Top 10 Developer-Centric Code Snippet Managers and Customization Plugins that Will Dominate the Software Industry in 2026

Leveraging Snippet Managers for E-commerce Development Efficiency

In the fast-paced world of e-commerce development, where rapid iteration and consistent code quality are paramount, developer-centric code snippet managers and their associated customization plugins are no longer a luxury but a necessity. By 2026, these tools will be integral to maintaining a competitive edge, enabling teams to share, reuse, and standardize code components efficiently. This post dives into the top contenders and essential plugins that will define this landscape, focusing on practical implementation and advanced customization.

1. SnippetBox: The Integrated Powerhouse

SnippetBox stands out for its deep IDE integration and robust tagging system. Its strength lies in its ability to sync snippets across multiple IDEs and cloud storage, ensuring developers always have access to the code they need, regardless of their environment. The real game-changer for 2026 will be its advanced AI-powered suggestion engine, which learns from your project context to propose relevant snippets.

Customization Plugin: SnippetBox AI Enhancer

This plugin leverages local LLMs (e.g., Ollama with Llama 3) to provide context-aware snippet suggestions directly within the IDE. It analyzes the current file, surrounding code, and project structure to offer highly relevant code blocks.

Setup & Configuration (Conceptual):

Assume SnippetBox has an API for plugin development. The AI Enhancer would interact with a local Ollama instance.

# Conceptual Python script for the AI Enhancer plugin
import requests
import json
import os

SNIPPETBOX_API_URL = "http://localhost:5000/api/v1/snippets" # Hypothetical SnippetBox API
OLLAMA_API_URL = "http://localhost:11434/api/generate" # Ollama API endpoint

def get_current_file_context():
    # In a real plugin, this would fetch the active editor content and project path
    return {
        "content": "def calculate_total(price, quantity):\n    return price * quantity",
        "project_path": "/path/to/ecommerce/project"
    }

def suggest_snippets(context):
    prompt = f"""
    Given the following code context from a project at '{context['project_path']}':

    ```python
    {context['content']}
    ```

    Suggest relevant e-commerce code snippets (e.g., for cart manipulation, product display, payment processing).
    Format your suggestions as a JSON array of objects, each with 'title', 'description', and 'code' fields.
    Prioritize Python and PHP snippets.
    """

    payload = {
        "model": "llama3", # Or your preferred local model
        "prompt": prompt,
        "stream": False,
        "options": {
            "num_predict": 200
        }
    }

    try:
        response = requests.post(OLLAMA_API_URL, json=payload)
        response.raise_for_status()
        result = response.json()
        suggestions_text = result['response']

        # Attempt to parse JSON from the LLM output
        # LLMs can be finicky, so robust parsing is key
        try:
            suggestions = json.loads(suggestions_text)
            if isinstance(suggestions, list):
                return suggestions
            else:
                # Sometimes LLMs might wrap the JSON in markdown or other text
                # More sophisticated parsing might be needed here
                print(f"Warning: LLM output was not a direct JSON list. Raw output: {suggestions_text}")
                return []
        except json.JSONDecodeError:
            print(f"Error decoding JSON from LLM response: {suggestions_text}")
            return []

    except requests.exceptions.RequestException as e:
        print(f"Error communicating with Ollama API: {e}")
        return []

def integrate_with_snippetbox(suggestions):
    # This would involve calling SnippetBox's API to display suggestions
    print("Integrating suggestions with SnippetBox:")
    for suggestion in suggestions:
        print(f"- Title: {suggestion.get('title')}")
        print(f"  Description: {suggestion.get('description')}")
        print(f"  Code: {suggestion.get('code')[:100]}...") # Truncate for display
        # Hypothetical SnippetBox API call to display suggestion
        # requests.post(SNIPPETBOX_API_URL + "/suggest", json=suggestion)

if __name__ == "__main__":
    current_context = get_current_file_context()
    if current_context:
        suggested_snippets = suggest_snippets(current_context)
        integrate_with_snippetbox(suggested_snippets)
    else:
        print("Could not get current file context.")

2. Lepton: The Collaborative Hub

Lepton excels in team collaboration, offering features like shared snippet libraries, version control for snippets, and granular access permissions. Its 2026 iteration will likely focus on deeper integration with CI/CD pipelines for automated snippet validation and deployment.

Customization Plugin: Lepton CI/CD Validator

This plugin automatically triggers a lightweight linting and basic test suite for any new snippet added to a shared library. It ensures that shared code adheres to project standards before being widely adopted.

# Conceptual Git hook script for Lepton CI/CD Validator
# This would be triggered by Lepton's internal mechanisms or a pre-commit hook.

#!/bin/bash

# Assume snippets are stored in a specific directory and have a defined format (e.g., .snippet.php)
SNIPPET_DIR="~/lepton_snippets/shared"
LINT_COMMAND="php -l" # Example PHP linter
TEST_SCRIPT="./scripts/validate_snippet.sh" # A script to run basic tests

echo "Running Lepton CI/CD Validator..."

# Find new or modified snippets in the shared directory
find "$SNIPPET_DIR" -name "*.snippet.php" -mtime -1 -print0 | while IFS= read -r -d $'\0' snippet_file; do
    echo "Validating snippet: $snippet_file"

    # 1. Linting
    if ! $LINT_COMMAND "$snippet_file" &> /dev/null; then
        echo "ERROR: Linting failed for $snippet_file"
        exit 1
    fi

    # 2. Basic functional test (e.g., does it parse correctly, does it have expected structure)
    if ! "$TEST_SCRIPT" "$snippet_file"; then
        echo "ERROR: Basic test failed for $snippet_file"
        exit 1
    fi

    echo "Validation successful for $snippet_file"
done

echo "Lepton CI/CD Validator finished."
exit 0

3. Snipplr: The Developer’s Personal Assistant

Snipplr focuses on individual developer productivity. Its strength lies in its intuitive UI, powerful search capabilities, and seamless integration with popular IDEs. By 2026, expect enhanced natural language search and automated snippet generation from existing codebases.

Customization Plugin: Snipplr Auto-Generator

This plugin scans your current project for repetitive code patterns (e.g., common API call structures, form validation logic) and suggests creating new snippets from them. It uses AST (Abstract Syntax Tree) parsing for more accurate pattern detection.

<?php
// Conceptual PHP script for Snipplr Auto-Generator plugin
// This would run within the IDE's plugin framework.

require_once 'vendor/autoload.php'; // Assuming AST parser library is installed

use PhpParser\ParserFactory;
use PhpParser\Node\Stmt\Function_;
use PhpParser\Node\Expr\MethodCall;
use PhpParser\Node\Expr\Variable;
use PhpParser\Node\Identifier;

function scan_and_suggest_snippets($code) {
    $parser = (new ParserFactory)->create(ParserFactory::PREFER_PHP7);
    try {
        $ast = $parser->parse($code);
    } catch (\PhpParser\Error $e) {
        echo 'Parse Error: ' . $e->getMessage();
        return [];
    }

    $suggested_snippets = [];

    // Example: Detect common API call patterns (e.g., using Guzzle)
    $visitor = new class extends \PhpParser\NodeVisitorAbstract {
        public $apiCalls = [];

        public function enterNode(\PhpParser\Node $node) {
            if ($node instanceof MethodCall) {
                // Check if it's a method call on a variable named $client or $api
                if ($node->var instanceof Variable && in_array($node->var->name, ['client', 'api', '$client', '$api'])) {
                    // Check if the method name suggests an API interaction (e.g., 'get', 'post', 'request')
                    if ($node->name instanceof Identifier && in_array(strtolower($node->name->name), ['get', 'post', 'put', 'delete', 'request'])) {
                        // This is a simplified check. Real-world would involve more context.
                        $this->apiCalls[] = $node;
                    }
                }
            }
        }
    };

    $traverser = new \PhpParser\NodeTraverser;
    $traverser->addVisitor($visitor);
    $traverser->traverse($ast);

    if (!empty($visitor->apiCalls)) {
        foreach ($visitor->apiCalls as $apiCallNode) {
            // Extract the relevant code block for the snippet
            // This requires careful AST manipulation or pretty-printing specific nodes
            $snippet_code = "' . $apiCallNode->name . '(\'...\');' . "\n"; // Placeholder for arguments

            $suggested_snippets[] = [
                'title' => 'Common API Call (' . $apiCallNode->name . ')',
                'description' => 'A common pattern for making API requests.',
                'code' => $snippet_code,
                'tags' => ['api', 'http', strtolower($apiCallNode->name->name)]
            ];
        }
    }

    return $suggested_snippets;
}

// Example usage within an IDE plugin context:
// $current_file_content = file_get_contents($ide->getActiveEditor()->getFilePath());
// $suggestions = scan_and_suggest_snippets($current_file_content);
// $ide->showSuggestions($suggestions);

// Dummy execution for demonstration:
$sample_code = <<<PHP
get('https://api.example.com/data');
    return \$response;
}

\$data = makeRequest();
PHP;

$suggestions = scan_and_suggest_snippets($sample_code);
print_r($suggestions);

?>

4. CodeKeeper: Security and Compliance Focused

For e-commerce, security is non-negotiable. CodeKeeper specializes in managing sensitive code snippets, such as API keys, database credentials, and encryption routines. Its 2026 features will include automated vulnerability scanning of snippets and integration with secrets management platforms.

Customization Plugin: CodeKeeper Vulnerability Scanner

This plugin integrates with static analysis security testing (SAST) tools like SonarQube or custom regex patterns to scan snippets for common security flaws (e.g., hardcoded secrets, SQL injection vulnerabilities) before they are saved or shared.

# Conceptual script for CodeKeeper Vulnerability Scanner
# This could be a standalone script or integrated via hooks.

SNIPPET_FILE="/path/to/snippet.php" # The snippet file being processed
SONARQUBE_API_URL="http://localhost:9000/api/issues/search"
SONARQUBE_TOKEN="your_sonarqube_token"

# --- Custom Regex Checks ---
# Example: Detect potential hardcoded secrets (simplified)
SECRET_REGEX='(password|secret|api_key)\s*[:=]\s*["'\''][^"'\'' ]+["'\'']'

echo "Scanning snippet: $SNIPPET_FILE"

# 1. Custom Regex Scan
if grep -E -i "$SECRET_REGEX" "$SNIPPET_FILE"; then
    echo "SECURITY WARNING: Potential hardcoded secret detected in $SNIPPET_FILE."
    # In a real scenario, this would trigger an alert or prevent saving.
fi

# 2. Integration with SonarQube (Conceptual)
# This would involve uploading the snippet to a temporary SonarQube project
# or using an API endpoint that accepts code snippets directly for analysis.

# Example: Using SonarQube API to search for issues related to a specific file/project
# This is a simplified representation. Actual integration might require more complex API calls.
# curl -u "$SONARQUBE_TOKEN": "$SONARQUBE_API_URL?componentKey=your_project_key&types=VULNERABILITY,SECURITY_HOTSPOT&branch=main"

echo "Custom scan complete. Further integration with SAST tools like SonarQube would occur here."
# exit 1 # Uncomment to fail the operation if vulnerabilities are found

5. SnippetSync: Cloud-Native Synchronization

SnippetSync focuses on seamless cloud synchronization across devices and platforms. Its 2026 roadmap emphasizes real-time collaboration and offline access with intelligent conflict resolution, making it ideal for distributed e-commerce teams.

Customization Plugin: SnippetSync Conflict Resolver

This plugin provides advanced strategies for merging conflicting snippet edits, going beyond simple “last write wins.” It can offer options like semantic merging for code blocks or prompt the user with a diff view.

# Conceptual logic for SnippetSync Conflict Resolver
# This would be triggered when SnippetSync detects a merge conflict.

LOCAL_SNIPPET_PATH="/path/to/local/snippet.js"
REMOTE_SNIPPET_PATH="/path/to/remote/snippet.js" # Represents the incoming change
MERGED_SNIPPET_PATH="/path/to/merged/snippet.js" # Output path

echo "Resolving conflict for snippet: $LOCAL_SNIPPET_PATH"

# Strategy 1: Semantic Merging (Conceptual - requires AST or similar)
# If both snippets are valid JavaScript and represent similar structures,
# attempt to merge them intelligently. This is highly complex.
# Example: If one snippet adds a function and another modifies it, try to combine.

# Strategy 2: User-Prompted Diff Resolution
echo "Displaying diff view for user resolution..."
# Use a diff tool (like 'diff' command or a GUI diff viewer)
# diff -u "$LOCAL_SNIPPET_PATH" "$REMOTE_SNIPPET_PATH"

# Prompt user for resolution
read -p "Enter 'l' for local version, 'r' for remote version, or 'm' to manually edit and merge: " choice

case "$choice" in
    l|L)
        cp "$LOCAL_SNIPPET_PATH" "$MERGED_SNIPPET_PATH"
        echo "Selected local version."
        ;;
    r|R)
        cp "$REMOTE_SNIPPET_PATH" "$MERGED_SNIPPET_PATH"
        echo "Selected remote version."
        ;;
    m|M)
        echo "Opening manual merge editor..."
        # Launch a text editor or a specialized merge tool
        # vimdiff "$LOCAL_SNIPPET_PATH" "$REMOTE_SNIPPET_PATH"
        # After manual merge, the user would save the result to $MERGED_SNIPPET_PATH
        echo "Please save your merged result to $MERGED_SNIPPET_PATH"
        ;;
    *)
        echo "Invalid choice. Conflict not resolved automatically."
        exit 1
        ;;
esac

echo "Conflict resolution process for $LOCAL_SNIPPET_PATH completed."
# In a real system, SnippetSync would then use $MERGED_SNIPPET_PATH

6. SnippetHub: Open Source and Extensible

SnippetHub champions the open-source philosophy, offering a highly customizable and extensible platform. Its 2026 focus will be on a robust plugin marketplace and community-driven feature development, making it adaptable to any e-commerce stack.

Customization Plugin: SnippetHub Language Pack

This plugin allows users to define custom language modes and syntax highlighting for niche or proprietary templating languages used in specific e-commerce platforms (e.g., Shopify Liquid, Magento PHTML). It leverages existing TextMate grammar definitions.

{
  "name": "Shopify Liquid Language Pack",
  "version": "1.0.0",
  "description": "Adds syntax highlighting for Shopify Liquid templates.",
  "engines": {
    "snippetHub": ">=2.0.0"
  },
  "contributes": {
    "languages": [{
      "id": "liquid",
      "aliases": ["Liquid", "shopify-liquid"],
      "extensions": [".liquid"],
      "configuration": "./language-configuration.json"
    }],
    "grammars": [{
      "language": "liquid",
      "scopeName": "text.html.liquid",
      "path": "./syntaxes/liquid.tmLanguage.json"
    }]
  },
  "scripts": {
    "postinstall": "node ./scripts/install-grammar.js"
  }
}

// language-configuration.json (Conceptual)
{
  "comments": {
    "lineComment": "{# ... #}"
  },
  "brackets": [
    ["{{", "}}"],
    ["{%", "%}"],
    ["{#", "#}"]
  ],
  "autoClosingPairs": [
    ["{{", "}}"],
    ["{%", "%}"],
    ["{#", "#}"]
  ]
}

// syntaxes/liquid.tmLanguage.json (Simplified TextMate Grammar)
{
  "scopeName": "text.html.liquid",
  "patterns": [
    { "include": "#html" },
    { "include": "#liquid-tags" },
    { "include": "#liquid-variables" },
    { "include": "#liquid-comments" }
  ],
  "repository": {
    "html": {
      "patterns": [
        { "include": "text.html.basic" }
      ]
    },
    "liquid-tags": {
      "begin": "(\\{%)(?!\\s*#)",
      "end": "(\\%\\})",
      "name": "keyword.control.liquid",
      "patterns": [
        { "include": "#liquid-tag-content" }
      ]
    },
    "liquid-variables": {
      "begin": "(\\{\\{)(?!\\s*#)",
      "end": "(\\{\\})",
      "name": "variable.language.liquid",
      "patterns": [
        { "include": "#liquid-variable-content" }
      ]
    },
    "liquid-comments": {
      "begin": "(\\{#)",
      "end": "(#\\})",
      "name": "comment.block.liquid"
    },
    "liquid-tag-content": {
      "patterns": [
        { "match": "\\b(if|unless|for|else|break|continue|render|section|assign|capture)\\b", "name": "keyword.control.liquid" },
        { "match": "[a-zA-Z0-9_.-]+", "name": "entity.name.tag.liquid" },
        { "match": "\\S+", "name": "invalid.illegal.liquid" }
      ]
    },
    "liquid-variable-content": {
      "patterns": [
        { "match": "[a-zA-Z0-9_.-]+", "name": "variable.parameter.liquid" },
        { "match": "\\|\\s*[a-zA-Z0-9_.-]+", "name": "keyword.operator.filter.liquid" },
        { "match": "\\S+", "name": "invalid.illegal.liquid" }
      ]
    }
  }
}

7. Snippetify: AI-Powered Code Generation

Snippetify distinguishes itself with advanced AI capabilities, aiming to generate code snippets from natural language descriptions or even rough sketches. By 2026, it will offer sophisticated code completion and refactoring suggestions powered by large language models trained on e-commerce specific code.

Customization Plugin: Snippetify E-commerce Domain Adapter

This plugin fine-tunes Snippetify’s underlying LLM on a curated dataset of e-commerce code (e.g., WooCommerce, Magento, custom PHP frameworks) to improve the relevance and accuracy of generated snippets for product management, order processing, and payment gateways.

# Conceptual Python script for fine-tuning an LLM for e-commerce snippets
# This is a high-level overview of the process.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
from datasets import Dataset

# --- Configuration ---
BASE_MODEL = "meta-llama/Llama-2-7b-hf" # Example base model
FINE_TUNED_MODEL_DIR = "./ecommerce-llama-tuned"
TRAINING_DATA_PATH = "./ecommerce_snippets.jsonl" # JSON Lines format: {"text": "..."}

# --- Load Dataset ---
def load_ecommerce_data(path):
    data = []
    with open(path, 'r') as f:
        for line in f:
            data.append(json.loads(line))
    return Dataset.from_list(data)

# --- Tokenization ---
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
# Add padding token if it doesn't exist
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

def tokenize_function(examples):
    # Tokenize text, ensuring padding and truncation are handled
    return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=512)

# --- Model Loading ---
model = AutoModelForCausalLM.from_pretrained(BASE_MODEL, torch_dtype=torch.float16)

# --- Training Arguments ---
training_args = TrainingArguments(
    output_dir=FINE_TUNED_MODEL_DIR,
    num_train_epochs=3,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=2,
    learning_rate=2e-5,
    logging_dir='./logs',
    logging_steps=10,
    save_steps=500,
    fp16=True, # Use mixed precision for faster training
    report_to="tensorboard",
)

# --- Trainer Setup ---
ecommerce_dataset = load_ecommerce_data(TRAINING_DATA_PATH)
tokenized_dataset = ecommerce_dataset.map(tokenize_function, batched=True)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset,
    tokenizer=tokenizer,
)

# --- Fine-tuning ---
print("Starting fine-tuning process...")
trainer.train()

# --- Save Model ---
print(f"Saving fine-tuned model to {FINE_TUNED_MODEL_DIR}")
trainer.save_model(FINE_TUNED_MODEL_DIR)
tokenizer.save_pretrained(FINE_TUNED_MODEL_DIR)

print("Fine-tuning complete.")

# --- Usage Example (after fine-tuning) ---
# loaded_model = AutoModelForCausalLM.from_pretrained(FINE_TUNED_MODEL_DIR)
# loaded_tokenizer = AutoTokenizer.from_pretrained(FINE_TUNED_MODEL_DIR)
# prompt = "Generate a PHP snippet to add an item to a WooCommerce cart."
# inputs = loaded_tokenizer(prompt, return_tensors="pt")
# outputs = loaded_model.generate(**inputs, max_length=200)
# print(loaded_tokenizer.decode(outputs[0], skip_special_tokens=True))

8. SnippetVault: Enterprise-Grade Security

SnippetVault is designed for large enterprises, focusing on robust security, auditing, and compliance features. Its 2026 enhancements will include integration with SIEM systems and advanced access control policies based on project roles.

Customization Plugin: SnippetVault Audit Trail Exporter

This plugin allows for the export of detailed audit logs of snippet usage, creation, and modification in a format suitable for compliance reporting (e.g., CSV, JSON) and integration with Security Information and Event Management (SIEM) systems.

#!/bin/bash
# Conceptual script to export SnippetVault audit logs

AUDIT_LOG_PATH="/var/log/snippetvault/audit.log" # Assume this is the raw log file
EXPORT_FORMAT="csv" # or "json"
OUTPUT_FILE="snippetvault_audit_report_$(date +%Y%m%d_%H%M%S).${EXPORT_FORMAT}"

echo "Exporting SnippetVault audit logs..."

if [ ! -f "$AUDIT_LOG_PATH" ]; then
    echo "ERROR: Audit log file not found at $AUDIT_LOG_PATH"
    exit 1
fi

# --- Log Parsing and Formatting ---
# Assume log lines are in a structured format, e.g.:
# TIMESTAMP USER ACTION TARGET DETAILS
# 2023-10-27T10:30:00Z admin CREATE snippet_id_123 {"name": "get_product_by_sku"}

if [ "$EXPORT_FORMAT" == "csv" ]; then
    echo "Timestamp,User,Action,Target,Details" > "$OUTPUT_FILE"
    # Use awk or similar to parse and format
    awk -F' ' '
    {
        timestamp = $1 " " $2;
        user = $3;
        action = $4;
        target = $5;
        # Reconstruct details field, assuming it starts after the target and is quoted JSON
        details = "";
        in_details = 0;
        for (i = 6; i <= NF; i++) {
            if ($i ~ /^\{/) in_details = 1;
            if (in_details) {
                details = details ($i ~ /\}$/ ? substr($i, 1, length($i)-1) : $i) (i==NF ? "" : " ");
            }
        }
        gsub(/"/, "", details); # Remove quotes from details for simplicity in CSV
        print timestamp "," user "," action "," target "," details
    }
    ' "$AUDIT_LOG_PATH" >> "$OUTPUT_FILE"
elif [ "$EXPORT_FORMAT" == "json" ]; then
    # Convert log lines to JSON objects and output as a JSON array
    echo "[" > "$OUTPUT_FILE"
    first_line=1
    awk -F' ' '
    {
        timestamp = $1 " " $2;
        user = $3;
        action = $4;
        target = $5;
        details = "";
        in_details = 0;
        for (i = 6; i <= NF; i++) {
            if ($i ~ /^\{/) in_details = 1;
            if (in_details) {
                details = details ($i ~ /\}$/ ? substr($i, 1, length($i)-1) : $i) (i==NF ? "" : " ");
            }
        }
        # Format as JSON object
        if (first_line) first_line=0; else printf ",\n";
        printf "  {\"timestamp\": \"%s\", \"user\": \"%s\", \"action\": \"%s\", \"target\": \"%s\", \"details\": \"%s\"}", timestamp, user, action, target, details
    }
    ' "$AUDIT_LOG_PATH" >> "$OUTPUT_FILE"
    echo "" >> "$OUTPUT_FILE" # Newline after last JSON object
    echo "]" >> "$OUTPUT_FILE"
else
    echo "ERROR: Unsupported export format: $EXPORT_FORMAT"
    exit 1
fi

echo "Audit log export complete. Report saved to: $OUTPUT_FILE"

# --- SIEM Integration (Conceptual) ---
# This script could be extended to send the generated file to a SIEM endpoint
# using curl or a dedicated agent.
# e.g., curl -X POST -T "$OUTPUT_FILE" http://siem.example.com/api/ingest

9. SnippetFlow: Workflow Automation

SnippetFlow focuses on integrating code snippets into broader development workflows. By 2026, it will offer advanced automation for tasks like generating boilerplate code for new microservices, setting up testing environments, or deploying common e-commerce features.

Customization Plugin: SnippetFlow Microservice Boilerplate Generator

This plugin uses a set of predefined snippets (e.g., Dockerfile, basic API endpoint structure, configuration files) to quickly scaffold new microservices based on user-defined parameters (language, framework, database). It automates the initial setup, saving significant developer time.

#!/bin/bash
# Conceptual script for SnippetFlow Microservice Boilerplate Generator

SERVICE_NAME="user-service"
LANGUAGE="python"
FRAMEWORK="flask"
DATABASE="postgresql"
OUTPUT_DIR="./services/${SERVICE_NAME}"

# --- Snippet Definitions (Conceptual) ---
# In a real system, these would be actual files managed by SnippetFlow
declare -A SNIPPETS
SNIPPETS[python_flask_dockerfile]="FROM python:3.9-slim\nWORKDIR /app\nCOPY requirements.txt .\nRUN pip install --no-cache-dir -r requirements.txt\nCOPY . .\nCMD [\"python\", \"app.py\"]"
SNIPPETS[python_flask_app]="from flask import Flask\n\napp = Flask(__name__)\n\[email protected]('/')\ndef hello_world():\n    return 'Hello, {SERVICE_NAME}!'\n\nif __name__ == '__main__':\n    app.run(debug=True, host='0.0.0.0')\n"
SNIPPETS[python_flask_requirements]="Flask\npsycopg2-binary # For PostgreSQL"
SNIPPETS[python_flask_docker_compose]="version: '3.8'\nservices:\n  web:\n    build: .\n    ports:\n      - \"5000:5000\"\n  db:\n    image: postgres:13\n    environment:\n      POSTGRES_DB: ${SERVICE_NAME}_db\n      POSTGRES_USER: user\n      POSTGRES_PASSWORD: password\n"

echo "Generating boilerplate for microservice: ${SERVICE_NAME}"

# Create output directory
mkdir -p "${OUTPUT_DIR}"
cd "${OUTPUT_DIR}" || exit 1

# Generate files based on selected language/framework/db
case "${LANGUAGE}-${FRAMEWORK}" in
    python-flask)
        # Dockerfile
        echo -e "${SNIPPETS[python_flask_dockerfile]}" | sed "s/{SERVICE_NAME}/${SERVICE_NAME}/g" > Dockerfile

        # app.py
        echo -e "${SNIPPETS[python_flask_app]}" | sed "s/{SERVICE_NAME}/${SERVICE_NAME}/g" > app.py

        # requirements.txt
        echo -e "${SNIPPETS[python_flask_requirements]}" | sed "