Codacy
Static analysis and quality gates for engineering teams.
Shippie
AI-powered code reviewer that speeds up CI/CD pipelines
Shippie integrates large language models into your CI/CD workflow to automatically detect secrets, inefficiencies, bugs, and edge cases, offering both CLI and GitHub Action reviews.
Overview
Overview
Shippie is designed for development teams that want automated, intelligent code quality checks baked directly into their CI/CD pipelines. By leveraging large language models, it acts like a human reviewer, spotting exposed credentials, performance regressions, and subtle bugs before code reaches production.
Capabilities & Deployment
Run Shippie locally with a simple npx shippie review command to evaluate staged files, or configure it as a GitHub Action for continuous integration. Custom rule files let you tailor the checks to your project's standards, while the Model Context Protocol (MCP) enables integration with external tools such as observability platforms. Installation works with bun, npm, or pnpm, and the tool operates under an MIT license.
Getting Started
After cloning the repository, install dependencies, add your OpenAI (or compatible) API key, and start the service. Shippie fits naturally into existing workflows, providing fast, AI‑driven feedback without replacing traditional linting tools.
Highlights
Detects exposed secrets and credentials
Identifies performance bottlenecks and inefficient code patterns
Catches potential bugs and unhandled edge cases
Runs as a CLI tool or GitHub Action with configurable rule files
Pros
- Seamless CI/CD integration
- Leverages LLMs for nuanced code analysis
- Customizable rule sets
- Fast local execution via bun/npm
Considerations
- Depends on external AI provider API keys
- Potential latency for large codebases
- Requires internet connectivity for LLM calls
- Limited to languages supported by the model
Managed products teams compare with
When teams consider Shippie, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Teams seeking automated code quality checks in CI pipelines
- Developers who want instant feedback on staged changes
- Organizations aiming to reduce secret leaks before deployment
- Projects that already use GitHub Actions and want AI augmentation
Not ideal when
- Environments with strict offline or air‑gapped policies
- Projects requiring deterministic, rule‑only linting without AI variability
- Teams without budget for AI provider usage
- Codebases in languages the model cannot understand well
How teams use it
Pre‑merge secret scanning
Blocks pull requests that contain hard‑coded API keys or passwords.
Performance regression detection
Flags newly introduced slow loops or inefficient algorithms before release.
Bug edge‑case identification
Highlights potential null dereferences or unhandled exceptions in recent commits.
Local staged file review
Provides developers immediate AI feedback on code they are about to commit.
Tech snapshot
TypeScript99%
JavaScript1%
Frequently asked questions
Do I need an OpenAI account?
Yes, Shippie requires an API key from a supported LLM provider such as OpenAI.
Can I use Shippie with other CI systems?
While GitHub Actions are documented, the CLI can be invoked in any pipeline, making it compatible with GitLab, CircleCI, etc.
How are the review rules configured?
Rule files can be added to the project to customize which checks run and their severity.
Is there a cost associated with using Shippie?
Only the usage fees of the underlying LLM provider; Shippie itself is free under the MIT license.
Can I run Shippie offline?
No, because it relies on remote LLM APIs; offline use is not supported.
Project at a glance
Active- Stars
- 2,325
- Watchers
- 2,325
- Forks
- 238
LicenseMIT
Repo age2 years old
Last commit2 months ago
Primary languageTypeScript
Last synced 4 hours ago