@dataclass
class Skill:
    name: str                    # Human-readable identifier
    description: str             # What the skill does (for retrieval)
    preconditions: Callable      # When can this skill be invoked?
    parameters: Dict[str, Type]  # Input arguments
    implementation: Callable     # The actual behavior (code or policy)
    postconditions: Callable     # What's true after execution?
    examples: List[str]          # Usage examples for LLM prompting

navigate_to_skill = Skill(
    name="navigate_to",
    description="Move the agent to a target location while avoiding obstacles",
    preconditions=lambda state: state.agent_can_move,
    parameters={"target": "Position", "speed": "float"},
    implementation=navigation_policy,
    postconditions=lambda state, target: distance(state.position, target) < 0.1,
    examples=[
        "navigate_to(target=kitchen, speed=1.0)",
        "navigate_to(target=Position(5, 3), speed=0.5)"
    ]
)

def discover_skills_betweenness(trajectories, graph):
    """Find bottleneck states and create skills to reach them"""
    # Compute betweenness centrality
    betweenness = {}
    for node in graph.nodes:
        paths_through_node = count_shortest_paths_through(node, graph)
        total_paths = count_all_shortest_paths(graph)
        betweenness[node] = paths_through_node / total_paths

    # Top-k nodes become subgoals
    subgoals = sorted(betweenness, key=betweenness.get, reverse=True)[:k]

    # Create skills to reach each subgoal
    skills = []
    for subgoal in subgoals:
        skill = learn_goal_reaching_policy(
            goal=subgoal,
            trajectories=trajectories
        )
        skills.append(skill)

    return skills

def synthesize_skill_with_llm(task_description, environment_api, existing_skills):
    """Generate a new skill using an LLM"""

    prompt = f"""
    You are a skill synthesis agent. Write a Python function to accomplish:

    Task: {task_description}

    Available API:
    {environment_api}

    Existing skills you can call:
    {[s.name + ': ' + s.description for s in existing_skills]}

    Write a function that:
    1. Has a clear name describing what it does
    2. Uses existing skills when possible
    3. Handles edge cases gracefully
    4. Returns True on success, False on failure

    ```python
    def new_skill(...):
    ```
    """

    code = llm.generate(prompt)

    # Verify the skill works
    success = test_skill_in_sandbox(code, environment_api)

    if success:
        skill = parse_code_to_skill(code)
        return skill
    else:
        # Retry with error feedback
        return synthesize_skill_with_llm(
            task_description + f"\nPrevious attempt failed: {error}",
            environment_api,
            existing_skills
        )

class SkillLibrary:
    def __init__(self, embedding_model):
        self.skills = []
        self.embeddings = []
        self.embedding_model = embedding_model

    def add_skill(self, skill: Skill):
        # Embed the skill description
        embedding = self.embedding_model.encode(
            f"{skill.name}: {skill.description}"
        )
        self.skills.append(skill)
        self.embeddings.append(embedding)

    def retrieve(self, task_description: str, top_k: int = 5) -> List[Skill]:
        """Find most relevant skills for a task"""
        query_embedding = self.embedding_model.encode(task_description)

        # Cosine similarity
        similarities = [
            np.dot(query_embedding, emb) /
            (np.linalg.norm(query_embedding) * np.linalg.norm(emb))
            for emb in self.embeddings
        ]

        # Return top-k skills
        top_indices = np.argsort(similarities)[-top_k:][::-1]
        return [self.skills[i] for i in top_indices]

def chain_skills(skills: List[Skill], goal):
    """Execute skills in sequence"""
    for skill in skills:
        if not skill.preconditions(current_state):
            return False, "Precondition failed"

        success = skill.implementation(current_state)

        if not success:
            return False, f"Skill {skill.name} failed"

    return True, "Goal achieved"

make_coffee_skill = Skill(
    name="make_coffee",
    description="Prepare a cup of coffee",
    implementation=lambda: (
        skill_library.get("navigate_to")(target="kitchen") and
        skill_library.get("pick_up")(object="coffee_cup") and
        skill_library.get("use_machine")(machine="coffee_maker") and
        skill_library.get("pour")(source="coffee_maker", target="cup")
    )
)

def plan_with_skills(task: str, skill_library: SkillLibrary, llm):
    relevant_skills = skill_library.retrieve(task, top_k=10)

    prompt = f"""
    Task: {task}

    Available skills:
    {[f"- {s.name}: {s.description}" for s in relevant_skills]}

    Plan a sequence of skill calls to accomplish the task.
    Output as JSON: [{{"skill": "name", "params": {{...}}}}, ...]
    """

    plan = llm.generate(prompt)
    return json.loads(plan)

from dataclasses import dataclass, field
from typing import Callable, Dict, List, Any, Optional
import numpy as np
from sentence_transformers import SentenceTransformer

@dataclass
class Skill:
    name: str
    description: str
    implementation: Callable
    parameters: Dict[str, type] = field(default_factory=dict)
    preconditions: Optional[Callable] = None
    postconditions: Optional[Callable] = None
    success_count: int = 0
    failure_count: int = 0

    @property
    def success_rate(self) -> float:
        total = self.success_count + self.failure_count
        return self.success_count / total if total > 0 else 0.0

    def execute(self, **kwargs) -> bool:
        try:
            result = self.implementation(**kwargs)
            if result:
                self.success_count += 1
            else:
                self.failure_count += 1
            return result
        except Exception as e:
            self.failure_count += 1
            return False


class SkillLibrary:
    def __init__(self, embedding_model_name: str = "all-MiniLM-L6-v2"):
        self.skills: Dict[str, Skill] = {}
        self.encoder = SentenceTransformer(embedding_model_name)
        self.embeddings: Dict[str, np.ndarray] = {}

    def add(self, skill: Skill) -> None:
        """Add a skill to the library"""
        self.skills[skill.name] = skill
        # Create embedding for retrieval
        text = f"{skill.name}: {skill.description}"
        self.embeddings[skill.name] = self.encoder.encode(text)
        print(f"Added skill: {skill.name}")

    def get(self, name: str) -> Optional[Skill]:
        """Get a skill by exact name"""
        return self.skills.get(name)

    def search(self, query: str, top_k: int = 5) -> List[Skill]:
        """Semantic search for relevant skills"""
        query_emb = self.encoder.encode(query)

        scores = []
        for name, emb in self.embeddings.items():
            similarity = np.dot(query_emb, emb) / (
                np.linalg.norm(query_emb) * np.linalg.norm(emb)
            )
            scores.append((name, similarity))

        scores.sort(key=lambda x: x[1], reverse=True)
        return [self.skills[name] for name, _ in scores[:top_k]]

    def compose(self, skill_sequence: List[str], **shared_kwargs) -> bool:
        """Execute a sequence of skills"""
        for skill_name in skill_sequence:
            skill = self.get(skill_name)
            if skill is None:
                print(f"Skill not found: {skill_name}")
                return False

            if skill.preconditions and not skill.preconditions():
                print(f"Precondition failed for: {skill_name}")
                return False

            success = skill.execute(**shared_kwargs)
            if not success:
                print(f"Skill failed: {skill_name}")
                return False

        return True

    def statistics(self) -> Dict[str, Any]:
        """Get library statistics"""
        return {
            "total_skills": len(self.skills),
            "skills_by_success": sorted(
                [(s.name, s.success_rate) for s in self.skills.values()],
                key=lambda x: x[1],
                reverse=True
            )
        }


# Example usage
def create_example_library():
    library = SkillLibrary()

    # Define primitive skills
    library.add(Skill(
        name="move_forward",
        description="Move the agent forward by a specified distance",
        parameters={"distance": float},
        implementation=lambda distance: print(f"Moving forward {distance}") or True
    ))

    library.add(Skill(
        name="turn",
        description="Rotate the agent by specified degrees",
        parameters={"degrees": float},
        implementation=lambda degrees: print(f"Turning {degrees} degrees") or True
    ))

    library.add(Skill(
        name="pick_up",
        description="Pick up an object within reach",
        parameters={"object_name": str},
        implementation=lambda object_name: print(f"Picking up {object_name}") or True
    ))

    library.add(Skill(
        name="place",
        description="Place held object at current location",
        parameters={"surface": str},
        implementation=lambda surface: print(f"Placing on {surface}") or True
    ))

    # Composite skill using primitives
    def navigate_to_object(target: str):
        # In practice, this would use path planning
        library.get("turn").execute(degrees=45)
        library.get("move_forward").execute(distance=2.0)
        return True

    library.add(Skill(
        name="navigate_to_object",
        description="Navigate to a named object in the environment",
        parameters={"target": str},
        implementation=navigate_to_object
    ))

    return library


if __name__ == "__main__":
    library = create_example_library()

    # Semantic search
    print("\nSearching for 'go to something':")
    results = library.search("go to something")
    for skill in results:
        print(f"  - {skill.name}: {skill.description}")

    # Execute a skill
    print("\nExecuting pick_up:")
    library.get("pick_up").execute(object_name="red_cube")

    # Compose skills
    print("\nComposing skill sequence:")
    library.compose(["turn", "move_forward", "pick_up"],
                   degrees=90, distance=1.0, object_name="blue_ball")

    # Statistics
    print("\nLibrary statistics:")
    print(library.statistics())

def discover_skills_from_demos(demonstrations: List[List[str]], min_length=2, min_frequency=3):
    """
    Find recurring action sequences across demonstrations.

    Args:
        demonstrations: List of action sequences, e.g., [["move", "pick", "place"], ...]
        min_length: Minimum actions in a skill
        min_frequency: Minimum occurrences to be considered a skill

    Returns:
        List of discovered skill patterns
    """
    # TODO: Implement n-gram frequency analysis
    # Hint: Use sliding windows and count subsequence occurrences

    from collections import Counter

    subsequence_counts = Counter()

    # Your implementation here
    for demo in demonstrations:
        for length in range(min_length, len(demo) + 1):
            for start in range(len(demo) - length + 1):
                subseq = tuple(demo[start:start + length])
                subsequence_counts[subseq] += 1

    # Filter by minimum frequency
    skills = [
        seq for seq, count in subsequence_counts.items()
        if count >= min_frequency
    ]

    # Remove subsequences of longer skills
    # ...

    return skills

# Test data
demos = [
    ["navigate", "pick", "navigate", "place"],
    ["navigate", "pick", "navigate", "place", "navigate"],
    ["pick", "navigate", "place"],
    ["navigate", "pick", "navigate", "place", "rest"]
]

skills = discover_skills_from_demos(demos)
print("Discovered skills:", skills)