Marketing Context

#!/usr/bin/env python3
"""Validate marketing context completeness — scores 0-100."""

import json
import re
import sys
from pathlib import Path

SECTIONS = {
    "Product Overview": {"required": True, "weight": 10, "markers": ["one-liner", "what it does", "product category", "business model"]},
    "Target Audience": {"required": True, "weight": 12, "markers": ["target compan", "decision-maker", "use case", "jobs to be done"]},
    "Personas": {"required": False, "weight": 5, "markers": ["persona", "champion", "decision maker"]},
    "Problems & Pain Points": {"required": True, "weight": 10, "markers": ["core problem", "fall short", "cost", "tension"]},
    "Competitive Landscape": {"required": True, "weight": 10, "markers": ["direct", "competitor", "secondary"]},
    "Differentiation": {"required": True, "weight": 10, "markers": ["differentiator", "differently", "why customers choose"]},
    "Objections": {"required": False, "weight": 5, "markers": ["objection", "response", "anti-persona"]},
    "Switching Dynamics": {"required": False, "weight": 5, "markers": ["push", "pull", "habit", "anxiety"]},
    "Customer Language": {"required": True, "weight": 10, "markers": ["verbatim", "words to use", "words to avoid"]},
    "Brand Voice": {"required": True, "weight": 8, "markers": ["tone", "style", "personality"]},
    "Style Guide": {"required": False, "weight": 3, "markers": ["grammar", "capitalization", "formatting"]},
    "Proof Points": {"required": True, "weight": 7, "markers": ["metric", "customer", "testimonial"]},
    "Content & SEO": {"required": False, "weight": 3, "markers": ["keyword", "internal link"]},
    "Goals": {"required": True, "weight": 2, "markers": ["business goal", "conversion"]}
}


def validate_context(content: str) -> dict:
    """Validate marketing context file and return score."""
    content_lower = content.lower()
    results = {"sections": {}, "score": 0, "max_score": 100, "missing_required": [], "missing_optional": [], "warnings": []}
    total_weight = sum(s["weight"] for s in SECTIONS.values())
    earned = 0

    for name, config in SECTIONS.items():
        section_present = name.lower().replace("& ", "").replace("  ", " ") in content_lower or any(
            m in content_lower for m in config["markers"][:2]
        )

        markers_found = sum(1 for m in config["markers"] if m in content_lower)
        markers_total = len(config["markers"])

        has_placeholder = bool(re.search(r'\[.*?\]', content[content_lower.find(name.lower()):content_lower.find(name.lower()) + 500] if name.lower() in content_lower else ""))

        if section_present and markers_found > 0:
            completeness = markers_found / markers_total
            if has_placeholder and completeness < 0.5:
                completeness *= 0.5  # Penalize unfilled templates
            section_score = round(config["weight"] * completeness)
            earned += section_score
            status = "complete" if completeness >= 0.75 else "partial"
        else:
            section_score = 0
            status = "missing"
            if config["required"]:
                results["missing_required"].append(name)
            else:
                results["missing_optional"].append(name)

        results["sections"][name] = {
            "status": status,
            "markers_found": markers_found,
            "markers_total": markers_total,
            "score": section_score,
            "max_score": config["weight"],
            "required": config["required"]
        }

    results["score"] = round((earned / total_weight) * 100)

    # Warnings
    if "verbatim" not in content_lower and '"' not in content:
        results["warnings"].append("No verbatim customer quotes found — copy will sound generic")
    if not re.search(r'\d+%|\$\d+|\d+ customer', content_lower):
        results["warnings"].append("No metrics or proof points with numbers found")
    if "last updated" in content_lower:
        date_match = re.search(r'last up