"""
Skill Loader - Progressive Disclosure Implementation.

This module provides progressive disclosure loading for agent skills:
- Frontmatter: Always loaded (~50 words)
- Summary: Loaded on skill activation (~100 words)
- Full: Loaded on demand (entire skill content)

Based on Anthropic's Agent Skills pattern.
"""

import logging
import re
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import Any

import yaml

from .schemas import TeamRole

logger = logging.getLogger(__name__)

# Skill directory relative to this file
SKILLS_DIR = Path(__file__).parent.parent / "prompts" / "skills"
REFERENCES_DIR = "references"


class LoadLevel(str, Enum):
    """Progressive disclosure levels."""

    FRONTMATTER = "frontmatter"  # Always loaded (metadata only)
    SUMMARY = "summary"  # Loaded on activation (~100 words)
    FULL = "full"  # Full content on demand


@dataclass
class SkillMetadata:
    """Metadata extracted from skill frontmatter."""

    name: str
    version: str = "1.0.0"
    triggers: list[str] = field(default_factory=list)
    summary: str = ""
    priority: str = "medium"
    estimated_tokens: int = 500
    author: str = "system"
    tags: list[str] = field(default_factory=list)


@dataclass
class SkillContent:
    """Loaded skill content at a specific disclosure level."""

    level: LoadLevel
    metadata: SkillMetadata
    content: str
    references: dict[str, str] = field(default_factory=dict)


def _normalize_role(role: str | TeamRole) -> str:
    """Convert role to directory name."""
    if isinstance(role, TeamRole):
        return role.value
    return role.lower().strip()


def _parse_frontmatter(content: str) -> tuple[dict[str, Any], str]:
    """
    Parse YAML frontmatter from markdown content.

    Returns:
        Tuple of (frontmatter_dict, remaining_content)
    """
    # Match YAML frontmatter between --- markers
    pattern = r"^---\s*\n(.*?)\n---\s*\n(.*)$"
    match = re.match(pattern, content, re.DOTALL)

    if match:
        frontmatter_yaml = match.group(1)
        body = match.group(2)
        try:
            frontmatter = yaml.safe_load(frontmatter_yaml) or {}
        except yaml.YAMLError as e:
            logger.warning(f"Failed to parse frontmatter: {e}")
            frontmatter = {}
        return frontmatter, body

    return {}, content


def _extract_summary(content: str, max_words: int = 100) -> str:
    """
    Extract summary from content (first max_words words).

    Strips headers, focuses on core instructions.
    """
    # Remove markdown headers
    lines = []
    for line in content.split("\n"):
        # Skip header lines but keep content
        if line.startswith("#"):
            continue
        lines.append(line)

    cleaned = " ".join(lines)

    # Token estimation: ~4 chars per token
    max_chars = max_words * 4

    if len(cleaned) <= max_chars:
        return cleaned.strip()

    # Truncate at word boundary
    truncated = cleaned[:max_chars]
    last_space = truncated.rfind(" ")
    if last_space > max_chars * 0.8:  # At least 80% of max
        truncated = truncated[:last_space]

    return truncated.strip() + "..."


class SkillLoader:
    """
    Progressive disclosure loader for agent skills.

    Loads skill content at different disclosure levels:
    - FRONTMATTER: Always, for routing decisions
    - SUMMARY: On skill activation, core instructions
    - FULL: On demand, complete context

    Example:
        >>> loader = SkillLoader()
        >>> metadata = await loader.load_metadata("product_owner")
        >>> if should_activate(metadata):
        ...     summary = await loader.load("product_owner", LoadLevel.SUMMARY)
    """

    def __init__(self, base_path: Path | None = None):
        self.base_path = base_path or SKILLS_DIR
        self._metadata_cache: dict[str, SkillMetadata] = {}
        self._content_cache: dict[str, str] = {}

    def _get_skill_path(self, role: str) -> Path:
        """Get path to skill folder for role."""
        normalized = _normalize_role(role)
        return self.base_path / normalized

    def _get_skill_file_path(self, role: str, filename: str = "SKILL.md") -> Path:
        """Get path to a file within the skill folder."""
        return self._get_skill_path(role) / filename

    def _list_references(self, role: str) -> list[Path]:
        """List all reference files in the skill's references folder."""
        skill_path = self._get_skill_path(role)
        refs_path = skill_path / REFERENCES_DIR

        if not refs_path.exists():
            return []

        return sorted(refs_path.glob("*.md"))

    async def load_metadata(self, role: str | TeamRole) -> SkillMetadata | None:
        """
        Load skill metadata (from frontmatter) - always fast operation.

        This is the lightest weight load - just parses YAML frontmatter.
        """
        role_value = _normalize_role(role)

        if role_value in self._metadata_cache:
            return self._metadata_cache[role_value]

        skill_path = self._get_skill_file_path(role_value, "SKILL.md")

        if not skill_path.exists():
            logger.debug(f"Skill file not found: {skill_path}")
            return None

        try:
            content = skill_path.read_text(encoding="utf-8")
            frontmatter, _ = _parse_frontmatter(content)

            metadata = SkillMetadata(
                name=frontmatter.get("name", role_value),
                version=frontmatter.get("version", "1.0.0"),
                triggers=frontmatter.get("triggers", []),
                summary=frontmatter.get("summary", ""),
                priority=frontmatter.get("priority", "medium"),
                estimated_tokens=frontmatter.get("estimated_tokens", 500),
                author=frontmatter.get("author", "system"),
                tags=frontmatter.get("tags", []),
            )

            self._metadata_cache[role_value] = metadata
            return metadata

        except Exception as e:
            logger.error(f"Failed to load metadata for {role_value}: {e}")
            return None

    async def load(
        self,
        role: str | TeamRole,
        level: LoadLevel = LoadLevel.FRONTMATTER,
        reference_query: str | None = None,
    ) -> SkillContent | None:
        """
        Load skill content at specified disclosure level.

        Args:
            role: The role to load skill for
            level: Disclosure level (frontmatter, summary, full)
            reference_query: Optional query for semantic search in references

        Returns:
            SkillContent with metadata and content at requested level
        """
        role_value = _normalize_role(role)

        # Get metadata (always needed)
        metadata = await self.load_metadata(role_value)
        if not metadata:
            return None

        # Handle each level
        if level == LoadLevel.FRONTMATTER:
            # Just return metadata summary as content
            return SkillContent(
                level=level,
                metadata=metadata,
                content=metadata.summary
                or f"Skill: {metadata.name} v{metadata.version}",
            )

        # Load full content for SUMMARY or FULL
        skill_path = self._get_skill_file_path(role_value, "SKILL.md")

        if not skill_path.exists():
            logger.warning(f"Skill file not found: {skill_path}")
            return None

        # Check cache for full content
        cache_key = f"{role_value}:full"
        if cache_key not in self._content_cache:
            self._content_cache[cache_key] = skill_path.read_text(encoding="utf-8")

        full_content = self._content_cache[cache_key]

        # Parse frontmatter to get body
        _, body = _parse_frontmatter(full_content)

        if level == LoadLevel.SUMMARY:
            # Extract summary (first ~100 words)
            summary = _extract_summary(body, max_words=100)
            return SkillContent(
                level=level,
                metadata=metadata,
                content=summary,
            )

        # FULL level - load with references if query provided
        references: dict[str, str] = {}

        if reference_query:
            # Semantic search in references would go here
            # For now, load all references
            for ref_file in self._list_references(role_value):
                ref_content = ref_file.read_text(encoding="utf-8")
                references[ref_file.stem] = ref_content

        return SkillContent(
            level=level,
            metadata=metadata,
            content=body,
            references=references,
        )

    async def should_activate(self, role: str | TeamRole, user_input: str) -> bool:
        """
        Determine if skill should activate based on user input.

        Matches against triggers in frontmatter.
        """
        metadata = await self.load_metadata(role)
        if not metadata or not metadata.triggers:
            return True  # No triggers = always activate

        input_lower = user_input.lower()

        return any(trigger.lower() in input_lower for trigger in metadata.triggers)

    async def list_skills(self) -> list[str]:
        """List all available skills."""
        if not self.base_path.exists():
            return []

        skills = []
        for item in self.base_path.iterdir():
            if item.is_dir() and (item / "SKILL.md").exists():
                skills.append(item.name)

        return sorted(skills)

    def clear_cache(self):
        """Clear all caches (useful for development)."""
        self._metadata_cache.clear()
        self._content_cache.clear()
        logger.info("Skill loader cache cleared")


# Singleton instance
_loader: SkillLoader | None = None


def get_skill_loader() -> SkillLoader:
    """Get singleton SkillLoader instance."""
    global _loader
    if _loader is None:
        _loader = SkillLoader()
    return _loader


# Convenience functions for common operations
async def get_skill_summary(role: str | TeamRole) -> str | None:
    """Get skill summary at SUMMARY disclosure level."""
    loader = get_skill_loader()
    content = await loader.load(role, LoadLevel.SUMMARY)
    return content.content if content else None


async def get_skill_metadata(role: str | TeamRole) -> SkillMetadata | None:
    """Get skill metadata."""
    loader = get_skill_loader()
    return await loader.load_metadata(role)