"""Skill tools — load and finish skills during agent operation."""

from __future__ import annotations

from pathlib import Path
from typing import TYPE_CHECKING, Any, ClassVar

from pydantic import BaseModel, Field

from app.models.config import AppConfig
from app.models.tool_call import ToolResult, ToolResultStatus
from app.tools.base import BaseTool

if TYPE_CHECKING:
    from app.services.skill_runner import SkillRunner
    from app.services.skills import SkillsManager


class LoadSkillParams(BaseModel):
    """Parameters for the load_skill tool."""

    name: str = Field(description="Name of the skill to load")


class LoadSkillTool(BaseTool):
    """Load a skill's full instructions by name.

    Use when a skill is relevant to the current task.
    For package skills, this activates the full skill lifecycle
    (config overrides, chaining, prompt injection).
    """

    name: ClassVar[str] = "load_skill"
    description: ClassVar[str] = (
        "Load a skill's full instructions by name. "
        "Use when a skill is relevant to the current task."
    )
    params_model: ClassVar[type[BaseModel]] = LoadSkillParams

    def __init__(
        self,
        workspace_root: Path,
        config: AppConfig,
        skills_manager: SkillsManager,
        skill_runner: SkillRunner | None = None,
    ) -> None:
        super().__init__(workspace_root, config)
        self._skills = skills_manager
        self._runner = skill_runner

    def set_skill_runner(self, runner: SkillRunner) -> None:
        """Late-bind the SkillRunner (avoids circular init dependencies)."""
        self._runner = runner

    def execute(self, *, tool_call_id: str, **kwargs: Any) -> ToolResult:
        skill_name: str = kwargs["name"]

        # Check if skill exists
        skill = self._skills.get_skill(skill_name)
        if skill is None:
            available = [s.name for s in self._skills.list_skills()]
            return ToolResult(
                tool_call_id=tool_call_id,
                tool_name=self.name,
                status=ToolResultStatus.ERROR,
                error=f"Unknown skill '{skill_name}'. Available: {available}",
            )

        # For package skills with a runner, use full activation flow
        if skill.manifest is not None and self._runner is not None:
            content = self._runner.activate(skill_name)
            if content is None:
                return ToolResult(
                    tool_call_id=tool_call_id,
                    tool_name=self.name,
                    status=ToolResultStatus.ERROR,
                    error=f"Failed to activate skill '{skill_name}'",
                )
            return ToolResult(
                tool_call_id=tool_call_id,
                tool_name=self.name,
                status=ToolResultStatus.SUCCESS,
                output=f"Skill '{skill_name}' activated.\n\n{content}",
            )

        # Legacy skill: just load content
        content = self._skills.load_skill(skill_name)
        if content is None:
            return ToolResult(
                tool_call_id=tool_call_id,
                tool_name=self.name,
                status=ToolResultStatus.ERROR,
                error=f"Failed to load skill '{skill_name}'",
            )
        return ToolResult(
            tool_call_id=tool_call_id,
            tool_name=self.name,
            status=ToolResultStatus.SUCCESS,
            output=content,
        )


class FinishSkillParams(BaseModel):
    """Parameters for the finish_skill tool."""

    summary: str = Field(
        default="Skill complete.",
        description="Brief summary of what was accomplished during the skill",
    )


class FinishSkillTool(BaseTool):
    """Signal that the active skill is complete and should be deactivated.

    Restores config overrides and tool availability to pre-skill state.
    The agent loop continues after this (unlike the finish tool).
    """

    name: ClassVar[str] = "finish_skill"
    description: ClassVar[str] = (
        "Call this when the active skill's task is complete. "
        "Deactivates the skill and restores normal config. "
        "The conversation continues after this."
    )
    params_model: ClassVar[type[BaseModel]] = FinishSkillParams

    def __init__(
        self,
        workspace_root: Path,
        config: AppConfig,
        skill_runner: SkillRunner | None = None,
    ) -> None:
        super().__init__(workspace_root, config)
        self._runner = skill_runner

    def set_skill_runner(self, runner: SkillRunner) -> None:
        """Late-bind the SkillRunner (avoids circular init dependencies)."""
        self._runner = runner

    def execute(self, *, tool_call_id: str, **kwargs: Any) -> ToolResult:
        summary: str = kwargs.get("summary", "Skill complete.")

        if self._runner is None or not self._runner.is_active:
            return ToolResult(
                tool_call_id=tool_call_id,
                tool_name=self.name,
                status=ToolResultStatus.ERROR,
                error="No skill is currently active.",
            )

        skill_name = self._runner.active_skill_name
        self._runner.deactivate(summary=summary)
        return ToolResult(
            tool_call_id=tool_call_id,
            tool_name=self.name,
            status=ToolResultStatus.SUCCESS,
            output=f"Skill '{skill_name}' completed: {summary}",
        )