init

.opencode/skills/context-engineering/scripts/compression_evaluator.py

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+#!/usr/bin/env python3
+"""
+Compression Evaluator - Evaluate compression quality with probe-based testing.
+
+Usage:
+    python compression_evaluator.py evaluate <original_file> <compressed_file>
+    python compression_evaluator.py generate-probes <context_file>
+"""
+
+import argparse
+import json
+import os
+import re
+import sys
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Optional
+
+MAX_FILE_SIZE_MB = 100
+
+
+def load_file(path: str, as_json: bool = True):
+    """Load file with proper error handling and size validation."""
+    try:
+        size_mb = os.path.getsize(path) / (1024 * 1024)
+        if size_mb > MAX_FILE_SIZE_MB:
+            print(f"Error: File too large ({size_mb:.1f}MB). Max {MAX_FILE_SIZE_MB}MB", file=sys.stderr)
+            sys.exit(1)
+        with open(path, encoding='utf-8') as f:
+            return json.load(f) if as_json else f.read()
+    except FileNotFoundError:
+        print(f"Error: File not found: {path}", file=sys.stderr)
+        sys.exit(1)
+    except PermissionError:
+        print(f"Error: Permission denied: {path}", file=sys.stderr)
+        sys.exit(1)
+    except json.JSONDecodeError as e:
+        print(f"Error: Invalid JSON in {path}: {e}", file=sys.stderr)
+        sys.exit(1)
+
+
+class ProbeType(Enum):
+    RECALL = "recall"           # Factual retention
+    ARTIFACT = "artifact"       # File tracking
+    CONTINUATION = "continuation"  # Task planning
+    DECISION = "decision"       # Reasoning chains
+
+
+@dataclass
+class Probe:
+    type: ProbeType
+    question: str
+    ground_truth: str
+    context_reference: Optional[str] = None
+
+
+@dataclass
+class ProbeResult:
+    probe: Probe
+    response: str
+    scores: dict
+    overall_score: float
+
+
+@dataclass
+class EvaluationReport:
+    compression_ratio: float
+    quality_score: float
+    dimension_scores: dict
+    probe_results: list
+    recommendations: list = field(default_factory=list)
+
+
+# Six evaluation dimensions with weights
+DIMENSIONS = {
+    "accuracy": {"weight": 0.20, "description": "Technical correctness"},
+    "context_awareness": {"weight": 0.15, "description": "Conversation state"},
+    "artifact_trail": {"weight": 0.20, "description": "File tracking"},
+    "completeness": {"weight": 0.20, "description": "Coverage and depth"},
+    "continuity": {"weight": 0.15, "description": "Work continuation"},
+    "instruction_following": {"weight": 0.10, "description": "Constraint adherence"}
+}
+
+
+def estimate_tokens(text: str) -> int:
+    """Estimate token count."""
+    return len(text) // 4
+
+
+def extract_facts(messages: list) -> list:
+    """Extract factual statements that can be probed."""
+    facts = []
+    patterns = [
+        (r"error[:\s]+([^.]+)", "error"),
+        (r"next step[s]?[:\s]+([^.]+)", "next_step"),
+        (r"decided to\s+([^.]+)", "decision"),
+        (r"implemented\s+([^.]+)", "implementation"),
+        (r"found that\s+([^.]+)", "finding")
+    ]
+
+    for msg in messages:
+        content = str(msg.get("content", "") if isinstance(msg, dict) else msg)
+        for pattern, fact_type in patterns:
+            matches = re.findall(pattern, content, re.IGNORECASE)
+            for match in matches:
+                facts.append({"type": fact_type, "content": match.strip()})
+    return facts
+
+
+def extract_files(messages: list) -> list:
+    """Extract file references."""
+    files = []
+    patterns = [
+        r"(?:created|modified|updated|edited|read)\s+[`'\"]?([a-zA-Z0-9_/.-]+\.[a-zA-Z]+)[`'\"]?",
+        r"file[:\s]+[`'\"]?([a-zA-Z0-9_/.-]+\.[a-zA-Z]+)[`'\"]?"
+    ]
+
+    for msg in messages:
+        content = str(msg.get("content", "") if isinstance(msg, dict) else msg)
+        for pattern in patterns:
+            matches = re.findall(pattern, content)
+            files.extend(matches)
+    return list(set(files))
+
+
+def extract_decisions(messages: list) -> list:
+    """Extract decision points."""
+    decisions = []
+    patterns = [
+        r"chose\s+([^.]+)\s+(?:because|since|over)",
+        r"decided\s+(?:to\s+)?([^.]+)",
+        r"went with\s+([^.]+)"
+    ]
+
+    for msg in messages:
+        content = str(msg.get("content", "") if isinstance(msg, dict) else msg)
+        for pattern in patterns:
+            matches = re.findall(pattern, content, re.IGNORECASE)
+            decisions.extend(matches)
+    return decisions
+
+
+def generate_probes(messages: list) -> list:
+    """Generate probe set for evaluation."""
+    probes = []
+
+    # Recall probes from facts
+    facts = extract_facts(messages)
+    for fact in facts[:3]:  # Limit to 3 recall probes
+        probes.append(Probe(
+            type=ProbeType.RECALL,
+            question=f"What was the {fact['type'].replace('_', ' ')}?",
+            ground_truth=fact["content"]
+        ))
+
+    # Artifact probes from files
+    files = extract_files(messages)
+    if files:
+        probes.append(Probe(
+            type=ProbeType.ARTIFACT,
+            question="Which files have been modified or created?",
+            ground_truth=", ".join(files)
+        ))
+
+    # Continuation probe
+    probes.append(Probe(
+        type=ProbeType.CONTINUATION,
+        question="What should be done next?",
+        ground_truth="[Extracted from context]"  # Would need LLM to generate
+    ))
+
+    # Decision probes
+    decisions = extract_decisions(messages)
+    for decision in decisions[:2]:  # Limit to 2 decision probes
+        probes.append(Probe(
+            type=ProbeType.DECISION,
+            question=f"Why was the decision made to {decision[:50]}...?",
+            ground_truth=decision
+        ))
+
+    return probes
+
+
+def evaluate_response(probe: Probe, response: str) -> dict:
+    """
+    Evaluate response against probe.
+    Note: Production should use LLM-as-Judge.
+    """
+    scores = {}
+    response_lower = response.lower()
+    ground_truth_lower = probe.ground_truth.lower()
+
+    # Heuristic scoring (replace with LLM evaluation in production)
+    # Check for ground truth presence
+    if ground_truth_lower in response_lower:
+        base_score = 1.0
+    elif any(word in response_lower for word in ground_truth_lower.split()[:3]):
+        base_score = 0.6
+    else:
+        base_score = 0.3
+
+    # Adjust based on probe type
+    if probe.type == ProbeType.ARTIFACT:
+        # Check file mentions
+        files_mentioned = len(re.findall(r'\.[a-z]+', response_lower))
+        scores["artifact_trail"] = min(1.0, base_score + files_mentioned * 0.1)
+        scores["accuracy"] = base_score
+    elif probe.type == ProbeType.RECALL:
+        scores["accuracy"] = base_score
+        scores["completeness"] = base_score
+    elif probe.type == ProbeType.CONTINUATION:
+        scores["continuity"] = base_score
+        scores["context_awareness"] = base_score
+    elif probe.type == ProbeType.DECISION:
+        scores["accuracy"] = base_score
+        scores["context_awareness"] = base_score
+
+    return scores
+
+
+def calculate_compression_ratio(original: str, compressed: str) -> float:
+    """Calculate compression ratio."""
+    original_tokens = estimate_tokens(original)
+    compressed_tokens = estimate_tokens(compressed)
+    if original_tokens == 0:
+        return 0.0
+    return 1.0 - (compressed_tokens / original_tokens)
+
+
+def evaluate_compression(original_messages: list, compressed_text: str,
+                         probes: Optional[list] = None) -> EvaluationReport:
+    """
+    Evaluate compression quality.
+
+    Args:
+        original_messages: Original context messages
+        compressed_text: Compressed summary
+        probes: Optional pre-generated probes
+
+    Returns:
+        EvaluationReport with scores and recommendations
+    """
+    # Generate probes if not provided
+    if probes is None:
+        probes = generate_probes(original_messages)
+
+    # Calculate compression ratio
+    original_text = json.dumps(original_messages)
+    compression_ratio = calculate_compression_ratio(original_text, compressed_text)
+
+    # Evaluate each probe (simulated - production uses LLM)
+    probe_results = []
+    dimension_scores = {dim: [] for dim in DIMENSIONS}
+
+    for probe in probes:
+        # In production, send compressed_text + probe.question to LLM
+        # Here we simulate with heuristic check
+        scores = evaluate_response(probe, compressed_text)
+
+        overall = sum(scores.values()) / len(scores) if scores else 0
+        probe_results.append(ProbeResult(
+            probe=probe,
+            response="[Would be LLM response]",
+            scores=scores,
+            overall_score=overall
+        ))
+
+        # Aggregate by dimension
+        for dim, score in scores.items():
+            if dim in dimension_scores:
+                dimension_scores[dim].append(score)
+
+    # Calculate dimension averages
+    avg_dimensions = {}
+    for dim, scores in dimension_scores.items():
+        avg_dimensions[dim] = sum(scores) / len(scores) if scores else 0.5
+
+    # Calculate weighted quality score
+    quality_score = sum(
+        avg_dimensions.get(dim, 0.5) * info["weight"]
+        for dim, info in DIMENSIONS.items()
+    )
+
+    # Generate recommendations
+    recommendations = []
+    if compression_ratio > 0.99:
+        recommendations.append("Very high compression. Risk of information loss.")
+    if avg_dimensions.get("artifact_trail", 1) < 0.5:
+        recommendations.append("Artifact tracking weak. Add explicit file section to summary.")
+    if avg_dimensions.get("continuity", 1) < 0.5:
+        recommendations.append("Continuity low. Add 'Next Steps' section to summary.")
+    if quality_score < 0.6:
+        recommendations.append("Quality below threshold. Consider less aggressive compression.")
+
+    return EvaluationReport(
+        compression_ratio=compression_ratio,
+        quality_score=quality_score,
+        dimension_scores=avg_dimensions,
+        probe_results=probe_results,
+        recommendations=recommendations
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Compression quality evaluator")
+    subparsers = parser.add_subparsers(dest="command", required=True)
+
+    # Evaluate command
+    eval_parser = subparsers.add_parser("evaluate", help="Evaluate compression quality")
+    eval_parser.add_argument("original_file", help="JSON file with original messages")
+    eval_parser.add_argument("compressed_file", help="Text file with compressed summary")
+
+    # Generate probes command
+    probe_parser = subparsers.add_parser("generate-probes", help="Generate evaluation probes")
+    probe_parser.add_argument("context_file", help="JSON file with context messages")
+
+    args = parser.parse_args()
+
+    if args.command == "evaluate":
+        original = load_file(args.original_file, as_json=True)
+        messages = original if isinstance(original, list) else original.get("messages", [])
+        compressed = load_file(args.compressed_file, as_json=False)
+
+        report = evaluate_compression(messages, compressed)
+        print(json.dumps({
+            "compression_ratio": f"{report.compression_ratio:.1%}",
+            "quality_score": f"{report.quality_score:.2f}",
+            "dimension_scores": {k: f"{v:.2f}" for k, v in report.dimension_scores.items()},
+            "probe_count": len(report.probe_results),
+            "recommendations": report.recommendations
+        }, indent=2))
+
+    elif args.command == "generate-probes":
+        data = load_file(args.context_file, as_json=True)
+        messages = data if isinstance(data, list) else data.get("messages", [])
+
+        probes = generate_probes(messages)
+        output = []
+        for probe in probes:
+            output.append({
+                "type": probe.type.value,
+                "question": probe.question,
+                "ground_truth": probe.ground_truth
+            })
+        print(json.dumps(output, indent=2))
+
+
+if __name__ == "__main__":
+    main()