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🔬 EvalScope

Multi-language code evaluation and vulnerability scanning platform — scan codebases, APIs, and configurations for security issues, style violations, performance bottlenecks, and compliance gaps, surfaced through a rich web dashboard.

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ScorecardQuick StartArchitectureAPIModulesContributing


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📊 Scorecard

┌────────────────────────────────────────────────────────────────┐
│                    EVALSCOPE SCAN REPORT                        │
├────────────────────────────────────────────────────────────────┤
│ Repository:   Crynge/eval-demo     Branch: main                │
│ Commit:       a1b2c3d4             Scanned: 2026-07-01         │
├────────────────────────────────────────────────────────────────┤
│ Category          Score     Issues    ──────────── Progress    │
│ Security          ████████░░  80%     12    ▓▓▓▓▓▓▓▓░░░░░░   │
│ Code Style        ███████░░░  70%     24    ▓▓▓▓▓▓▓░░░░░░░░   │
│ Performance       █████████░  90%      3    ▓▓▓▓▓▓▓▓▓░░░░░░   │
│ Accessibility     ██████░░░░  60%     18    ▓▓▓▓▓▓░░░░░░░░░░   │
│ Compliance        ████████░░  80%      7    ▓▓▓▓▓▓▓▓░░░░░░░░   │
│ Test Coverage     █████░░░░░  50%     31    ▓▓▓▓▓░░░░░░░░░░░░  │
├────────────────────────────────────────────────────────────────┤
│ OVERALL           ███████░░░  72%     95 issues                │
│ VERDICT:          ⚠️ Needs improvement (threshold: 80%)        │
└────────────────────────────────────────────────────────────────┘

Features

Feature Description Languages
🔒 Security Scan SQL injection, XSS, hardcoded secrets, credential leaks TS, Go, Python, Rust
🎨 Style Check Linting, formatting, naming conventions TS, Go, Python
⚡ Performance Bottleneck detection, memory leak analysis Go, Rust
♿ Accessibility ARIA labels, contrast ratios, keyboard nav HTML, TSX
📋 Compliance SPDX headers, license audits, dependency checks All
📈 Test Coverage Line/branch coverage mapping TS, Go, Python

Quick Start

# Install
npm install @crynge/evalscope

# Scan a directory (auto-detects language)
npx evalscope scan ./src --format detailed

# CI mode — fail build if score below threshold
npx evalscope scan ./src --ci --threshold 80

# Start web dashboard
npx evalscope dashboard --port 8080
import { Scanner } from '@crynge/evalscope/scanner';

const scanner = new Scanner({
  categories: ['security', 'performance', 'compliance'],
  failOn: ['critical', 'high'],
});

const report = await scanner.scan('./src');
console.log(report.summary);
// { score: 85, passed: true, issues: { critical: 0, high: 2, medium: 7 } }

Architecture

flowchart TB
    subgraph Scanner["Scanner Layer"]
        A[File System Watcher] --> B[Language Detector]
        B --> C1[TS Scanner]
        B --> C2[Go Scanner]
        B --> C3[Python Scanner]
        B --> C4[Rust Scanner]
    end

    subgraph Rules["Rule Engine"]
        C1 --> D[Rule Matcher]
        C2 --> D
        C3 --> D
        C4 --> D
        D --> E[Severity Classifier]
    end

    subgraph API["API Layer"]
        E --> F[Go API Server]
        E --> G[Express API Server]
        F --> H[(SQLite Store)]
        G --> H
    end

    subgraph UI["Dashboard"]
        H --> I[Web Dashboard]
        I --> J[Report View]
        I --> K[Trend Chart]
        I --> L[CI Integration]
    end
Loading

API

# Run a scan
curl -X POST http://localhost:8080/api/scan \
  -H "Content-Type: application/json" \
  -d '{"path": "./src", "categories": ["security", "performance"]}'

# Get results
curl http://localhost:8080/api/results/latest

# Get trend data
curl http://localhost:8080/api/trends?days=30

Modules

src/
├── api/
│   ├── server.ts            # TypeScript REST API
│   └── server.go            # Go REST API (high-throughput)
├── scanner/
│   └── scanner.ts           # Scan engine
├── rules/
│   └── rules.ts             # Rule definitions
└── web/
    └── server.ts            # Dashboard frontend

Contributing

See CONTRIBUTING.md for guidelines.


License

MIT


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Comprehensive AI agent evaluation platform — searchable benchmark catalog, comparison matrices, automated scanner, interactive dashboards, and community-curated best practices for LLM evaluation.

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