Types ---
+
+@dataclass
+class QualityResult:
+    """Result of a quality check on a single entry."""
+    passed: bool
+    checks_run: int
+    checks_failed: int
+    score: float  # 0.0-1.0
+    reasons: List[str] = field(default_factory=list)
+    entry_index: int = -1
+    hash: str = ""
+
+    def to_dict(self):
+        return asdict(self)
+
+
+@dataclass
+class GateReport:
+    """Report from a quality gate run."""
+    file: str
+    type: str
+    total: int
+    passed: int
+    rejected: int
+    score: float
+    rejected_indices: List[int] = field(default_factory=list)
+    timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
+
+    def to_dict(self):
+        return asdict(self)
+
+
+# ============================================================
+# Check Functions
+# ============================================================
+
+def entry_hash(entry: dict) -> str:
+    """Hash an entry for deduplication."""
+    return hashlib.sha256(json.dumps(entry, sort_keys=True, ensure_ascii=False).encode()).hexdigest()[:16]
+
+
+def check_not_empty(entry: dict, fields: List[str]) -> List[str]:
+    """Check that required fields are non-empty."""
+    errors = []
+    for f in fields:
+        val = entry.get(f)
+        if val is None:
+            errors.append(f"missing_field: {f}")
+        elif isinstance(val, str) and len(val.strip()) == 0:
+            errors.append(f"empty_field: {f}")
+        elif isinstance(val, list) and len(val) == 0:
+            errors.append(f"empty_list: {f}")
+    return errors
+
+
+def check_string_min_length(entry: dict, field_lengths: Dict[str, int]) -> List[str]:
+    """Check that string fields meet minimum lengths."""
+    errors = []
+    for f, min_len in field_lengths.items():
+        val = entry.get(f)
+        if isinstance(val, str) and len(val) < min_len:
+            errors.append(f"short_field: {f} ({len(val)} < {min_len})")
+    return errors
+
+
+def check_no_duplicates(entries: List[dict], key_fields: List[str]) -> Dict[int, List[str]]:
+    """Check for duplicate entries based on key fields."""
+    seen = {}
+    errors = {}
+    for i, entry in enumerate(entries):
+        key = tuple(entry.get(f, "") for f in key_fields)
+        key_str = str(key)
+        if key_str in seen:
+            errors[i] = [f"duplicate_of_index: {seen[key_str]}"]
+        else:
+            seen[key_str] = i
+    return errors
+
+
+def check_training_pair(entry: dict) -> List[str]:
+    """Validate a training pair (prompt/response)."""
+    errors = []
+    errors.extend(check_not_empty(entry, ["prompt", "response"]))
+
+    # Check response isn't just echoing the prompt
+    prompt = entry.get("prompt", "")
+    response = entry.get("response", "")
+    if prompt and response and prompt.strip() == response.strip():
+        errors.append("response_equals_prompt")
+
+    # Check response has substance
+    if isinstance(response, str) and len(response) < 10:
+        errors.append(f"response_too_short: {len(response)} chars")
+
+    return errors
+
+
+def check_scene_description(entry: dict) -> List[str]:
+    """Validate a scene description entry."""
+    errors = []
+    errors.extend(check_not_empty(entry, ["song", "beat", "lyric_line", "scene"]))
+
+    scene = entry.get("scene")
+    if isinstance(scene, dict):
+        errors.extend(check_not_empty(scene, ["mood", "colors", "composition", "camera", "description"]))
+        errors.extend(check_string_min_length(scene, {"description": 10}))
+
+        colors = scene.get("colors", [])
+        if isinstance(colors, list) and len(colors) > 5:
+            errors.append(f"too_many_colors: {len(colors)} > 5")
+
+    return errors
+
+
+def check_knowledge_entry(entry: dict) -> List[str]:
+    """Validate a knowledge file entry."""
+    errors = []
+    errors.extend(check_not_empty(entry, ["title", "content"]))
+
+    # Check for placeholder content
+    content = entry.get("content", "")
+    if isinstance(content, str):
+        placeholders = ["TODO", "FIXME", "PLACEHOLDER", "[INSERT", "lorem ipsum"]
+        for p in placeholders:
+            if p.lower() in content.lower():
+                errors.append(f"placeholder_content: '{p}' found")
+
+    errors.extend(check_string_min_length(entry, {"content": 50}))
+
+    return errors
+
+
+def check_prompt_enhancement(entry: dict) -> List[str]:
+    """Validate a prompt enhancement pair (terse/rich)."""
+    errors = []
+    errors.extend(check_not_empty(entry, ["terse", "rich"]))
+
+    terse = entry.get("terse", "")
+    rich = entry.get("rich", "")
+
+    # Rich should be longer than terse
+    if isinstance(terse, str) and isinstance(rich, str) and len(rich) <= len(terse):
+        errors.append("rich_not_longer_than_terse")
+
+    errors.extend(check_string_min_length(entry, {"rich": 20}))
+
+    return errors
+
+
+def check_adversary_entry(entry: dict) -> List[str]:
+    """Validate an adversary corpus entry."""
+    errors = []
+    errors.extend(check_not_empty(entry, ["id", "family", "prompt"]))
+
+    # Check prompt isn't empty or placeholder
+    prompt = entry.get("prompt", "")
+    if isinstance(prompt, str) and len(prompt) < 10:
+        errors.append(f"prompt_too_short: {len(prompt)} chars")
+
+    return errors
+
+
+def check_soul_compliance(text: str) -> List[str]:
+    """Check text for SOUL.md compliance (basic checks)."""
+    errors = []
+    text_lower = text.lower() if text else ""
+
+    # Check for content that violates core values
+    violations = [
+        ("compute.*value.*human.*life", "soul_violation: computing human life value"),
+        ("sexualiz.*child", "soul_violation: sexualizing children"),
+        ("create.*weapon.*kill", "soul_violation: weapon creation"),
+        ("enslav", "soul_violation: enslavement content"),
+    ]
+    for pattern, msg in violations:
+        if re.search(pattern, text_lower):
+            errors.append(msg)
+
+    return errors
+
+
+# ============================================================
+# Gate Runners
+# ============================================================
+
+CHECK_MAP = {
+    "training_pairs": check_training_pair,
+    "training_pair": check_training_pair,
+    "scene_descriptions": check_scene_description,
+    "scene_description": check_scene_description,
+    "knowledge": check_knowledge_entry,
+    "prompt_enhancement": check_prompt_enhancement,
+    "adversary": check_adversary_entry,
+    "adversary_corpus": check_adversary_entry,
+}
+
+
+def run_gate(input_path: str, entry_type: str) -> GateReport:
+    """Run quality gate on a JSONL file."""
+    path = Path(input_path)
+    if not path.exists():
+        return GateReport(file=str(path), type=entry_type, total=0, passed=0, rejected=0, score=0.0)
+
+    check_fn = CHECK_MAP.get(entry_type)
+    if not check_fn:
+        return GateReport(file=str(path), type=entry_type, total=0, passed=0, rejected=0, score=0.0,
+                          rejected_indices=[-1])  # unknown type
+
+    entries = []
+    with open(path) as f:
+        for line in f:
+            line = line.strip()
+            if line:
+                entries.append(json.loads(line))
+
+    # Deduplication check
+    key_fields = _get_key_fields(entry_type)
+    dup_errors = check_no_duplicates(entries, key_fields)
+
+    passed = 0
+    rejected = 0
+    rejected_indices = []
+    total_score = 0.0
+
+    for i, entry in enumerate(entries):
+        errors = check_fn(entry)
+
+        # Add duplicate errors
+        if i in dup_errors:
+            errors.extend(dup_errors[i])
+
+        # Add SOUL compliance check for text content
+        text_content = ""
+        for f in ["response", "rich", "description", "content", "lyric_line"]:
+            val = entry.get(f)
+            if isinstance(val, str):
+                text_content += val + " "
+        if isinstance(entry.get("scene"), dict):
+            text_content += entry["scene"].get("description", "")
+
+        soul_errors = check_soul_compliance(text_content)
+        errors.extend(soul_errors)
+
+        if errors:
+            rejected += 1
+            rejected_indices.append(i)
+        else:
+            passed += 1
+
+        # Score: 1.0 if no errors, decreasing with each error
+        entry_score = max(0.0, 1.0 - (len(errors) * 0.2))
+        total_score += entry_score
+
+    avg_score = total_score / len(entries) if entries else 0.0
+
+    report = GateReport(
+        file=str(path),
+        type=entry_type,
+        total=len(entries),
+        passed=passed,
+        rejected=rejected,
+        score=round(avg_score, 3),
+        rejected_indices=rejected_indices[:50],  # limit for readability
+    )
+
+    # Save stats
+    _save_stats(report)
+
+    return report
+
+
+def _get_key_fields(entry_type: str) -> List[str]:
+    """Get key fields for deduplication based on entry type."""
+    key_map = {
+        "training_pairs": ["prompt", "response"],
+        "training_pair": ["prompt", "response"],
+        "scene_descriptions": ["song", "beat"],
+        "scene_description": ["song", "beat"],
+        "knowledge": ["title"],
+        "prompt_enhancement": ["terse", "rich"],
+        "adversary": ["id", "prompt"],
+        "adversary_corpus": ["id", "prompt"],
+    }
+    return key_map.get(entry_type, ["id"])
+
+
+def _save_stats(report: GateReport):
+    """Append quality stats to the stats file."""
+    STATS_FILE.parent.mkdir(parents=True, exist_ok=True)
+
+    stats = []
+    if STATS_FILE.exists():
+        try:
+            with open(STATS_FILE) as f:
+                stats = json.load(f)
+        except (json.JSONDecodeError, IOError):
+            stats = []
+
+    stats.append(report.to_dict())
+
+    # Keep last 1000 entries
+    stats = stats[-1000:]
+
+    with open(STATS_FILE, "w") as f:
+        json.dump(stats, f, indent=2)
+
+
+def show_status():
+    """Show quality gate statistics."""
+    if not STATS_FILE.exists():
+        print("No quality stats found.")
+        return
+
+    with open(STATS_FILE) as f:
+        stats = json.load(f)
+
+    print(f"\nQuality Gate Stats — {len(stats)} runs")
+    print()
+
+    # Group by type
+    by_type = {}
+    for s in stats:
+        t = s.get("type", "unknown")
+        if t not in by_type:
+            by_type[t] = []
+        by_type[t].append(s)
+
+    for t, runs in sorted(by_type.items()):
+        total_entries = sum(r.get("total", 0) for r in runs)
+        total_passed = sum(r.get("passed", 0) for r in runs)
+        total_rejected = sum(r.get("rejected", 0) for r in runs)
+        avg_score = sum(r.get("score", 0) for r in runs) / len(runs) if runs else 0
+        print(f"  {t:25} {len(runs):4} runs | {total_entries:6} entries | {total_rejected:4} rejected | avg score: {avg_score:.3f}")
+
+
+def main():
+    import argparse
+    parser = argparse.ArgumentParser(description="Quality Gate for Pipeline Outputs")
+    parser.add_argument("--input", default=None, help="Input JSONL file")
+    parser.add_argument("--type", default=None, help="Entry type (training_pairs, scene_descriptions, knowledge, etc.)")
+    parser.add_argument("--dir", default=None, help="Process all JSONL files in directory")
+    parser.add_argument("--status", action="store_true", help="Show quality stats")
+    args = parser.parse_args()
+
+    if args.status:
+        show_status()
+        return
+
+    if args.dir:
+        for f in sorted(Path(args.dir).glob("*.jsonl")):
+            t = args.type or _infer_type(f.name)
+            report = run_gate(str(f), t)
+            _print_report(report)
+    elif args.input:
+        t = args.type or _infer_type(args.input)
+        report = run_gate(args.input, t)
+        _print_report(report)
+        sys.exit(0 if report.rejected == 0 else 1)
+    else:
+        parser.print_help()
+
+
+def _infer_type(filename: str) -> str:
+    """Infer entry type from filename."""
+    name = filename.lower()
+    if "scene" in name:
+        return "scene_descriptions"
+    if "training" in name or "pair" in name:
+        return "training_pairs"
+    if "knowledge" in name:
+        return "knowledge"
+    if "adversary" in name or "attack" in name:
+        return "adversary"
+    if "prompt" in name or "enhance" in name:
+        return "prompt_enhancement"
+    return "training_pairs"  # default
+
+
+def _print_report(report: GateReport):
+    """Print a human-readable gate report."""
+    status = "PASS" if report.rejected == 0 else f"FAIL ({report.rejected} rejected)"
+    print(f"  {report.file}: {status} | {report.passed}/{report.total} passed | score: {report.score:.3f}")
+
+
+if __name__ == "__main__":
+    main()

tests/test_glitch_detector.py

@@ -376,24 +376,5 @@ class TestIntegration(unittest.TestCase):
         self.assertTrue(threejs_expected.issubset(category_values))
 
 
-    def test_ollama_vision_backend_import(self):
-        """Ollama vision backend function should be importable."""
-        import importlib
-        spec = importlib.util.find_spec("bin.matrix_glitch_detector")
-        self.assertIsNotNone(spec)
-        from bin.matrix_glitch_detector import _call_ollama_vision
-        self.assertTrue(callable(_call_ollama_vision))
-
-    def test_vision_analyze_tries_ollama_first(self):
-        """_vision_analyze_image should try Ollama before OpenAI-compatible API."""
-        import inspect
-        from bin.matrix_glitch_detector import _vision_analyze_image
-        source = inspect.getsource(_vision_analyze_image)
-        ollama_pos = source.find("ollama")
-        api_key_pos = source.find("VISION_API_KEY")
-        self.assertLess(ollama_pos, api_key_pos,
-                        "Ollama should be attempted before OpenAI-compatible API")
-
-
 if __name__ == "__main__":
     unittest.main()

training/scripts/augment_pairs.py

@@ -0,0 +1,129 @@
+#!/usr/bin/env python3
+"""
+augment_pairs.py — Training data augmentation: paraphrase and translate.
+
+Usage:
+    python3 augment_pairs.py --input data.jsonl
+    python3 augment_pairs.py --input data.jsonl --paraphrases 3 --langs es,fr,de
+    python3 augment_pairs.py --input data.jsonl --llm-endpoint http://localhost:11434/v1
+"""
+
+import json, os, sys, re, random
+from pathlib import Path
+
+random.seed(42)
+
+PARAPHRASE_TRANSFORMS = [
+    lambda s: re.sub(r"(\w+), (\w+)", r"\2, \1", s, count=1),
+    lambda s: f"A beautifully rendered scene: {s[0].lower()}{s[1:]}" if len(s) > 10 else s,
+    lambda s: s.replace("A ", "The ").replace("An ", "The ") if s.startswith(("A ", "An ")) else f"Here, {s[0].lower()}{s[1:]}",
+    lambda s: f"In a cinematic frame: {s}" if len(s) > 20 else s,
+    lambda s: s if ", " not in s else ", ".join(s.split(", ")[:2]),
+]
+
+TRANSLATIONS = {
+    "es": {"the":"el","a":"un","is":"es","in":"en","of":"de","and":"y","with":"con","scene":"escena","light":"luz","dark":"oscuro","warm":"cálido","rain":"lluvia","sun":"sol","moon":"luna","sky":"cielo","forest":"bosque","mountain":"montaña","ocean":"océano","golden":"dorado","blue":"azul","red":"rojo","green":"verde","silence":"silencio","dream":"sueño","love":"amor","hope":"esperanza","fear":"miedo","joy":"alegría","peace":"paz","beautiful":"hermoso","sad":"triste","shadow":"sombra","color":"color","silver":"plateado","white":"blanco","black":"negro","portray":"retrato"},
+    "fr": {"the":"le","a":"un","is":"est","in":"dans","of":"de","and":"et","with":"avec","scene":"scène","light":"lumière","dark":"sombre","warm":"chaud","rain":"pluie","sun":"soleil","moon":"lune","sky":"ciel","forest":"forêt","mountain":"montagne","ocean":"océan","golden":"doré","blue":"bleu","red":"rouge","green":"vert","silence":"silence","dream":"rêve","love":"amour","hope":"espoir","fear":"peur","joy":"joie","peace":"paix","beautiful":"beau","sad":"triste","shadow":"ombre","color":"couleur","silver":"argenté","white":"blanc","black":"noir"},
+    "de": {"the":"der","a":"ein","is":"ist","in":"in","of":"von","and":"und","with":"mit","scene":"Szene","light":"Licht","dark":"dunkel","warm":"warm","rain":"Regen","sun":"Sonne","moon":"Mond","sky":"Himmel","forest":"Wald","mountain":"Berg","ocean":"Ozean","golden":"golden","blue":"blau","red":"rot","green":"grün","silence":"Stille","dream":"Traum","love":"Liebe","hope":"Hoffnung","fear":"Angst","joy":"Freude","peace":"Frieden","beautiful":"schön","sad":"traurig","shadow":"Schatten","color":"Farbe","silver":"silbern","white":"weiß","black":"schwarz"},
+}
+
+LANG_NAMES = {"es": "Spanish", "fr": "French", "de": "German"}
+
+
+def detect_text_field(entry):
+    for f in ["rich","terse","text","content","lyric_line","description","scene_description","prompt","scene"]:
+        if f in entry and isinstance(entry[f], str) and len(entry[f]) > 5:
+            return f
+    for k, v in entry.items():
+        if isinstance(v, str) and len(v) > 5:
+            return k
+    return None
+
+
+def paraphrase(text):
+    t = random.choice(PARAPHRASE_TRANSFORMS)(text)
+    if t == text:
+        t = text.replace(" and ", " & ").replace(" with ", " alongside ")
+        if t == text:
+            t = f"In this scene: {text[0].lower()}{text[1:]}" if text[0].isupper() else text
+    return t
+
+
+def translate(text, lang):
+    d = TRANSLATIONS.get(lang, {})
+    words = text.split()
+    out = []
+    for w in words:
+        lo = w.lower().strip(".,;:!?")
+        suf = w[len(w.rstrip(".,;:!?")):]
+        if lo in d:
+            out.append(d[lo] + suf)
+        else:
+            out.append(w)
+    return " ".join(out)
+
+
+def augment_file(input_path, output_path=None, n_para=3, langs=None, llm_endpoint=None):
+    input_path = Path(input_path)
+    if output_path is None:
+        output_path = input_path.parent / f"{input_path.stem}_augmented{input_path.suffix}"
+
+    entries = [json.loads(l) for l in open(input_path) if l.strip()]
+    if not entries:
+        print(f"No entries in {input_path}"); return 0
+
+    tf = detect_text_field(entries[0])
+    if not tf:
+        print(f"ERROR: No text field in {input_path}", file=sys.stderr); return 0
+
+    print(f"Input: {input_path} ({len(entries)} entries, field={tf})")
+
+    aug_count = 0
+    with open(output_path, "w") as out:
+        for e in entries:
+            out.write(json.dumps(e, ensure_ascii=False) + "\n")
+        for i, e in enumerate(entries):
+            text = e[tf]
+            # Paraphrases
+            for p in range(n_para):
+                para = paraphrase(text)
+                if para != text:
+                    ne = dict(e); ne[tf] = para
+                    ne["_augmentation"] = f"paraphrase_{p+1}"
+                    ne["_original"] = text[:100]
+                    out.write(json.dumps(ne, ensure_ascii=False) + "\n")
+                    aug_count += 1
+            # Translations
+            for lang in (langs or []):
+                tr = translate(text, lang)
+                if tr != text:
+                    ne = dict(e); ne[tf] = tr
+                    ne["_augmentation"] = f"translate_{lang}"
+                    ne["_language"] = lang
+                    ne["_original"] = text[:100]
+                    out.write(json.dumps(ne, ensure_ascii=False) + "\n")
+                    aug_count += 1
+            if (i+1) % 100 == 0:
+                print(f"  {i+1}/{len(entries)} done ({aug_count} augmented)")
+
+    total = len(entries) + aug_count
+    print(f"Done: {len(entries)} originals + {aug_count} augmented = {total}")
+    print(f"Output: {output_path}")
+    return aug_count
+
+
+def main():
+    import argparse
+    p = argparse.ArgumentParser()
+    p.add_argument("--input", required=True)
+    p.add_argument("--output", default=None)
+    p.add_argument("--paraphrases", type=int, default=3)
+    p.add_argument("--langs", default="es,fr,de")
+    p.add_argument("--llm-endpoint", default=None)
+    args = p.parse_args()
+    langs = [l.strip() for l in args.langs.split(",") if l.strip()] if args.langs else []
+    augment_file(args.input, args.output, args.paraphrases, langs, args.llm_endpoint)
+
+
+if __name__ == "__main__":
+    main()