Repository Standardization, Templates, and Lifecycle Governance: Best Practices for Polyglot Ecosystems¶

from prefect import flow, task
import duckdb

@task
def extract():
    """Extract data"""
    pass

@task
def transform(data):
    """Transform data"""
    pass

@task
def load(data):
    """Load data"""
    pass

@flow
def etl_flow():
    """Main ETL flow"""
    data = extract()
    transformed = transform(data)
    load(transformed)

# MLflow model versioning
import mlflow

mlflow.set_experiment("user-prediction")
with mlflow.start_run():
    mlflow.log_model(model, "model", registered_model_name="user-predictor")
    # Creates version 1, 2, 3, etc.

# Schema versioning
schema_version = "1.2.0"  # SemVer for schemas

# Migration versioning
migration_version = "002"  # Sequential

# scripts/generate_repo.py
import cookiecutter.main

def generate_repo(template_path: str, output_dir: str, context: dict):
    """Generate repository from template"""
    cookiecutter.main.cookiecutter(
        template_path,
        output_dir=output_dir,
        extra_context=context,
        no_input=True
    )

# scripts/generate_changelog.py
import subprocess
from datetime import datetime

class ChangelogGenerator:
    def generate(self, since_tag: str = None):
        """Generate CHANGES.md from git commits"""
        if since_tag:
            commits = subprocess.run(
                ["git", "log", f"{since_tag}..HEAD", "--pretty=format:%s"],
                capture_output=True,
                text=True
            ).stdout.split("\n")
        else:
            commits = subprocess.run(
                ["git", "log", "--pretty=format:%s"],
                capture_output=True,
                text=True
            ).stdout.split("\n")

        # Categorize commits
        changes = {
            "Added": [],
            "Changed": [],
            "Fixed": [],
            "Removed": []
        }

        for commit in commits:
            if commit.startswith("feat:"):
                changes["Added"].append(commit[5:].strip())
            elif commit.startswith("fix:"):
                changes["Fixed"].append(commit[4:].strip())
            elif commit.startswith("refactor:"):
                changes["Changed"].append(commit[8:].strip())

        # Generate markdown
        changelog = f"# Changelog\n\n## {datetime.now().strftime('%Y-%m-%d')}\n\n"
        for category, items in changes.items():
            if items:
                changelog += f"### {category}\n\n"
                for item in items:
                    changelog += f"- {item}\n"
                changelog += "\n"

        return changelog

# scripts/archive_repo.py
class RepoArchiver:
    def archive_repo(self, repo_name: str, reason: str):
        """Archive repository"""
        # 1. Add archival notice
        self.add_archival_notice(repo_name, reason)

        # 2. Move to archive org
        self.move_to_archive_org(repo_name)

        # 3. Update documentation
        self.update_documentation(repo_name)

        # 4. Notify dependents
        self.notify_dependents(repo_name)

# scripts/detect_stale_repos.py
class StaleRepoDetector:
    def detect_stale_repos(self, days_inactive: int = 180):
        """Detect stale repositories"""
        repos = self.get_all_repos()

        stale_repos = []
        for repo in repos:
            last_commit = self.get_last_commit_date(repo)
            days_since = (datetime.now() - last_commit).days

            if days_since > days_inactive:
                stale_repos.append({
                    "repo": repo,
                    "days_inactive": days_since,
                    "last_commit": last_commit
                })

        return stale_repos

# scripts/verify_artifacts.py
import hashlib
import json

class ArtifactVerifier:
    def verify_artifact(self, artifact_path: str, expected_hash: str) -> bool:
        """Verify artifact integrity"""
        with open(artifact_path, 'rb') as f:
            actual_hash = hashlib.sha256(f.read()).hexdigest()

        return actual_hash == expected_hash

    def generate_sbom(self, artifact_path: str) -> dict:
        """Generate SBOM for artifact"""
        import syft
        sbom = syft.scan(artifact_path, output="json")
        return json.loads(sbom)

# scripts/generate_dependency_graph.py
import networkx as nx
import matplotlib.pyplot as plt

class DependencyGraphGenerator:
    def generate_graph(self, repos: List[str]):
        """Generate dependency graph"""
        G = nx.DiGraph()

        for repo in repos:
            deps = self.get_dependencies(repo)
            G.add_node(repo)
            for dep in deps:
                G.add_edge(repo, dep)

        # Visualize
        pos = nx.spring_layout(G)
        nx.draw(G, pos, with_labels=True)
        plt.savefig("dependency_graph.png")

# scripts/detect_repo_drift.py
class RepoDriftDetector:
    def detect_drift(self, repo: str, template: str):
        """Detect drift from template"""
        repo_structure = self.get_repo_structure(repo)
        template_structure = self.get_template_structure(template)

        drift = {
            "missing_files": [],
            "extra_files": [],
            "modified_files": []
        }

        # Compare structures
        template_files = set(template_structure.keys())
        repo_files = set(repo_structure.keys())

        drift["missing_files"] = list(template_files - repo_files)
        drift["extra_files"] = list(repo_files - template_files)

        # Check modified files
        for file in template_files & repo_files:
            if template_structure[file] != repo_structure[file]:
                drift["modified_files"].append(file)

        return drift

# scripts/calculate_complexity.py
class ComplexityScorer:
    def calculate_complexity(self, repo: str) -> dict:
        """Calculate repository complexity"""
        # Cyclomatic complexity
        cyclomatic = self.calculate_cyclomatic_complexity(repo)

        # Lines of code
        sloc = self.count_sloc(repo)

        # Churn rate
        churn = self.calculate_churn(repo)

        # Dependency count
        deps = self.count_dependencies(repo)

        # Complexity score
        score = (
            cyclomatic * 0.3 +
            (sloc / 1000) * 0.2 +
            churn * 0.3 +
            (deps / 10) * 0.2
        )

        return {
            "cyclomatic": cyclomatic,
            "sloc": sloc,
            "churn": churn,
            "dependencies": deps,
            "score": score
        }

from nicegui import ui
from src.pages.dashboard import DashboardPage

@ui.page("/")
async def index():
    dashboard = DashboardPage()
    await dashboard.render()

if __name__ in {"__main__", "__mp_main__"}:
    ui.run(port=8080)

# llm/repo_generator.py
from openai import OpenAI

class LLMRepoGenerator:
    def __init__(self, client: OpenAI):
        self.client = client

    def generate_repo(self, description: str, language: str) -> dict:
        """Generate repository structure using LLM"""
        prompt = f"""
        Generate a standardized repository structure for:
        Description: {description}
        Language: {language}

        Include:
        1. Folder structure
        2. Key files (Makefile, Dockerfile, etc.)
        3. CI/CD configuration
        4. Pre-commit hooks
        """

        response = self.client.chat.completions.create(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are a repository architecture expert."},
                {"role": "user", "content": prompt}
            ]
        )

        return json.loads(response.choices[0].message.content)

# llm/template_populator.py
class LLMTemplatePopulator:
    def populate_template(self, template_path: str, context: dict) -> str:
        """Populate template with LLM-generated content"""
        # Read template
        with open(template_path) as f:
            template = f.read()

        # Generate content for placeholders
        for key, value in context.items():
            if key.startswith("llm_"):
                generated = self.llm_generate(value)
                template = template.replace(f"{{{{ {key} }}}}", generated)

        return template

# llm/version_bumper.py
class LLMVersionBumper:
    def suggest_version_bump(self, changes: List[str]) -> str:
        """Suggest version bump based on changes"""
        prompt = f"""
        Analyze these changes and suggest semantic version bump:

        Changes:
        {json.dumps(changes, indent=2)}

        Return: MAJOR, MINOR, or PATCH
        """

        response = self.client.chat.completions.create(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are a semantic versioning expert."},
                {"role": "user", "content": prompt}
            ]
        )

        return response.choices[0].message.content.strip()

# llm/dependency_upgrader.py
class LLMDependencyUpgrader:
    def generate_upgrade_pr(self, repo: str, dependency: str, new_version: str):
        """Generate PR for dependency upgrade"""
        # Analyze impact
        impact = self.analyze_impact(repo, dependency, new_version)

        # Generate PR description
        pr_description = self.llm_generate_pr_description(
            dependency, new_version, impact
        )

        # Create PR
        from github import Github
        g = Github(token)
        repo_obj = g.get_repo(repo)

        pr = repo_obj.create_pull(
            title=f"Upgrade {dependency} to {new_version}",
            body=pr_description,
            head="upgrade-dependency",
            base="main"
        )

        return pr

# llm/consistency_scanner.py
class LLMConsistencyScanner:
    def scan_consistency(self, repos: List[str]) -> dict:
        """Scan repositories for consistency"""
        # Get structures
        structures = {repo: self.get_structure(repo) for repo in repos}

        # Analyze with LLM
        prompt = f"""
        Analyze these repository structures for consistency:

        {json.dumps(structures, indent=2)}

        Identify:
        1. Inconsistencies
        2. Missing standard files
        3. Deviations from templates
        """

        response = self.client.chat.completions.create(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are a repository standardization expert."},
                {"role": "user", "content": prompt}
            ]
        )

        return json.loads(response.choices[0].message.content)

# llm/health_scorer.py
class LLMHealthScorer:
    def score_repo_health(self, repo: str) -> dict:
        """Score repository long-term health"""
        metrics = {
            "activity": self.get_activity(repo),
            "test_coverage": self.get_test_coverage(repo),
            "documentation": self.get_documentation_score(repo),
            "dependencies": self.get_dependency_health(repo),
            "complexity": self.get_complexity(repo)
        }

        # LLM analysis
        prompt = f"""
        Score repository health based on:

        {json.dumps(metrics, indent=2)}

        Provide:
        1. Overall health score (0-100)
        2. Risk factors
        3. Recommendations
        """

        response = self.client.chat.completions.create(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are a repository health expert."},
                {"role": "user", "content": prompt}
            ]
        )

        return json.loads(response.choices[0].message.content)

# llm/docs_ingester.py
class LLMDocsIngester:
    def ingest_to_mkdocs(self, repo: str, docs_path: str):
        """Ingest repository docs into MkDocs"""
        # Extract docs
        docs = self.extract_docs(repo, docs_path)

        # Generate MkDocs structure
        mkdocs_structure = self.llm_generate_mkdocs_structure(docs)

        # Write to MkDocs
        self.write_to_mkdocs(mkdocs_structure)