framework detector
2
总安装量
0
周安装量
#75285
全站排名
安装命令
npx skills add https://github.com/fortiumpartners/ai-mesh --skill 'Framework Detector'
Skill 文档
Framework Detector Skill
Quick Reference
When to Use: Automatically detect framework in project before loading framework-specific skills
Supported Frameworks: NestJS, React, Phoenix, Rails, .NET/ASP.NET Core, Blazor
Detection Method: Multi-signal analysis with weighted confidence scoring
Usage
Basic Detection
const FrameworkDetector = require('./detect-framework');
const detector = new FrameworkDetector('/path/to/project');
const result = await detector.detect();
console.log(result.primary); // "nestjs"
console.log(result.confidence); // 0.92
console.log(result.alternates); // [{ framework: "dotnet", confidence: 0.45 }]
CLI Usage
# Detect framework in current directory
./detect-framework.js
# Detect framework in specific project
./detect-framework.js /path/to/project
# Output format (JSON)
{
"primary": "react",
"confidence": 0.89,
"alternates": [],
"details": { ... }
}
Detection Signals
1. Package Manager (Weight: 10)
- Node.js:
package.jsondependencies - Ruby:
Gemfilegems - Elixir:
mix.exsdependencies - .NET:
*.csprojPackageReferences
2. Files (Weight: 8-9)
- Required: Framework-specific config files
- Optional: Common project structure files
- Wildcards: Pattern matching (e.g.,
*.csproj)
3. Imports (Weight: 7-8)
- Code pattern analysis in source files
- Regex-based matching
- File sampling for performance (max 20 files)
4. Boost Factors (Multiplier: 1.2-1.6x)
- Strong indicators multiply confidence
- Framework-specific patterns
- Examples:
- NestJS:
nest-cli.json(+50%) - React: JSX files (+40%)
- Blazor:
.razorfiles (+60%)
- NestJS:
Confidence Threshold
Default: 0.8 (80% confidence required)
Interpretation:
- ⥠0.9: Very high confidence
- 0.8-0.9: High confidence
- 0.6-0.8: Medium confidence (below threshold)
- < 0.6: Low confidence
Framework-Specific Patterns
NestJS Detection
â @nestjs/core in package.json
â nest-cli.json exists
â @Module decorator in .ts files
â @Controller decorator in .ts files
React Detection
â react in package.json
â .jsx or .tsx files exist
â useState or useEffect in code
â createRoot in code
Phoenix Detection
â {:phoenix, in mix.exs
â config/config.exs exists
â Phoenix.Endpoint in .ex files
â Phoenix.Router in .ex files
Rails Detection
â gem 'rails' in Gemfile
â config/application.rb exists
â Rails.application in code
â ActiveRecord::Base in code
.NET Detection
â Microsoft.AspNetCore in .csproj
â Program.cs exists
â [ApiController] in .cs files
â using Microsoft.AspNetCore
Blazor Detection
â Microsoft.AspNetCore.Components in .csproj
â .razor files exist
â @page directive in .razor files
â ComponentBase in code
Performance
Optimizations:
- File sampling (10-20 files max per check)
- Glob ignore patterns (node_modules, dist, build)
- Early exit on strong matches
- Async/await for parallel checks
Typical Detection Time: 100-500ms
Error Handling
No Frameworks Detected:
{
primary: null,
confidence: 0,
alternates: [],
details: {}
}
Multiple Frameworks (e.g., monorepo):
{
primary: "react",
confidence: 0.91,
alternates: [
{ framework: "nestjs", confidence: 0.87 }
]
}
Integration with SkillLoader
const { SkillLoader } = require('../skill-loader');
const FrameworkDetector = require('./detect-framework');
async function loadFrameworkSkill(projectRoot) {
// 1. Detect framework
const detector = new FrameworkDetector(projectRoot);
const result = await detector.detect();
// 2. Handle low confidence
if (result.confidence < 0.8) {
// Prompt user or use alternates
console.warn('Low confidence detection');
}
// 3. Load skill
const loader = new SkillLoader({
agentName: 'backend-developer',
agentVersion: '3.0.0'
});
const skill = await loader.loadSkill(result.primary, 'quick');
return skill;
}
Configuration
Edit framework-patterns.json to:
- Add new frameworks
- Adjust detection weights
- Modify confidence threshold
- Update boost factors
See Also
- framework-patterns.json – Detection patterns configuration
- detect-framework.js – Implementation source
- ../../lib/skill-loader.js – Skill loading integration