"""
S2 Decomposed: knowledge-transfer
==========================================

Auto-generated by S2 Auto-Decomposer.

Original skill: automation/knowledge-transfer
Scale structure:
  L0 (Micro): 9 operations
  L1 (Meso): 4 operations
  L2 (Macro): 2 operations
  L3 (Meta): 1 orchestrator

Model routing:
  L0 -> codegemma
  L1 -> qwen3
  L2 -> qwen3
  L3 -> deepseek_r1
"""

```python
from typing import Any, Dict, List, Optional
from skills.s2.context_stack import ScaleLevel
from skills.s2.composer import S2Composer

# L0 MICRO SKILLS
def micro_parse_input_text(input: str, **kwargs) -> Dict[str, Any]:
    """Extract essential information from user input text."""
    topic = ""
    context = ""
    details = ""
    
    # Simple keyword-based parsing for demonstration
    if "topic" in input.lower():
        topic = input.split("topic")[1].split("context")[0].strip()
    if "context" in input.lower():
        context = input.split("context")[1].split("details")[0].strip()
    if "details" in input.lower():
        details = input.split("details")[1].strip()
    
    return {
        "topic": topic,
        "context": context,
        "details": details,
        "raw_input": input
    }

def micro_identify_key_actors(input: str, **kwargs) -> Dict[str, Any]:
    """Identify key actors or roles from the input text."""
    actors = []
    
    # Look for common role keywords
    role_keywords = ["audience", "recipient", "team", "role", "user", "agent"]
    for keyword in role_keywords:
        if keyword in input.lower():
            actors.append(keyword)
    
    return {
        "identified_actors": actors,
        "raw_input": input
    }

def micro_retrieve_information(input: str, **kwargs) -> Dict[str, Any]:
    """Retrieve structured information from knowledge base."""
    # Simulate database lookup
    knowledge_base = {
        "database_maintenance": [
            {"step": "Backup database", "constraint": "Use encrypted storage"},
            {"step": "Update schema", "constraint": "Test in staging first"}
        ],
        "system_upgrade": [
            {"step": "Check dependencies", "constraint": "Ensure version compatibility"},
            {"step": "Deploy changes", "constraint": "Use blue-green deployment"}
        ]
    }
    
    # Extract topic from input
    topic = input.split("topic")[1].split("context")[0].strip() if "topic" in input else ""
    
    return {
        "retrieved_data": knowledge_base.get(topic, []),
        "topic": topic,
        "raw_input": input
    }

def micro_generate_text_summary(input: str, **kwargs) -> Dict[str, Any]:
    """Generate concise summary of procedure information."""
    summary = ""
    steps = []
    
    # Simulate summary generation
    if "steps" in input.lower():
        steps = [line.strip() for line in input.split("steps")[1].split("constraints")[0].split(".") if line.strip()]
    
    return {
        "summary_text": "Procedure Steps:\n" + "\n".join(steps) if steps else "No steps identified",
        "raw_input": input
    }

def micro_check_comprehension_level(input: str, **kwargs) -> Dict[str, Any]:
    """Check if text meets basic clarity criteria."""
    is_clear = False
    reasons = []
    
    # Basic clarity checks
    if "steps" in input.lower() and "constraints" in input.lower():
        is_clear = True
        reasons.append("Contains both steps and constraints")
    if len(input.split()) > 10:
        reasons.append("Sufficient detail provided")
    
    return {
        "is_clear": is_clear,
        "reasons": reasons,
        "raw_input": input
    }

# L1 MESO SKILLS
def meso_identify_transfer_target(data: Any, **kwargs) -> Dict[str, Any]:
    """Combine input parsing and actor identification."""
    micro_results = {
        "parse": micro_parse_input_text(data),
        "actors": micro_identify_key_actors(data)
    }
    
    return {
        "target_audience": micro_results["actors"]["identified_actors"],
        "procedure_topic": micro_results["parse"]["topic"],
        "context": micro_results["parse"]["context"],
        "raw_input": micro_results["parse"]["raw_input"]
    }

def meso_extract_procedure_components(data: Any, **kwargs) -> Dict[str, Any]:
    """Combine information retrieval and summary generation."""
    micro_results = {
        "retrieve": micro_retrieve_information(data),
        "summary": micro_generate_text_summary(data)
    }
    
    return {
        "procedure_steps": micro_results["retrieve"]["retrieved_data"],
        "summary_text": micro_results["summary"]["summary_text"],
        "topic": micro_results["retrieve"]["topic"],
        "raw_input": micro_results["retrieve"]["raw_input"]
    }

def meso_dist


# Skill metadata
SKILL_METADATA = {
    "original_id": "automation/knowledge-transfer",
    "original_name": "knowledge-transfer",
    "scale_structure": {
    "L0": [
        "parse_input_text: **: [brief description] Analyze the user input text to extract essential information about the knowledge being transferred.",
        "identify_key_actors: **: Identify potential target audience or recipient roles within a system context.",
        "retrieve_information: **: Access relevant data, facts, steps, constraints, and caveats related to the specific procedure from memory or other sources.",
        "generate_text_summary: **: Create concise textual summaries based on identified information patterns (steps, constraints).",
        "check_comprehension_level: **: Assess if a given text fragment meets basic clarity criteria for knowledge representation.",
        "identify_transfer_target: **: [combines: parse_input_text, identify_key_actors] Determine the specific target audience and define the primary goal of sharing knowledge.",
        "extract_procedure_components: **: [combines: retrieve_information, generate_text_summary] Systematically pull out all essential elements (steps, constraints, caveats) from a given procedure description or context.",
        "distill_and_handoff_knowledge: **: [combines: identify_transfer_target, extract_procedure_components] Assemble and deliver the core knowledge transfer brief using concise methods.",
        "detailed_verification_handoff: **: [combines: distill_and_handoff_knowledge OR multiple L1s?] Package complex information into a structured checklist for step-by-step verification."
    ],
    "L1": [
        "identify_transfer_target: **: [combines: parse_input_text, identify_key_actors] Determine the specific target audience and define the primary goal of sharing knowledge.",
        "extract_procedure_components: **: [combines: retrieve_information, generate_text_summary] Systematically pull out all essential elements (steps, constraints, caveats) from a given procedure description or context.",
        "distill_and_handoff_knowledge: **: [combines: identify_transfer_target, extract_procedure_components] Assemble and deliver the core knowledge transfer brief using concise methods.",
        "detailed_verification_handoff: **: [combines: distill_and_handoff_knowledge OR multiple L1s?] Package complex information into a structured checklist for step-by-step verification."
    ],
    "L2": [
        "distill_and_handoff_knowledge: **: [combines: identify_transfer_target, extract_procedure_components] Assemble and deliver the core knowledge transfer brief using concise methods.",
        "detailed_verification_handoff: **: [combines: distill_and_handoff_knowledge OR multiple L1s?] Package complex information into a structured checklist for step-by-step verification."
    ],
    "L3": [
        "automation_knowledge_transfer_orchestrator"
    ]
},
    "skill_tree": {
    "automation/knowledge_transfer/orchestrator": [
        "automation/knowledge_transfer/distill_and_handoff_knowledge",
        "automation/knowledge_transfer/detailed_verification_handoff"
    ],
    "automation/knowledge_transfer/distill_and_handoff_knowledge": [
        "automation/knowledge_transfer/parse_input_text",
        "automation/knowledge_transfer/identify_key_actors",
        "automation/knowledge_transfer/retrieve_information"
    ],
    "automation/knowledge_transfer/detailed_verification_handoff": [
        "automation/knowledge_transfer/parse_input_text",
        "automation/knowledge_transfer/identify_key_actors",
        "automation/knowledge_transfer/retrieve_information"
    ],
    "automation/knowledge_transfer/identify_transfer_target": [
        "automation/knowledge_transfer/parse_input_text",
        "automation/knowledge_transfer/identify_key_actors",
        "automation/knowledge_transfer/retrieve_information"
    ],
    "automation/knowledge_transfer/extract_procedure_components": [
        "automation/knowledge_transfer/parse_input_text",
        "automation/knowledge_transfer/identify_key_actors",
        "automation/knowledge_transfer/retrieve_information"
    ]
},
    "model_routing": {
    "L0": "codegemma",
    "L1": "qwen3",
    "L2": "qwen3",
    "L3": "deepseek_r1"
},
}
