Scoring Proposal

class Bco:
    def __init__(self, contents):
        self.contents = contents
        self.score = 0

def bco_score(bco_instance: Bco) -> Bco:
    """BCO Score

    Process and score BioCompute Objects (BCOs).
    """
    contents = bco_instance.contents
    score = 0

    # Provenance Domain scoring
    prov_domain = contents.get("provenance_domain", {})
    if all(key in prov_domain for key in ["name", "version", "license", "derived_from", "created", "modified"]):
        score += 1
    if all(key in prov_domain for key in ["obsolete_date", "embargo"]):
        score += 1
    if len(prov_domain.get("contributors", [])) >= 3:
        score += 1
    if "review" in prov_domain:
        score += 1

    # Usability Domain scoring
    usability_domain = contents.get("usability_domain", [])
    if len(usability_domain) >= 3:
        score += 1

    # Description Domain scoring
    description_domain = contents.get("description_domain", {})
    if len(description_domain.get("keywords", [])) >= 3:
        score += 1
    if "platforms" in description_domain:
        # Assuming verification with repo is done externally
        score += 1

    # Extension Domain scoring
    extension_domain = contents.get("extension_domain", None)
    if extension_domain:
        score += 2

    # Parametric Domain scoring
    parametric_domain = contents.get("parametric_domain", {}).get("steps", [])
    if len(parametric_domain) >= 3:
        score += 1

    # I/O Domain scoring
    io_domain = contents.get("io_domain", {})
    input_subdomain = io_domain.get("input_subdomain", [])
    output_subdomain = io_domain.get("output_subdomain", [])
    score += min(len(input_subdomain), len(output_subdomain))

    # Execution Domain scoring
    exec_domain = contents.get("execution_domain", {})
    if "script_driver" in exec_domain:
        score += 1
    if "scripts" in exec_domain:
        score += sum(1 for script in exec_domain["scripts"] if "prerequisite" in script)
    if "external_data_endpoints" in exec_domain:
        score += len(exec_domain["external_data_endpoints"])

    # Error Domain scoring
    error_domain = contents.get("error_domain", {})
    if "algorithmic_error" in error_domain and "empirical_error" in error_domain:
        score += 2

    bco_instance.score = score
    return bco_instance

# Example usage:
bco_contents = {
    "provenance_domain": {
        "name": "example",
        "version": "1.0",
        "license": "MIT",
        "derived_from": "base",
        "created": "2021-01-01",
        "modified": "2021-06-01",
        "obsolete_date": "2023-01-01",
        "embargo": "2022-01-01",
        "contributors": ["Alice", "Bob", "Carol"],
        "review": "review_text"
    },
    "usability_domain": ["use1", "use2", "use3"],
    "description_domain": {
        "keywords": ["keyword1", "keyword2", "keyword3"],
        "platforms": ["platform1", "platform2"]
    },
    "extension_domain": {},
    "parametric_domain": {
        "steps": ["step1", "step2", "step3"]
    },
    "io_domain": {
        "input_subdomain": ["input1", "input2"],
        "output_subdomain": ["output1", "output2"]
    },
    "execution_domain": {
        "script_driver": "driver_script",
        "scripts": [{"prerequisite": "req1"}, {"prerequisite": "req2"}],
        "external_data_endpoints": ["endpoint1", "endpoint2"]
    },
    "error_domain": {
        "algorithmic_error": "alg_error",
        "empirical_error": "emp_error"
    }
}

bco_instance = Bco(bco_contents)
scored_bco = bco_score(bco_instance)
print(scored_bco.score)  # Output should be the total score based on the provided domains