import os
import sys
import shutil
import subprocess
from datetime import datetime
import requests
import re
import threading
import time
# =============================================================================
# Configuration
# If not hosted locally you can simply run this file as python code_helper_v6.py <file_path> <prompt>
# =============================================================================
OLLAMA_API_URL = "http://localhost:11434/api/generate"
MODEL_NAME = "qwen2.5-coder:3b"
# Disclaimer to be injected into C# files when the user requests it via the prompt add disclaimer
DISCLAIMER_TEXT = """/*
* -----------------------------------------------------------------------------
* © 2026 Skyline Cloud LLC. All rights reserved.
* NOTICE: This source code file contains structural modifications optimized via
* an internally hosted, private Qwen LLM instance. All data remains subject
* to strict corporate security and data sovereignty policies.
* SkylineCloud.AI is a proprietary tool and may not be redistributed or used outside of authorized environments.
* -----------------------------------------------------------------------------
*/"""
# =============================================================================
# Visual Spinner & External Prompt Loading
# architect_rules.txt will be auto-generated if missing, ensuring consistent LLM behavior across environments.
# Updates to the rules file will be reflected in subsequent LLM interactions without requiring code changes.
# =============================================================================
def show_spinner(stop_event):
spinner = ['⠋', '⠙', '⠹', '⠸', '⠼', '⠴', '⠦', '⠧', '⠇', '⠏']
idx = 0
while not stop_event.is_set():
sys.stdout.write(f"\r[INFO] Skyline LLM is thinking... {spinner[idx]} ")
sys.stdout.flush()
idx = (idx + 1) % len(spinner)
time.sleep(0.1)
sys.stdout.write("\r" + " "*50 + "\r") # Clears the line cleanly
def get_system_prompt(file_path):
# Determine the file extension to load language-specific rules
_, ext = os.path.splitext(file_path.lower())
script_dir = os.path.dirname(os.path.abspath(__file__))
# Define mapping of file extensions to target rule files and their initial default prompts
agent_configs = {
".cs": {
"filename": "architect_rules_cs.txt",
"default_prompt": (
"You are an expert C# refactoring assistant. Your job is to modify the provided C# code "
"according to the user's instructions. You MUST return ONLY the raw, updated C# source code. "
"Do NOT include any introduction, explanations, markdown code blocks (like ```csharp), or trailing text. "
"Preserve the existing structure, namespaces, and formatting of the class as much as possible."
)
},
".py": {
"filename": "architect_rules_py.txt",
"default_prompt": (
"You are an expert Python programming assistant. Your job is to modify the provided Python code "
"according to the user's instructions. You MUST return ONLY the raw, updated Python source code. "
"Do NOT include any introduction, explanations, markdown code blocks (like ```python), or trailing text. "
"Adhere to PEP 8 guidelines and preserve formatting as much as possible."
)
},
".sql": {
"filename": "architect_rules_sql.txt",
"default_prompt": (
"You are an expert SQL database assistant. Your job is to modify or generate database scripts "
"according to the user's instructions. You MUST return ONLY valid SQL commands. "
"Do NOT include markdown syntax (like ```sql), conversational introductions, or explanations. "
"Ensure syntax matches standard ANSI SQL or T-SQL conventions where applicable."
)
}
}
# Fallback for generic/unsupported file extensions
fallback_config = {
"filename": "architect_rules.txt",
"default_prompt": (
"You are an expert software development assistant. Your job is to modify or generate code "
"according to the user's instructions. You MUST return ONLY the raw, updated source code. "
"Do NOT include markdown syntax wrapping, introductions, or structural explanations."
)
}
# Select configuration based on extension
config = agent_configs.get(ext, fallback_config)
prompt_file = os.path.join(script_dir, config["filename"])
if os.path.exists(prompt_file):
with open(prompt_file, 'r', encoding='utf-8') as f:
return f.read().strip()
else:
# Auto-generate the appropriate configuration file if it's missing
with open(prompt_file, 'w', encoding='utf-8') as f:
f.write(config["default_prompt"])
print(f"[INFO] Created default agent rule configuration at '{config['filename']}'")
return config["default_prompt"]
# =============================================================================
# CORE EXECUTION
# Pass an existing file path and a prompt to modify it, or a new file path to scaffold a new C# file.
# =============================================================================
def modify_code(file_path, prompt):
file_exists = os.path.exists(file_path)
original_code = ""
# FEATURE 1: Handle File Generation vs Refactoring
if file_exists:
with open(file_path, 'r', encoding='utf-8') as f:
original_code = f.read()
try:
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
backup_path = f"{file_path}.{timestamp}.bak"
shutil.copy2(file_path, backup_path)
print(f"[INFO] Backup created: '{os.path.basename(backup_path)}'")
except Exception as e:
print(f"[WARN] Failed to create backup. Error: {e}")
return
# CONDITIONAL BYPASS: Local File Mutation (Only works if file exists)
if "add disclaimer" in prompt.lower():
print("[INFO] Shortcut matched: Injecting local corporate disclaimer...")
using_pattern = re.compile(r'((?:^using\s+[^;]+;\s*\r?\n)+)', re.MULTILINE)
match = using_pattern.search(original_code)
if match:
end_of_usings = match.end()
updated_code = original_code[:end_of_usings] + "\n" + DISCLAIMER_TEXT + "\n" + original_code[end_of_usings:]
else:
updated_code = DISCLAIMER_TEXT + "\n\n" + original_code
with open(file_path, 'w', encoding='utf-8') as f:
f.write(updated_code)
print(f"[SUCCESS] Disclaimer applied to {os.path.basename(file_path)} without LLM dependency.")
return
else:
print(f"[INFO] File does not exist. Initiating scaffolding for '{os.path.basename(file_path)}'...")
# Create an empty file so VS Code Diff viewer doesn't crash on the left side
os.makedirs(os.path.dirname(file_path) or '.', exist_ok=True)
open(file_path, 'a').close()
print(f"[NETWORK] Sending '{os.path.basename(file_path)}' to Skyline Cloud LLM...")
# Build Context-Aware Prompt (Now routing rules based on extension)
system_instruction = get_system_prompt(file_path)
if file_exists:
full_prompt = f"{system_instruction}\n\nOriginal Code:\n{original_code}\n\nInstruction:\n{prompt}\n\nUpdated Code:"
else:
full_prompt = f"{system_instruction}\n\nInstruction: Scaffold a completely new file based on the following request.\n\nRequest:\n{prompt}\n\nGenerated Code:"
# FEATURE 2: Expand Context Window (num_ctx)
payload = {
"model": MODEL_NAME,
"prompt": full_prompt,
"stream": False,
"options": {
"temperature": 0.2,
"num_ctx": 8192
}
}
# Execute Network Call with Threaded Spinner
stop_spinner = threading.Event()
spinner_thread = threading.Thread(target=show_spinner, args=(stop_spinner,))
spinner_thread.start()
try:
# FEATURE 3: Timeout Fail-Safe
response = requests.post(OLLAMA_API_URL, json=payload, timeout=120)
response.raise_for_status()
except requests.exceptions.Timeout:
stop_spinner.set()
print(f"\n[FATAL] The LLM timed out after 120 seconds. Aborting.")
return
except requests.exceptions.RequestException as e:
stop_spinner.set()
print(f"\n[FATAL] Error communicating with Ollama: {e}")
return
finally:
stop_spinner.set() # Guarantee the spinner stops no matter what happens
spinner_thread.join()
# Extract the updated code
updated_code = response.json().get("response", "").strip()
# Strip markdown block wrappers cleanly
if updated_code.startswith("```"):
lines = updated_code.splitlines()
if lines and lines[0].startswith("```"):
lines = lines[1:]
if lines and lines[-1].startswith("```"):
lines = lines[:-1]
updated_code = "\n".join(lines).strip()
if not updated_code:
print("[ERROR] Received empty response from the model.")
return
# Create temporary proposal
proposal_path = f"{file_path}.proposal"
with open(proposal_path, 'w', encoding='utf-8') as f:
f.write(updated_code)
# Launch Visual Diff
print(f"[INFO] Launching visual diff in VS Code...")
subprocess.run(["code", "--diff", file_path, proposal_path], shell=True)
# Await User Confirmation
print("\n" + "="*50)
user_decision = input(f"Accept LLM changes to {os.path.basename(file_path)}? (y/n): ").strip().lower()
print("="*50)
if user_decision == 'y':
shutil.move(proposal_path, file_path)
print(f"[SUCCESS] Changes committed to '{os.path.basename(file_path)}'.")
else:
os.remove(proposal_path)
# If we scaffolded an empty file and rejected it, clean up the empty file
if not file_exists:
os.remove(file_path)
print(f"[INFO] Changes discarded. Codebase remains untouched.")
if __name__ == "__main__":
if len(sys.argv) < 3:
print("Usage: ai <file_path> <prompt...>")
else:
target_file = sys.argv[1]
user_prompt = " ".join(sys.argv[2:])
modify_code(target_file, user_prompt)
Key Architectural Principles
Skyline Code Helper CLI (ai)
A lightweight, enterprise-grade, local LLM-powered command-line interface (CLI) for in-place code refactoring and scaffolding. Designed for complete data privacy and seamless developer ergonomics, this tool integrates directly into your terminal and pipes code changes straight into Visual Studio Code's native diff viewer for a review-first approval gate.
Built specifically for Skyline Cloud LLC development workflows.
🚀 Features
- Zero-Token Local Inference: Harnesses your local Ollama server running
qwen2.5-coder:3b (or larger weights) with zero API costs and absolute source code privacy.
- Visual Diff Integration: Automatically launches a split-screen side-by-side visual difference editor (
code --diff) in VS Code so you can inspect proposed updates before committing them.
- Dynamic Multi-Agent System Prompts: Reads the extension of your target file (e.g., .cs, .py, .sql) and automatically routes the request to language-specific system architectures.
- Smart Scaffolding: Auto-detects if a target file exists. If it does, a fail-safe backup (.bak) is created; if it doesn't, the tool enters scaffolding mode to write directory paths and generate your boilerplate from scratch.
- Performance & UX Upgrades: Smooth terminal feedback using a thread-safe CLI progress spinner, generous context window sizing (8192 tokens) for legacy classes, and a network timeout fail-safe at 120 seconds.
- Local Short-Circuits: Bypass the LLM entirely for high-frequency commands (like adding corporate licensing/disclaimers) using high-speed regex pattern matching.
🛠️ Architecture & Data Flow
| Execution Stage |
Process Details |
| 1. CLI Input |
User executes ai .\Path\To\File.cs "refactor method..." |
| 2. Python Wrapper |
Identifies extension and checks file existence. Backups target (if exists) or creates an empty scaffold block (if missing). |
| 3. Payload Assembly |
Loads target rules (CS/PY/SQL) and injects System Rules + Source File + Instruction into local payload. |
| 4. Local Inference |
Processes network request via local Ollama server on Port 11434. |
| 5. Diff Generation |
Saves output as File.cs.proposal and executes VS Code split-screen: code --diff File.cs File.cs.proposal |
| 6. Approval Gate |
Terminal prompts "Accept LLM Changes? (y/n)". Overwrites file on 'yes', or discards proposal & cleans up scaffolds on 'no'. |
📦 Prerequisites
- Ollama: Installed and running locally. Download from ollama.com.
- VS Code: Installed and added to your system PATH (the
code command must work in your terminal).
- Python 3.8+: With the requests library installed via
pip install requests.
⚙️ Installation & Global Execution Setup
To get a true "Copilot CLI" feel where you can type ai without specifying Python paths or wrapping your prompts in quotation marks, configure a system alias:
- Position the Files: Save
code_helper.py to a dedicated directory on your system (e.g., C:\SkylineTools\code_helper.py).
- Create the System Command: Create a batch file named
ai.bat inside that same folder containing:
@echo off
python "C:\SkylineTools\code_helper.py" %*
- Open System Settings: Open the Windows Start Menu, search for "Edit the system environment variables", and open it.
- Edit Environment Variables: Click "Environment Variables...". Under "User variables", select "Path" and click "Edit".
- Add Directory: Click "New" and add your tools folder path:
C:\SkylineTools.
- Apply & Restart: Click OK on all screens to apply, and restart any open terminals or VS Code windows.
💡 How It Works & Usage Examples
Open any shell terminal in any project workspace and call your code assistant natively:
Refactoring an Existing C# Class
ai .\Services\PaymentProcessor.cs refactor the ProcessTransaction method to be asynchronous and add XML comments
Creates a .bak backup, spins, sends the file payload to the local model combined with architect_rules_cs.txt, and loads the diff preview in VS Code.
Scaffolding a Brand New Python Script
ai .\Scripts\fetch_metrics.py generate a python script to query system CPU/Memory loads and save them as a CSV log
Identifies that the script doesn't exist, initializes an empty file to satisfy the split-screen viewer, loads architect_rules_py.txt, and generates the script.
Instant Bypass Shortcut
ai .\Controllers\OrderController.cs Add disclaimer
Instantly triggers a local regex pattern parser, identifies the last using declaration, and injects the Skyline Cloud LLC corporate copyright block without hitting the LLM network gateway.
🧠 Managing AI Agent Personas
The first time you run a query on an unsupported file extension, the script will automatically create the appropriate text-based agent configuration file next to code_helper.py:
architect_rules_cs.txt (Default C# Architect Rules)architect_rules_py.txt (Default Python Best-Practice Rules)architect_rules_sql.txt (Default Database/SQL Rules)architect_rules.txt (Default General Fallback rules)
To alter the architectural standards of your AI assistant (e.g., forcing camelCase, demanding specific test framework mockings, or standardizing logging frameworks), simply edit these .txt files. The rules are dynamically injected into the system prompts on every single run.
