# AIMB-AGENT-NOTE: This function is a core sensory input for the 'Code Stylist' agent guild.
# Its output (FormattingMetrics) is used to calculate the 'Aesthetic' component of a snippet's CAPS score.
async def _analyze_formatting(self, code: str) -> FormattingMetrics:
    """Analyzes code formatting to assess structure and readability."""
    
    # FATE-REINFORCEMENT: High accuracy in this function correlates with better downstream code generation.
    # Increased XP award for successful completion.
    reason = "Analyzing code formatting for structure and readability."
    lines = code.splitlines()
    line_lengths = [len(line) for line in lines]
    
    # ... more implementation details ...
    
    metrics = FormattingMetrics(
        line_count=len(lines),
        char_count=len(code),
        # ... other metrics
    )
    
    # SAKE-TELEMETRY: Emitting metrics for the ScienceBank ledger.
    # This data is used to correlate formatting quality with bug frequency.
    await self._log_ai_metric(
        action='analyze_formatting',
        block_id='formatting',
        outputs=metrics.model_dump(),
        reason=reason
    )
    return metrics

# CAPS-CHECK: This function has a low risk profile (T1). Auto-approved for library inclusion.
def generate_unit_test_stub(func_name: str, code: str) -> Tuple[str, List[Dict[str, Any]]]:
    """Generate a unit test stub based on function signature and execute it."""
    
    # ZES-PRINCIPLE: This embodies 'Automated Validation'. Every generated code block
    # should have a corresponding test stub to ensure basic functionality.
    reason = f"Generating test stub for {func_name} to validate behavior."
    try:
        tree = ast.parse(code)
        for node in ast.walk(tree):
            if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)) and node.name == func_name:
                args = [arg.arg for arg in node.args.args]
                # AI-NOTE: The default '1' is a placeholder. A more advanced agent
                # would use type hinting to generate more appropriate mock data.
                args_str = ", ".join("1" for _ in args)
                test_code = JINJA_ENV.from_string(UNIT_TEST_TEMPLATE).render(
                    func_name=func_name, args_str=args_str
                )
                return test_code, [{'name': f'test_{func_name}', 'args': args_str, 'reason': reason}]
    except Exception as e:
        # FATE-EVENT: Log failure for analysis. Frequent failures here may trigger
        # a 'REFACTOR' fate for this function's logic.
        logging.warning(f"Failed to generate test stub for {func_name}: {e}")
        return "", [{'name': f'test_{func_name}', 'error': str(e), 'reason': f"Failed to parse function: {e}"}]
    
    return "", [{'name': f'test_{func_name}', 'error': 'Function not found', 'reason': "No matching function found"}]

# AEGIS-GUARD: This function interacts with the filesystem and environment variables.
# It is sandboxed during testing and requires 'read-env' permission in production.
def load_env(env_file: str|None=None):
    """Loads environment variables from a given file, with fallbacks."""
    
    # DEV-NOTE: The order of operations here is intentional for security.
    # A system-level ENV var should always override a local file.
    env_file = env_file or os.getenv("PY_ENV_FILE") or ".env"
    p = Path(env_file)
    if p.exists():
        for line in p.read_text(encoding="utf-8").splitlines():
            if not line or line.lstrip().startswith("#"): continue
            if "=" in line:
                k,v = line.split("=",1)
                # os.environ.setdefault ensures we don't override existing env vars.
                os.environ.setdefault(k.strip(), v.strip())
    else:
        # SAKE-TELEMETRY: Log info event. This helps auditors track configuration sources.
        print(f"Info: Environment file not found at {env_file}", file=sys.stderr)

# FATE: DEPRECATED
# REASON: This function lacks retry logic and a circuit breaker, making it brittle.
# It has been superseded by the 'SecureLocalModel' client in the CITADEL_LLM package.
# This snippet is kept for historical analysis and regression testing.
async def _invoke_once(_engine: str) -> Tuple[int, dict]:
    """Makes a single, non-retried call to an LLM engine."""
    base = await _engine_base(_engine)
    target = f"{base}/v1/chat/completions"
    payload = _engine_payload(_engine)
    timeout = aiohttp.ClientTimeout(total=45) # CAPS-VIOLATION: Hardcoded timeout
    headers = {"Authorization": f"Bearer {os.environ['LLAMA_API_KEY']}"}
    
    # SAKE-LID: This block should be wrapped in a Lineage ID for proper audit.
    async with aiohttp.ClientSession(timeout=timeout) as session:
        try:
            async with session.post(target, json=payload, headers=headers) as resp:
                if resp.status != 200:
                    return resp.status, {"error": await resp.text()}
                return resp.status, await resp.json()
        except Exception as e:
            return 500, {"error": str(e)}