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🔷 PRISM

AI-Powered Pull Request Intelligence & Software Quality Monitor

Analyze code changes. Detect engineering risks. Improve software before it reaches production.

Status License TypeScript Python


📌 About

PRISM is an open-source developer tool designed to analyze GitHub pull requests and provide actionable engineering feedback.

Instead of treating code review as a simple pass/fail process, PRISM evaluates changes across multiple dimensions:

  • 🧹 Code quality
  • 🔐 Security
  • 🧪 Testing
  • ⚡ Complexity
  • 🏗️ Maintainability
  • 📊 Engineering risk

The long-term goal is to help developers identify problems before they reach production.

PRISM is currently under active development. Features marked as planned are not yet available.


🎯 Problem

Modern development teams move quickly, but fast development can introduce:

  • hidden bugs
  • weak error handling
  • missing tests
  • security risks
  • unnecessary complexity
  • duplicated logic
  • maintainability problems

Traditional linters are useful, but they often focus on isolated rules.

PRISM aims to provide a broader engineering view of a pull request.


💡 Solution

PRISM analyzes a pull request and converts its findings into an understandable engineering report.

Planned workflow

GitHub Pull Request
        │
        ▼
   Change Detection
        │
        ▼
 ┌───────────────────┐
 │   PRISM Engine    │
 ├───────────────────┤
 │ Code Quality      │
 │ Security          │
 │ Testing           │
 │ Complexity        │
 │ Maintainability   │
 └───────────────────┘
        │
        ▼
 Risk & Quality Score
        │
        ▼
 Actionable Report

🚀 Current Status

V1 — Core Analysis

In development

  • Repository analysis
  • Changed-file detection
  • Code quality analysis
  • Basic security checks
  • Complexity analysis
  • Test-related checks
  • Engineering score
  • Structured analysis report
  • Unit tests
  • CI pipeline

V2 — GitHub Integration

Planned

  • GitHub App
  • GitHub API integration
  • Pull request webhooks
  • Automatic PR analysis
  • PR review comments
  • Repository configuration

V3 — AI Intelligence

Planned

  • AI-assisted reasoning
  • Repository context
  • Intelligent recommendations
  • Finding prioritization
  • Explainable analysis
  • Historical quality tracking

V4 — Production Platform

Planned

  • Web dashboard
  • Docker deployment
  • Production monitoring
  • Sentry integration
  • CI/CD quality gates
  • Public project analytics

🏗️ Architecture

The project is designed around a modular analysis pipeline.

Repository
    │
    ▼
Discovery
    │
    ▼
File Analysis
    │
    ├── Code Quality
    ├── Security
    ├── Testing
    └── Complexity
    │
    ▼
Finding Aggregation
    │
    ▼
Risk Scoring
    │
    ▼
Report Generation

The architecture is intentionally modular so additional analyzers can be introduced without rewriting the entire system.


🛠️ Technology

Planned Core Stack

Layer Technology
Frontend Next.js
Language TypeScript
AI / Analysis Services Python
API FastAPI
Database PostgreSQL / Supabase
GitHub Integration GitHub API
Testing Vitest / Pytest
CI/CD GitHub Actions
Monitoring Sentry
Deployment Docker

The stack may evolve during development as engineering requirements become clearer.


🔍 Example Analysis

A future PRISM report may look like:

PRISM ANALYSIS
──────────────────────────────

Overall Score       84 / 100

Code Quality        88
Security            91
Testing             72
Complexity          81
Maintainability     86

Findings
──────────────────────────────

⚠ Medium
Missing test coverage for payment failure path.

⚠ Medium
Function complexity exceeds configured threshold.

ℹ Low
Error response could provide more structured information.

Recommended Actions
──────────────────────────────

1. Add failure-path tests.
2. Simplify the complex function.
3. Standardize API error handling.

Example output only. Scores shown above are illustrative and are not generated by the current implementation.


🧠 Design Principles

PRISM follows several principles:

1. Evidence over assumptions

Every finding should be connected to observable code or repository information.

2. Deterministic checks first

Where a reliable static rule can identify an issue, PRISM should prefer deterministic analysis over unnecessary AI calls.

3. AI as an assistant

AI should help explain, prioritize, and reason about findings — not blindly replace deterministic checks.

4. Actionable feedback

A finding should ideally explain:

  • What was detected
  • Why it matters
  • Where it occurs
  • How it can be improved

5. Human verification

PRISM is an engineering assistant, not an authority.

Developers should verify important findings before making production changes.


🔐 Security

Security is a core design goal.

PRISM should:

  • avoid exposing repository secrets
  • never log API keys
  • validate external input
  • apply least-privilege GitHub permissions
  • avoid executing untrusted repository code by default
  • document security-sensitive behavior

See SECURITY.md for the security policy.


🧪 Testing

Testing is part of the project architecture rather than an afterthought.

Planned testing layers:

Unit Tests
    ↓
Integration Tests
    ↓
GitHub API Tests
    ↓
End-to-End Tests

Every major analyzer should have automated tests covering both valid and problematic inputs.


📊 Quality Goals

PRISM will track measurable engineering metrics as the project develops.

Examples:

  • test coverage
  • analysis duration
  • analyzer reliability
  • false-positive rate
  • API response time
  • CI success rate

Metrics will only be published when they are measured from actual project runs.


🗺️ Roadmap

Phase 1 — Foundation

  • Repository initialization
  • Analyzer architecture
  • Basic quality rules
  • Test framework
  • CI

Phase 2 — Analysis Engine

  • Security checks
  • Complexity checks
  • Test analysis
  • Scoring engine
  • Report generation

Phase 3 — GitHub

  • GitHub App
  • Webhooks
  • Pull request analysis
  • Review comments

Phase 4 — Intelligence

  • AI reasoning
  • Repository context
  • Recommendations
  • Historical analysis

Phase 5 — Production

  • Dashboard
  • Monitoring
  • Docker
  • Documentation
  • Open-source contribution workflow

🤝 Contributing

Contributions are welcome.

Before submitting a pull request:

  1. Read the contribution guidelines.
  2. Create a focused branch.
  3. Add or update tests.
  4. Run the project's checks locally.
  5. Explain the reason for the change.
  6. Keep pull requests focused.

See CONTRIBUTING.md.


📚 Documentation

Documentation will be expanded as the project develops.

Planned documentation:

  • Architecture
  • API
  • Analyzer rules
  • Scoring system
  • GitHub App setup
  • Security model
  • Development guide
  • Deployment guide

⚖️ License

This project is licensed under the MIT License.

See LICENSE for details.


⭐ Project Vision

PRISM aims to become an open-source engineering intelligence layer for GitHub workflows.

The vision is simple:

Make software quality visible before software reaches production.


🏷️ Topics

ai
artificial-intelligence
code-review
github-app
github-api
pull-request
developer-tools
code-quality
static-analysis
security
devsecops
software-engineering
typescript
python
fastapi
nextjs
open-source

👨‍💻 Author

Om Harde

Building PRISM as an open-source project focused on AI-assisted software engineering, developer tooling, and production-quality development.


⭐ If PRISM becomes useful to you, consider starring the repository and contributing ideas, issues, or improvements.

About

PRISM analyzes GitHub pull requests, detects engineering risks, and gives developers actionable feedback before code reaches production.AI-powered GitHub Pull Request intelligence tool for code quality, security, testing, and engineering risk analysis.

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