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/recommend

Ultra-intelligent command recommendation engine - recommends the most suitable SuperClaude commands for any user input

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claude-scholar
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> /plugin marketplace add Galaxy-Dawn/claude-scholar
> /plugin install claude-scholar@claude-scholar

Command definition

recommend.md
name: sc:recommend
description: Ultra-intelligent command recommendation engine - recommends the most suitable SuperClaude commands for any user input
category: utility

SuperClaude Intelligent Command Recommender

**Purpose**: Ultra-intelligent command recommendation engine - recommends the most suitable SuperClaude commands for any user input

Command Definition

/sc:recommend [user request] --options [flags]

Multi-language Support

Language Detection and Translation System

language_mapping:
  turkish_keywords:
    machine_learning: ["machine learning", "ml", "artificial intelligence", "ai"]
    website: ["website", "web site", "site", "page"]
    application: ["app", "application", "program", "software"]
    error: ["error", "bug", "issue", "problem"]
    performance: ["performance", "speed", "fast", "optimization"]
    new: ["new", "create", "build", "start", "develop"]
    analysis: ["analyze", "analysis", "examine", "research"]

  english_keywords:
    machine learning: ["machine learning", "artificial intelligence", "ml", "ai"]
    website: ["website", "site", "page", "web application"]
    performance: ["performance", "speed", "optimization", "speed"]
    error: ["error", "issue", "bug", "problem"]

  universal_patterns:
    question_words: ["how", "what", "why", "which"]
    action_words: ["do", "create", "build", "develop"]
    help_words: ["help", "suggest", "recommend", "learn"]

Language Detection Algorithm

def detect_language_and_translate(input_text):
    turkish_chars = ['ç', 'ğ', 'ı', 'ö', 'ş', 'ü']
    if any(char in input_text.lower() for char in turkish_chars):
        return "tr"

    english_common = ["the", "and", "is", "are", "was", "were", "will", "would", "could", "should"]
    if any(word in input_text.lower().split() for word in english_common):
        return "en"

    return "en"  # Default to English

Multi-language Examples

# Turkish examples
/sc:recommend "makine öğrenmesi algoritması başlat"
/sc:recommend "sitem yavaş açılıyor, ne yapayım?"
/sc:recommend "yeni bir özellik eklemeliyim"
/sc:recommend "hata alıyorum, çözüm bul"

# English examples
/sc:recommend "I want to build ML algorithm"
/sc:recommend "my website is slow, help me optimize"
/sc:recommend "I need to add a new feature"
/sc:recommend "getting errors, need debugging"

# Mixed language
/sc:recommend "makine learning projesi yapmak istiyorum"

SuperClaude Integrated Recommendation Engine

1. Keyword Extraction and Persona Matching

keyword_extraction:
  pattern_matching:
    # Machine Learning
    - "machine learning|ml|ai|artificial intelligence" → ml_category + --persona-analyzer
    - "data|database|sql" → data_category + --persona-backend
    - "model|algorithm|prediction|classify" → ml_category + --persona-architect

    # Web Development
    - "website|frontend|ui/ux" → web_category + --persona-frontend
    - "react|vue|angular|component" → web_category + --persona-frontend --magic
    - "api|backend|server|microservice" → api_category + --persona-backend

    # Debugging & Performance
    - "error|bug|issue|not working" → debug_category + --persona-analyzer
    - "slow|performance|optimization" → performance_category + --persona-performance
    - "security|auth|vulnerability" → security_category + --persona-security

    # Development
    - "new|create|build|develop|feature" → create_category + --persona-frontend|backend
    - "design|architecture" → design_category + --persona-architect
    - "test|qa|quality|validation" → test_category + --persona-qa

    # Learning
    - "how|learn|explain|tutorial" → learning_category + --persona-mentor
    - "refactor|cleanup|improve|quality" → improve_category + --persona-refactorer

  context_analysis:
    - "beginner|starter|just started" → beginner_level + --persona-mentor
    - "expert|senior|experienced" → expert_level + --persona-architect
    - "continue|resume" → continuity_mode + --seq
    - "next step|what now" → next_step_mode + --think

2. SuperClaude Command Map

category_mapping:
  ml_category:
    primary_commands: ["/sc:analyze --seq --c7", "/sc:design --seq --ultrathink"]
    secondary_commands: ["/sc:build --feature --tdd", "/sc:improve --performance"]
    mcp_servers: ["--c7", "--seq"]
    personas: ["--persona-analyzer", "--persona-architect"]
    flags: ["--think-hard", "--evidence", "--profile"]

  web_category:
    primary_commands: ["/sc:build --feature --magic", "/sc:design --api --seq"]
    secondary_commands: ["/sc:test --coverage --e2e --pup", "/sc:analyze --code"]
    mcp_servers: ["--magic", "--c7", "--pup"]
    personas: ["--persona-frontend", "--persona-qa"]
    flags: ["--react", "--tdd", "--validate"]

  api_category:
    primary_commands: ["/sc:design --api --ddd --seq", "/sc:build --feature --tdd"]
    secondary_commands: ["/sc:scan --security --owasp", "/sc:analyze --performance --pup"]
    mcp_servers: ["--seq", "--c7", "--pup"]
    personas: ["--persona-backend", "--persona-security"]
    flags: ["--microservices", "--ultrathink", "--security"]

  debug_category:
    primary_commands: ["/sc:troubleshoot --investigate --seq", "/sc:analyze --code --seq"]
    secondary_commands: ["/sc:scan --security", "/sc:improve --quality"]
    mcp_servers: ["--seq", "--all-mcp"]
    personas: ["--persona-analyzer", "--persona-security"]
    flags: ["--evidence", "--think-hard", "--profile"]

  performance_category:
    primary_commands: ["/sc:analyze --performance --pup --profile", "/sc:troubleshoot --seq"]
    secondary_commands: ["/sc:improve --performance --iterate", "/sc:build --optimize"]
    mcp_servers: ["--pup", "--seq"]
    personas: ["--persona-performance", "--persona-analyzer"]
    flags: ["--profile", "--monitoring", "--benchmark"]

  security_category:
    primary_commands: ["/sc:scan --security --owasp --deps", "/sc:analyze --security --seq"]
    secondary_commands: ["/sc:improve --security --harden",
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Semi-automated research assistant for academic research and software development. Supports Claude Code, Codex CLI, Kimi Code CLI, and OpenCode across ideation, coding, experiments, writing, and publication.

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