This is a submission for the Xano AI-Powered Backend Challenge: Production-Ready Public API

What I Built

The Problem That Drove Me to Build This

Let’s be honest - we all know commit messages matter. They’re the breadcrumbs that help us (and our teammates) understand what happened in our codebase. But here’s the reality: writing good commit messages is a hassle.

I found myself doing one of two things:

1. Committing once or twice a day with lazy messages like "fix stuff" or "updates".
2. Spending way too much time crafting the "perfect" commit message for every small change.

Neither approach was sustainable. I needed something that could understand my changes and write meaningful commits for me.

Overview of GitSense API

GitSense API is a production-ready developer toolkit that solves the commit message problem once and for all. It’s designed around a simple workflow:

1. Initialize once - Initialize your project with the API.
2. One command - Send your git status, get back intelligent commit suggestions.
3. Copy and execute - Use the generated git commands directly!

But it goes beyond just commit messages. The API also acts as your personal security guard, scanning for exposed secrets and vulnerabilities before they hit your repository.

The Data Service It Provides

1) Smart Commit Intelligence:

• Analyzes your changed files and categorizes them by purpose (documentation, configuration, features, tooling). • Generates semantic commit messages following conventional commit standards. • Provides ready-to-execute git commands grouped by logical features.

2) Security Vulnerability Detection: • Scans file contents for exposed API keys (AWS, GitHub, OpenAI, Stripe, etc.) • Detects hardcoded passwords and database connection strings. • Offers actionable remediation steps with specific commands.

3) Project Analytics: • Tracks commit patterns and repository analysis history. • Maintains project metadata and suggestion history. • Provides insights into your development workflow.

Real-World Impact

Before GitSense:

git add .
git commit -m "fix stuff"  # 😅 We've all been there

After GitSense:

# One API call gives you this:
git add HACKATHON_SUBMISSION.md
git commit -m 'feat: add hackathon submission documentation'

git add .vscode/settings.json
git commit -m 'chore: configure VS Code workspace settings'

git add docs/API.md
git commit -m 'docs: update project documentation'

Why This Matters

1) For Individual Developers: • Saves 15-20 minutes per day on commit message crafting. • Creates professional git history that impresses during code reviews. • Prevents security disasters before they happen.

2) For Development Teams: • Standardizes commit message format across the entire team. • Makes git history actually useful for debugging and feature tracking. • Reduces security vulnerabilities in shared repositories.

3) For Project Maintainers: • Enables automatic changelog generation from semantic commits. • Improves code review efficiency with descriptive commit msgs. • Provides audit trail for security compliance.

GitSense API aims to transform git commits from a necessary chore into an automated, intelligent process that actually adds value to your development workflow. It’s the difference between having a messy, uninformative git history and having a clean, professional repository that tells the story of your project’s evolution.

---

API Documentation

Base URL

GitSense API

Available Endpoints

• POST /analyze-project - Initializes project analysis. • POST /generate_smart_commits - Generates intelligent commit suggestions. • GET /project_lookup - Retrieves project status and history. • POST /Security_Analysis - Scans for security vulnerabilities. • POST /generate_commit_suggestion - Basic commit message generation.

1. Initialize Project Analysis

Endpoint: POST https://x8ki-letl-twmt.n7.xano.io/api:78VRpQ6j/analyze-project

# Navigate to your project directory
cd /path/to/your/project

# Register project with GitSense API
curl -X POST "https://x8ki-letl-twmt.n7.xano.io/api:78VRpQ6j/analyze-project" \
-H "Content-Type: application/json" \
-d "{\"repo_path\": \"$(pwd)\"}"

Expected Response:

{
"status": "created",
"project_id": 3,
"message": "New project created successfully.",
"repo_path": "/path/to/your/project"
}

2. Generate Smart Commit Messages

Endpoint: POST https://x8ki-letl-twmt.n7.xano.io/api:78VRpQ6j/generate_smart_commits

# From your project directory
cd /path/to/your/project

# Get intelligent commit suggestions (replace project_id with your actual ID)
curl -X POST "https://x8ki-letl-twmt.n7.xano.io/api:78VRpQ6j/generate_smart_commits" \
-H "Content-Type: application/json" \
-d "{\"project_id\": 3, \"git_status\": \"$(git status --porcelain)\"}"

Expected Response:

{
"suggested_commits": [
{
"files": [".vscode/settings.json"],
"git_add": "git add .vscode/settings.json",
"git_commit": "git commit -m 'chore: configure VS Code workspace settings'",
"feature": "development_environment",
"type": "tooling"
},
{
"files": ["README.md", "docs/API.md"],
"git_add": "git add README.md docs/API.md",
"git_commit": "git commit -m 'docs: update project documentation'",
"feature": "documentation",
"type": "documentation"
}
],
"total_groups": 2,
"total_files": 6,
"analysis": "Files categorized by feature and purpose"
}

3. Check Project Status

Endpoint: GET https://x8ki-letl-twmt.n7.xano.io/api:78VRpQ6j/project_lookup

# Get project details and history
curl -X GET "https://x8ki-letl-twmt.n7.xano.io/api:78VRpQ6j/project_lookup?repo_path=$(pwd)" \
-H "Content-Type: application/json"

Expected Response:

{
"project_id": 3,
"repo_path": "/path/to/your/project",
"last_analyzed": 1765775353612,
"commit_count": 0,
"recent_suggestions": [],
"status": "success"
}

4. Security Vulnerability Scan

Endpoint: POST https://x8ki-letl-twmt.n7.xano.io/api:78VRpQ6j/Security_Analysis

# Scan file contents for security issues
curl -X POST "https://x8ki-letl-twmt.n7.xano.io/api:78VRpQ6j/Security_Analysis" \
-H "Content-Type: application/json" \
-d "{\"project_id\": 3, \"file_contents\": \"$(cat .env 2>/dev/null || echo 'const apiKey = \"sk-test123\";')\"}"

Expected Response:

{
"security_issues": ["Database Connection URL", "Hardcoded Password/Secret"],
"risk_level": "medium",
"suggestions": [
"Remove or use environment variables for Database Connection URL",
"Remove or use environment variables for Hardcoded Password/Secret"
],
"total_issues": 2,
"scan_timestamp": "2025-12-15T05:09:55.688Z"
}

5. Basic Commit Suggestion

Endpoint: POST https://x8ki-letl-twmt.n7.xano.io/api:78VRpQ6j/generate_commit_suggestion

# Generate simple commit message based on file count
curl -X POST "https://x8ki-letl-twmt.n7.xano.io/api:78VRpQ6j/generate_commit_suggestion" \
-H "Content-Type: application/json" \
-d "{\"repo_path\": \"$(pwd)\", \"git_status\": \"$(git status --porcelain)\"}"

Complete Workflow Example

# 1. Navigate to your project
cd /path/to/your/project

# 2. Initialize with GitSense (save the project_id from response)
PROJECT_RESPONSE=$(curl -s -X POST "https://x8ki-letl-twmt.n7.xano.io/api:78VRpQ6j/analyze-project" \
-H "Content-Type: application/json" \
-d "{\"repo_path\": \"$(pwd)\"}")

echo "Project initialized: $PROJECT_RESPONSE"

# 3. Get smart commit suggestions (use your actual project_id)
curl -X POST "https://x8ki-letl-twmt.n7.xano.io/api:78VRpQ6j/generate_smart_commits" \
-H "Content-Type: application/json" \
-d "{\"project_id\": 3, \"git_status\": \"$(git status --porcelain)\"}"

# 4. Scan for security vulnerabilities
curl -X POST "https://x8ki-letl-twmt.n7.xano.io/api:78VRpQ6j/Security_Analysis" \
-H "Content-Type: application/json" \
-d "{\"project_id\": 3, \"file_contents\": \"$(cat .env 2>/dev/null || echo 'No sensitive files found')\"}"

All commands tested and verified working! ✅

---

Demo

API Calls & Responses

Base URL: GitSense API

Examples:-

1. Project Initialization

Request:

curl -X POST "https://x8ki-letl-twmt.n7.xano.io/api:78VRpQ6j/analyze-project" \
-H "Content-Type: application/json" \
-d '{"repo_path": "/home/user/my-awesome-project"}'

Response:

{
"status": "created",
"project_id": 3,
"message": "New project created successfully.",
"repo_path": "/home/user/my-awesome-project"
}

2. Smart Commit Generation (The Magic Happens Here)

Request:

curl -X POST "https://x8ki-letl-twmt.n7.xano.io/api:78VRpQ6j/generate_smart_commits" \
-H "Content-Type: application/json" \
-d '{
"project_id": 3,
"git_status": "M  src/components/Header.js\nA  docs/API.md\nM  .vscode/settings.json\nA  HACKATHON_SUBMISSION.md\nM  package.json"
}'

Response:

{
"suggested_commits": [
{
"files": ["HACKATHON_SUBMISSION.md"],
"git_add": "git add HACKATHON_SUBMISSION.md",
"git_commit": "git commit -m 'feat: add hackathon submission documentation'",
"feature": "project_submission",
"type": "feature"
},
{
"files": [".vscode/settings.json"],
"git_add": "git add .vscode/settings.json",
"git_commit": "git commit -m 'chore: configure VS Code workspace settings'",
"feature": "development_environment",
"type": "tooling"
},
{
"files": ["docs/API.md"],
"git_add": "git add docs/API.md",
"git_commit": "git commit -m 'docs: update project documentation'",
"feature": "documentation",
"type": "documentation"
}
],
"total_groups": 3,
"total_files": 5,
"analysis": "Files categorized by feature and purpose"
}

3. Security Vulnerability Detection

Request:

curl -X POST "https://x8ki-letl-twmt.n7.xano.io/api:78VRpQ6j/Security_Analysis" \
-H "Content-Type: application/json" \
-d '{
"project_id": 3,
"file_contents": "const openaiKey = \"sk-1234567890abcdef\"; const password = \"admin123\"; const dbUrl = \"mongodb://user:pass@localhost:27017/db\";"
}'

Response:

{
"security_issues": ["Database Connection URL", "Hardcoded Password/Secret"],
"risk_level": "medium",
"suggestions": [
"Remove or use environment variables for Database Connection URL",
"Remove or use environment variables for Hardcoded Password/Secret"
],
"total_issues": 2,
"scan_timestamp": "2025-12-15T05:09:55.688Z"
}

Before vs After Comparison

Traditional Workflow:

# What developers usually do 😅
git add .
git commit -m "fix stuff"
git commit -m "updates"
git commit -m "more changes"

GitSense-Powered Workflow:

# What GitSense enables ✨
git add HACKATHON_SUBMISSION.md
git commit -m 'feat: add hackathon submission documentation'

git add .vscode/settings.json
git commit -m 'chore: configure VS Code workspace settings'

git add docs/API.md
git commit -m 'docs: update project documentation'

Try It Yourself

# Quick test with your own project
cd /path/to/your/project
curl -X POST "https://x8ki-letl-twmt.n7.xano.io/api:78VRpQ6j/analyze-project" \
-H "Content-Type: application/json" \
-d "{\"repo_path\": \"$(pwd)\"}"

Note: Install jq for pretty JSON formatting: sudo apt install jq (Linux)

---

The AI Prompt I Used

Database Setup Prompts

1) Projects Table:

Create a projects table:
- id (primary key)
- repo_path (text, unique)
- last_analyzed (timestamp)
- commit_count (number, default 0)

When someone registers a project, store the full folder path. Update last_analyzed every time they use it.

2) Commit Suggestions Table:

Create commit_suggestions table:
- id (primary key)
- project_id (links to projects table)
- message (text)
- created_at (timestamp)

Store every commit message we generate so users can see their history.

Endpoint Prompts

1) Project Registration

Endpoint: POST /analyze-project
Input: repo_path

Check if this project exists. If yes, update the timestamp. If no, create new record.
Return the project_id and whether it was created or updated.

2) Smart Commit Generator

Endpoint: POST /generate_smart_commits
Input: project_id, git_status

Parse the git status text. Group files:
- .md files = docs
- .vscode, config files = chore
- HACKATHON/SUBMISSION files = feat
- everything else = misc

Generate proper commit messages like "docs: update README" or "chore: configure VS Code settings".
Return git add and git commit commands for each group.

3) Project Lookup

Endpoint: GET /project_lookup
Input: repo_path (query param)

Find project by path. If not found, return error saying "run analyze-project first".
If found, return project details + recent commit suggestions.

4) Security Scanner

Endpoint: POST /Security_Analysis
Input: project_id, file_contents

Scan text for:
- API keys (sk-, AKIA, ghp_, etc)
- passwords in code
- database URLs

Return list of issues found + suggestions like "move to .env file" or "add to .gitignore"

5) Basic Commit Helper

Endpoint: POST /generate_commit_suggestion
Input: repo_path, git_status

Count changed files. Generate simple message:
- 1 file: "Update filename"
- 2-3 files: "Update X files"
- 4+ files: "Major update: X files changed"

Return ready-to-use git commands.

Function Logic Prompts

1) File Categorization:

When parsing git status, look at filenames:
- Contains "spec", "design", "requirements" → specs
- Contains "HACKATHON", "SUBMISSION" → submissions
- Contains ".vscode", "settings.json" → vscode
- Ends with ".md" (but not submissions) → docs
- Everything else → config

Make separate commits for each category with proper prefixes.

2) Security Patterns:

Detect these patterns in file content:
- AWS keys: AKIA followed by 16 chars
- OpenAI keys: sk- followed by 48 chars
- GitHub tokens: ghp_ followed by 36 chars
- Passwords: password = "anything"
- Database URLs: mongodb://, mysql://, postgres://

For each match, suggest moving to environment variables.

3) Error Handling:

Handle these cases:
- Missing required inputs → return error message
- Project not found → suggest registering first
- Empty git status → return "no changes detected"
- Database errors → return generic error message

Always return proper JSON with clear error descriptions.

That’s it. Simple, direct prompts that got me exactly what I needed without overthinking it.

---

How I Refined the AI-Generated Code

How I Transformed the AI-Generated Backend in Xano

The AI Logic Assistant and Database Assistant gave me a solid foundation, but the generated code & functions had several issues that needed human refinement. The main problems were incorrect variable references, missing validation steps, & inefficient function flows.

I worked directly in Xano’s web interface to fix these issues, transforming basic AI logic into production-ready endpoints.

What I Refactored for Scalability, Security, and Maintainability

1. Fixed Variable Reference Errors

Before (AI-generated):

// AI created this with wrong variable names
var files = var:status_lines|split
return files.length  // This failed - wrong syntax

After (My fix):

// Fixed variable references and syntax
var status_lines = input:git_status|split
var files = []
// Proper variable handling with correct Xano syntax

2. Enhanced File Categorization Logic

Before (AI-generated):

// Basic categorization
if (file.includes('.js')) {
src.push(file);
} else if (file.includes('.md')) {
docs.push(file);
}

After (My refinement):

// Context-aware categorization I added
if (file.includes('spec') || file.includes('design') || file.includes('requirements') || file.includes('tasks')) {
specs.push(file);
} else if (file.includes('HACKATHON') || file.includes('SUBMISSION') || file.includes('KIROWEEN')) {
submissions.push(file);
} else if (file.includes('.vscode') || file.includes('settings.json')) {
vscode.push(file);
}

3. Added Missing Error Handling

Before (AI-generated):

// No validation - would crash on empty input
var project = this.runSQL("SELECT * FROM project WHERE repo_path = ?", [inputs.repo_path]);
return project[0].id;  // Fails if project doesn't exist

After (My improvement):

// Added proper validation and error handling
Conditional: var:existing_project!=null
2.1 Edit Record In project (if exists)
2.2 Create Variable: response_payload = success message
2.3 Add Record In project (if new)
2.4 Create Variable: response_payload = created message

4. Improved Security Pattern Detection

Before (AI-generated):

// Simple string matching
if (content.includes('password')) {
issues.push('Password found');
}

After (My enhancement):

// Comprehensive regex patterns I implemented
var patterns = {
'GITHUB_TOKEN': /ghp_[0-9a-zA-Z]{36}/g,
'GOOGLE_API_KEY': /AIza[0-9A-Za-z-_]{35}/g
};

5. Optimized Function Stack Flow

Before (AI-generated):

1. Query All Records From project
2. Create Variable (redundant step)
3. Query All Records From project (duplicate!)
4. Return response

After (My optimization):

1. Query All Records From project (single query)
2. Conditional check for existence
2.1 Update existing record path
2.2 Create new record path
3. Return structured response

Key Improvements I Made

1) Scalability Enhancements:

• Eliminated duplicate database queries that AI generated.
• Streamlined function stacks from 8+ steps to 4-5 essential steps.
• Optimized variable usage to reduce memory overhead.
• Added proper indexing on frequently queried fields (repo_path, project_id).

2) Security Hardening:

• Input validation for all parameters (AI missed this completely).
• SQL injection prevention through proper parameterized queries.
• Comprehensive pattern matching for 6+ types of API keys vs AI’s basic detection.
• Actionable security recommendations instead of generic warnings.

3) Maintainability Improvements:

• Consistent error response format across all endpoints.
• Clear variable naming (AI used confusing names like var:trimmed).
• Modular function design with single responsibility principle.
• Proper HTTP status codes for different scenarios.

Real Example: Smart Commits Endpoint Transformation

Before (AI Logic Assistant output):

Function Stack:
1. Create Variable: status_lines = input:git_status|split
2. Create Variable: files = []
3. For Each Loop On: var:status_lines
4. Create Variable: file_count = var:files|count
5. Create Variable: message = NA
6. Conditional: var:file_count==1
7. Add Record In: commit_suggestion
8. Create Variable: response = {...}

After (My refined version):

Function Stack:
1. Lambda Function with comprehensive JavaScript logic
- File categorization by context (hackathon, vscode, docs, specs)
- Semantic commit message generation
- Conventional commit format compliance
- Multiple commit grouping strategy
Return: Complete structured response with git commands

The Human Touch That Made the Difference

The AI gave me building blocks, but I had to:

1. Fix syntax errors and variable reference mistakes.
2. Add real-world context (hackathon files, VS Code settings).
3. Implement proper error handling for edge cases.
4. Optimize performance by removing redundant operations.
5. Enhance security with comprehensive pattern detection . The result is a production-ready API that handles real developer workflows, not just the few scenarios the AI initially generated.

---

My Experience with Xano

Overall Experience Using Xano

Working with Xano was like having a superpower for backend development. Coming from traditional backend setups where you spend hours configuring databases, setting up servers, and writing boilerplate code, Xano felt refreshingly different.

The platform strikes the perfect balance between simplicity and power. I could focus entirely on solving my git workflow problem instead of wrestling with infrastructure setup. Within mins of starting, I had a working API endpoint - something that would typically take hours with traditional approaches.

What I Found Most Helpful

1) The AI Assistants Were Game-Changers

The Logic Assistant and Database Assistant were absolutely incredible. Here’s what blew my mind:

1. They actually understood my prompts - I could describe what I wanted in plain English, and they’d create a structured plan.
2. The planning phase - Before executing anything, they’d show me exactly what they were going to build and ask for confirmation.
3. One-click execution - After approving the plan, watching them build the entire function stack automatically was magical.

Coolest moment: When I described my smart commit categorization logic, the Logic Assistant created a complete plan with JS implementation, then executed it flawlessly. It felt like pair programming with an expert developer.

2) Instant API Deployment

The deployment experience was phenomenal. No Docker containers, no server configuration, no environment variables to manage. I literally went from idea to live API in under an hour. The automatic API documentation & testing interface saved me countless hrs of setup time.

3) Visual Function Builder

Being able to see the data flow visually helped me understand exactly what was happening at each step. When debugging issues, I could trace through the function stack and immediately spot where things went wrong.

Challenges I Encountered

1) The AI Wasn’t Perfect (But That’s Okay)

The main challenge was that the AI assistants sometimes generated code with subtle errors - mainly incorrect variable references and missing validation steps.

The frustrating part: I spent over 10 attempts trying to get the AI to fix a variable reference error in my commit suggestion endpoint. It kept generating the same mistake despite my corrections.

The solution: I eventually gave up on the AI fix & manually corrected it through Xano’s web interface. Once I understood the pattern, fixing similar issues became straightforward.

2) Learning Xano’s Syntax

Coming from traditional programming, I had to learn Xano’s specific syntax for variables (var:variable_name) and function chaining. The documentation helped, but there was definitely a learning curve.

3) Complex Logic Limitations

For my security scanning feature, I needed complex regex pattern matching. The visual function builder wasn’t ideal for this, so I ended up using the Lambda function feature to write raw JS -which worked perfectly.

What Made Xano Special

• Speed of iteration: I could test ideas immediately. No build processes, no deployment pipelines - just instant feedback.
• Real-time collaboration: The built-in API testing meant I could verify endpoints worked correctly without external tools.
• Production readiness: Despite the rapid development, the resulting API was genuinely production-ready with proper error
• handling and performance.

The Bottom Line

Xano transformed what could have been a week-long backend project into a few hrs of focused problem-solving. The AI assistants handled the tedious parts, letting me focus on the creative aspects of building something genuinely useful.

Yes, I had to manually fix some AI-generated errors, but that’s part of the process. The AI got me 70% of the way there instantly, I just had to add the final 10% of human insight and refinement.

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Thank You, Xano Team

Thank you to the Xano team for creating such an incredible platform and for sponsoring this challenge. The AI assistants, visual function builder, and instant deployment capabilities made this project not just possible, but genuinely enjoyable to build.

Special appreciation for the Logic Assistant - even when it made mistakes, it taught me how to think about API design in a more structured way. This challenge pushed me to explore Xano’s full potential, and I’m excited to use it for future projects.

The combination of human creativity and AI assistance that Xano enables is exactly what the future of development should look like. 🚀