project-analyzer

Automated brownfield codebase analysis. Detects project type, frameworks, dependencies, architecture patterns, and generates comprehensive project profile. Essential for Conductor integration and onboarding existing projects.

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Repositório: diegosouzapw/awesome-omni-skill
Última atividade na origem: 28 de fevereiro de 2026 às 03:54
Idioma detectado do SKILL.md: inglês
Estrelas: 50
Forks: 19

Opções de instalação

Por padrão, está selecionado o prompt que primeiro revisa a origem. Você pode mudar para um comando direto ou baixar uma cópia local.

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npx skills add https://github.com/diegosouzapw/awesome-omni-skill --skill project-analyzer

O comando permanece em uma só linha. Role horizontalmente para revisá-lo antes de copiar.

Prefere uma cópia local? Baixe os arquivos disponíveis atualmente no SkillsMP.

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SKILL.md

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2026-03-02

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AshAi extension guidelines for integrating AI capabilities with Ash Framework. Use when implementing vectorization/embeddings, exposing Ash actions as LLM tools, creating prompt-backed actions, or setting up MCP servers. Covers semantic search, LangChain integration, and structured outputs.

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ask-oracle

This skill should be used when solving hard questions, complex architectural problems, or debugging issues that benefit from GPT-5 Pro or GPT-5.1 thinking models with large file context. Use when standard Claude analysis needs deeper reasoning or extended context windows.

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name	project-analyzer
description	Automated brownfield codebase analysis. Detects project type, frameworks, dependencies, architecture patterns, and generates comprehensive project profile. Essential for Conductor integration and onboarding existing projects.
version	1.0.0
model	sonnet
invoked_by	both
user_invocable	true
tools	["Read","Glob","Grep","Bash"]
best_practices	["Detect project root from package managers and manifest files","Identify frameworks from dependencies and directory structure","Generate comprehensive file statistics and language breakdown","Map component relationships and architecture patterns","Validate output against project-analysis.schema.json","Execute in < 30 seconds for typical projects (< 10k files)"]
error_handling	graceful
streaming	supported
executable	.claude/tools/project-analyzer/analyzer.mjs
test_suite	.claude/tools/project-analyzer/tests/analyzer.test.mjs
output_schema	.claude/schemas/project-analysis.schema.json
verified	false
lastVerifiedAt	"2026-02-19T05:29:09.098Z"

References (archive): SCAFFOLD_SKILLS_ARCHIVE_MAP.md — ProjectAnalyzer monorepo/service detection from Auto-Claude-develop analysis/analyzers.

Project Analyzer - Automated brownfield codebase analysis for rapid project onboarding and understanding. - Detecting project type (frontend, backend, fullstack, library, cli, mobile, monorepo) - Identifying frameworks and libraries from manifests and structure - Generating file statistics and language breakdown - Mapping component relationships and module structure - Detecting architecture patterns (MVC, layered, microservices, etc.) - Analyzing dependency health and outdated packages - Identifying code quality indicators (linting, testing, type safety) - Detecting technical debt and anti-patterns - Generating prioritized improvement recommendations

Step 1: Identify Project Root

Locate project root by finding manifest files:

Search for package manager files:
- package.json (Node.js/JavaScript/TypeScript)
- requirements.txt, pyproject.toml, setup.py (Python)
- go.mod (Go)
- Cargo.toml (Rust)
- pom.xml, build.gradle (Java/Maven/Gradle)
- composer.json (PHP)
Identify project root:
- Directory containing primary package manager file
- Handle monorepos (multiple package.json files)
- Detect workspace configuration
Validate project root:
- Check for .git directory
- Verify source code directories exist
- Ensure manifest files are parsable

Step 2: Detect Project Type

Classify project based on manifest files and directory structure:

Frontend Projects:
- Indicators: React, Vue, Angular, Svelte dependencies
- Directory: src/components/, public/, assets/
- Frameworks: Next.js, Nuxt.js, Gatsby, Vite
Backend Projects:
- Indicators: Express, FastAPI, Django, Flask, Gin dependencies
- Directory: routes/, , ,

**Performance Requirements**: **Integration with Conductor**: - Provides automated project discovery - Eliminates manual context gathering - Enables 80% faster brownfield onboarding - Feeds project context to chat interface **Programmatic Usage**:

{ "analysis_id": "analysis-llm-rules-20250115", "project_type": "fullstack", "analyzed_at": "2025-01-15T10:30:00.000Z", "project_root": "C:\\dev\\projects\\LLM-RULES", "stats": { "total_files": 1243, "total_lines": 125430, "languages": { "JavaScript": 45230, "TypeScript": 38120, "Markdown": 25680, "JSON": 12400, "YAML": 4000 }, "file_types": { ".js": 234, ".mjs": 156, ".ts": 89, ".md": 312, ".json": 145 }, "directories": 87, "avg_file_size_lines": 101, "largest_files": [ { "path": ".claude/tools/enforcement-gate.mjs", "lines": 1520 } ] }, "frameworks": [ { "name": "nextjs", "version": "14.0.0", "category": "framework", "confidence": 1.0, "source": "package.json" }, { "name": "react", "version": "18.2.0", "category": "framework", "confidence": 1.0, "source": "package.json" } ], "structure": { "root_directories": [ { "name": ".claude", "purpose": "config", "file_count": 543 }, { "name": "conductor-main", "purpose": "source", "file_count": 234 } ], "entry_points": [ { "path": "conductor-main/src/index.ts", "type": "main" } ], "architecture_pattern": "modular", "module_system": "esm" }, "dependencies": { "production": 45, "development": 23 }, "code_quality": { "linting": { "configured": true, "tool": "eslint" }, "formatting": { "configured": true, "tool": "prettier" }, "testing": { "framework": "vitest", "test_files": 89, "coverage_configured": true }, "type_safety": { "typescript": true, "strict_mode": true } }, "tech_debt": { "score": 35, "indicators": [ { "category": "complexity", "severity": "medium", "description": "3 files exceed 1000 lines", "remediation_effort": "moderate" } ] }, "recommendations": [ { "priority": "P1", "category": "maintainability", "title": "Refactor large files", "description": "Break down files > 1000 lines into smaller modules", "effort": "moderate", "impact": "high" } ], "metadata": { "analyzer_version": "1.0.0", "analysis_duration_ms": 2340, "files_analyzed": 1243, "files_skipped": 3420, "errors": [] } }

project-analyzer

Opções de instalação

Revise os arquivos de origem

Step 1: Identify Project Root

Step 2: Detect Project Type

Step 1: Identify Project Root

Step 2: Detect Project Type

Step 3: Framework Detection

Step 4: File Statistics

Step 5: Structure Analysis

Step 6: Dependency Analysis

Step 7: Code Quality Indicators

Step 8: Pattern Detection

Step 9: Technical Debt Analysis

Step 10: Generate Recommendations

Step 11: Validate Output

References

Memory Protocol (MANDATORY)

project-analyzer

Informações da origem

Opções de instalação

Revise os arquivos de origem

Mais deste repositório

Mais deste repositório

Step 1: Identify Project Root

Step 2: Detect Project Type

Step 1: Identify Project Root

Step 2: Detect Project Type

Step 3: Framework Detection

Step 4: File Statistics

Step 5: Structure Analysis

Step 6: Dependency Analysis

Step 7: Code Quality Indicators

Step 8: Pattern Detection

Step 9: Technical Debt Analysis

Step 10: Generate Recommendations

Step 11: Validate Output

References

Memory Protocol (MANDATORY)