KiroLinter

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  KiroLinter

Inspiration

The inspiration for KiroLinter came from the daily struggles of developers like myself who spend hours manually reviewing code for bugs, security vulnerabilities, and performance issues. As a solo developer or team member, I've often found myself bogged down in repetitive tasks—spotting unused variables, flagging potential SQL injections, or optimizing inefficient loops—that could be automated to free up time for more creative work. When I received my access code for Kiro, the AI-powered IDE featured in the Code with Kiro Hackathon, I saw it as the perfect tool to bridge that gap. Kiro's promise as a "development partner" inspired me to create something that not only accelerates code writing but transforms the entire workflow, from brainstorming to production. The hackathon's Productivity & Workflow Tools category was a natural fit, pushing me to explore how AI can make code reviews smarter, faster, and more personalized.

What it does

KiroLinter is an AI-driven code review tool built for Python projects, acting as your vigilant sidekick to catch issues early and suggest fixes. It analyzes Git repositories or local codebases for code smells (like unused variables or dead code), security vulnerabilities (such as SQL injection risks or hardcoded secrets), and performance bottlenecks (inefficient loops or redundant operations). Using AST parsing, it generates detailed JSON reports with severity levels, line-specific alerts, and smart suggestions—including diff patches for easy application. The tool integrates with GitHub to post review comments on pull requests automatically and learns from team commit history to provide personalized recommendations that align with your coding style. With a simple CLI interface, it's configurable for rules and exclusions, ensuring it fits seamlessly into any workflow—all powered by Kiro's AI for rapid, accurate insights.

How we built it

We built KiroLinter using Kiro's AI-powered IDE as our core development partner, starting with multi-modal chat sessions to brainstorm features and structure conversations around specs. From the initial prompt—"Help me build an AI code review tool for Python with GitHub integration"—Kiro generated a detailed requirements document, design architecture, and task list in the /.kiro directory. We leveraged spec-driven development to define modular components like the AST-based scanner for issue detection and the engine for analysis orchestration. Inline AI coding was key—highlighting stubs in the editor and asking Kiro to "implement AST parsing for unused variables" produced robust visitors in seconds. Agent hooks automated workflows, such as the on_commit_analysis hook for post-commit checks, with a bash script triggered on git events. We integrated libraries like Click for the CLI, GitPython for repo handling, and YAML for configuration, testing iteratively on the Flask repo to ensure performance under 5 minutes. The entire process was collaborative, with Kiro refining code and generating tests, culminating in an MIT-licensed, open-source tool.

Challenges we ran into

One major challenge was ensuring the JSON report output was structured correctly—in early tests, it defaulted to summary text instead of proper JSON, requiring a deep dive into the engine.py's generate_report method and a temporary fix in cli.py until Kiro could generate a dedicated json_reporter.py. The project structure initially lacked key files like setup.py and pyproject.toml, leading to "ModuleNotFoundError" during installation, which taught us Python packaging basics on the fly. Recreating the /.kiro directory after it went missing in some commits was a scare, as it’s crucial for hackathon compliance, but Kiro's regeneration feature saved the day. Performance optimization for large repos like Flask was tricky, with initial scans exceeding the 5-minute limit, so we implemented streaming in repository_handler.py and timeouts in the performance_tracker. Balancing Kiro's AI suggestions with manual refinements for accuracy, like fine-tuning AST visitors for hardcoded secrets, was a learning curve but ultimately rewarding.

Accomplishments that we're proud of

We're incredibly proud of creating a fully functional MVP in just one week, leveraging Kiro to automate what would have taken months manually. The AST-based scanner, capable of detecting 229 issues in 83 files under 1.23 seconds, stands out as a testament to Kiro's power—especially the inline AI that generated complex visitors for unused imports and SQL injection risks. Integrating agent hooks for seamless Git commit analysis, complete with a bash script that skips non-Python changes and handles timeouts, was a highlight, making the tool feel truly integrated into real workflows. We're also thrilled with the personalized suggestions and diff patches, which add real value beyond basic linters. Submitting an MIT-licensed, public repo with all /.kiro artifacts intact, and testing it on real projects like Flask, gives us confidence in its scalability. Above all, mastering Kiro's spec-driven approach turned a simple idea into a polished tool we're excited to share.

What we learned

Building KiroLinter was an eye-opener to the transformative role of AI in development. We learned how to structure effective conversations with Kiro to refine specs and generate code, turning vague prompts into modular architecture like the scanner's AST visitors. Diving into Python's AST module taught us precise code analysis techniques, while integrating GitPython and bash scripts highlighted automation's potential for CI/CD workflows. Debugging packaging errors (e.g., ModuleNotFoundError) deepened our understanding of Python environments, and optimizing for performance (e.g., 5-minute scans) emphasized efficient design. We also discovered the value of iterative testing on repos like Flask to catch edge cases early. Overall, Kiro showed us how AI can not just accelerate coding but foster better architecture decisions and cleaner code, skills we'll carry forward.

What's next for KiroLinter

Next for KiroLinter is expanding its capabilities to make it an indispensable developer tool. We'll integrate full OpenAI for AI-powered suggestions, enhancing the rule-based fallback with context-aware refactors. Adding CVE database integration for advanced security checks and team style learning from commit history will personalize fixes further. We plan to build a Flask-based web dashboard for visual reports and support for more languages beyond Python. GitHub Actions integration for CI pipelines and webhook support for real-time PR reviews are high on the list. Long-term, we're aiming for a community-driven rule set and marketplace for custom hooks, turning KiroLinter into an open ecosystem. With Kiro as our partner, we're excited to iterate based on user feedback and push the boundaries of AI-assisted coding.

Built With

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