Skill Recommender Pro

Intelligent skill recommendations for OpenClaw. Analyzes installed skills using rule-based filtering and pattern matching. Suggests complementary skills, alternatives, and gap-fillers. Supports multi-language output.

ToBeWin@tobewin

Install

openclaw skills install @tobewin/skill-recommender-pro

Skill Recommender Pro

Intelligent skill recommendation engine for OpenClaw. Goes beyond simple search to provide personalized, context-aware recommendations.

What Makes This Better

Feature	Other Recommenders	skill-recommender-pro
Basic search	✅	✅
Installed skills analysis	❌	✅
Complementary recommendations	❌	✅
Alternative suggestions	❌	✅
Personalized by role	❌	✅
Semantic analysis	❌	✅ (agent-assisted)
Multi-language	❌	✅

Trigger Conditions

"Recommend skills for me" / "推荐skills给我"
"What skills should I install?" / "我应该安装什么skills？"
"Find alternatives to X" / "找X的替代品"
"Compare X and Y skills" / "对比X和Y这两个skill"
"What's missing in my setup?" / "我的配置缺少什么？"
"Best skills for developers" / "开发者最佳skills"
"skill-recommender-pro"

Step 1: Analyze User's Current Setup

First, understand what the user already has:

bash

# Get installed skills
clawhub list 2>/dev/null || echo "No skills installed"

Analysis Script

python

python3 << 'PYEOF'
import json
import subprocess
import os

def get_installed_skills():
    """Get list of installed skills"""
    try:
        result = subprocess.run(
            ["clawhub", "list"],
            capture_output=True,
            text=True,
            timeout=10
        )
        if result.returncode == 0:
            return result.stdout.strip().split('\n')
    except:
        pass
    return []

def categorize_skills(skills):
    """Categorize installed skills by function"""
    categories = {
        "development": ["github", "git", "code", "debug", "test"],
        "research": ["search", "research", "analyze", "summarize"],
        "productivity": ["calendar", "email", "task", "note", "todo"],
        "media": ["image", "video", "audio", "tts", "ocr"],
        "data": ["csv", "json", "database", "sql", "api"],
        "ai": ["llm", "model", "train", "embedding"],
        "devops": ["docker", "deploy", "ci", "cd", "cloud"]
    }
    
    user_categories = {}
    for skill in skills:
        skill_lower = skill.lower()
        for category, keywords in categories.items():
            if any(kw in skill_lower for kw in keywords):
                user_categories.setdefault(category, []).append(skill)
    
    return user_categories

def identify_gaps(installed, categories):
    """Identify missing categories that could be useful"""
    all_categories = set(categories.keys())
    user_categories = set(installed.keys())
    gaps = all_categories - user_categories
    return list(gaps)

installed = get_installed_skills()
categories = categorize_skills(installed)
gaps = identify_gaps(categories, {})

print(f"Installed skills: {len(installed)}")
print(f"Categories covered: {list(categories.keys())}")
print(f"Potential gaps: {gaps}")
PYEOF

Step 2: Generate Recommendations

Based on analysis, generate personalized recommendations:

Recommendation Types

text

1. Complementary Skills
   - Skills that work well with what you have
   - Example: If you have china-doc-ocr, recommend china-summarizer

2. Alternative Skills
   - Better options for same functionality
   - Based on downloads, ratings, freshness

3. Gap Filling
   - Skills for missing categories
   - Based on your apparent role/needs

4. Trending Skills
   - Popular skills in the community
   - Recent high-growth skills

Search & Recommend

bash

# For each gap, search for top skills
for category in research productivity devops; do
  echo "🔍 Searching for $category skills..."
  clawhub search "$category" 2>&1 | head -5
done

Generate Report

python

python3 << 'PYEOF'
import json

def generate_recommendations(installed, gaps, lang="en"):
    """Generate personalized recommendations"""
    
    recommendations = {
        "complementary": [],
        "alternatives": [],
        "gap_fillers": [],
        "trending": []
    }
    
    # Complementary pairs
    COMPLEMENTARY_MAP = {
        "china-doc-ocr": ["china-summarizer", "china-tts"],
        "china-tts": ["china-video-gen", "china-image-gen"],
        "research-orchestrator": ["skill-advisor", "web-search"],
        "skill-studio": ["skill-advisor", "research-orchestrator"]
    }
    
    for skill in installed:
        if skill in COMPLEMENTARY_MAP:
            for rec in COMPLEMENTARY_MAP[skill]:
                if rec not in installed:
                    recommendations["complementary"].append({
                        "skill": rec,
                        "reason": f"Works well with {skill}"
                    })
    
    return recommendations

# Example
installed = ["china-doc-ocr", "china-tts", "skill-studio"]
gaps = ["research", "devops"]
recs = generate_recommendations(installed, gaps, "zh")

print(json.dumps(recs, indent=2, ensure_ascii=False))
PYEOF

Step 3: Output Recommendation Report

Chinese Report Format

text

┌─────────────────────────────────────────────────────────┐
│  🎯 个性化Skill推荐                                      │
│  基于你已安装的 5 个skills                                │
└─────────────────────────────────────────────────────────┘

━━━ 📋 推荐报告 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

📊 你的Skills概况
├─ 已安装: china-doc-ocr, china-tts, skill-studio...
├─ 覆盖领域: 文档处理, 语音, 开发工具
└─ 潜在缺口: 研究分析, DevOps

🔥 互补推荐（与你现有skills配合使用）
├─ china-summarizer - 与china-doc-ocr配合，OCR后自动总结
├─ research-orchestrator - 与skill-studio配合，创建研究类skills
└─ skill-advisor - 安装前评估skills安全性

⭐ 热门推荐（社区最受欢迎）
├─ capability-evolver (35K+ downloads) - Agent自我进化
├─ gog (14K+ downloads) - Google Workspace集成
└─ agent-browser (11K+ downloads) - 浏览器自动化

🎯 填补缺口（你可能需要的领域）
├─ 研究分析: research-orchestrator, summarize
├─ DevOps: docker-manager, deploy-helper
└─ 生产力: calendar, email-integration

💡 安装建议
clawhub install china-summarizer research-orchestrator skill-advisor

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

English Report Format

text

┌─────────────────────────────────────────────────────────┐
│  🎯 Personalized Skill Recommendations                  │
│  Based on your 5 installed skills                       │
└─────────────────────────────────────────────────────────┘

━━━ 📋 Recommendation Report ━━━━━━━━━━━━━━━━━━━━━━━━━━━

📊 Your Skills Overview
├─ Installed: china-doc-ocr, china-tts, skill-studio...
├─ Categories: Document Processing, Voice, Dev Tools
└─ Potential Gaps: Research, DevOps

🔥 Complementary (Works with your existing skills)
├─ china-summarizer - Pair with china-doc-ocr for OCR+summary
├─ research-orchestrator - Pair with skill-studio for research
└─ skill-advisor - Pre-install security assessment

⭐ Popular (Community Favorites)
├─ capability-evolver (35K+ downloads) - Agent self-improvement
├─ gog (14K+ downloads) - Google Workspace integration
└─ agent-browser (11K+ downloads) - Browser automation

🎯 Gap Fillers (Areas you might need)
├─ Research: research-orchestrator, summarize
├─ DevOps: docker-manager, deploy-helper
└─ Productivity: calendar, email-integration

💡 Install Suggestion
clawhub install china-summarizer research-orchestrator skill-advisor

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Step 4: Compare Skills (When User Asks)

When user wants to compare skills:

bash

# Get details for each skill
clawhub inspect skill-a
clawhub inspect skill-b

# Compare and recommend

Comparison Report

text

━━━ 📊 Skill对比: skill-a vs skill-b ━━━━━━━━━━━━━━━━━

| 维度 | skill-a | skill-b |
|------|---------|---------|
| 下载量 | 1,000 | 5,000 |
| 更新时间 | 3天前 | 30天前 |
| 功能 | 基础搜索 | 高级分析 |
| 复杂度 | 简单 | 中等 |

🎯 推荐: skill-b（更活跃，功能更全）

Key Differentiators

1. Context-Aware

Analyzes what user already has
Doesn't recommend duplicates
Suggests complementary skills

2. Personalized

Adapts to user's apparent role
Developer vs Researcher vs Content Creator
Different recommendations for each

3. Multi-Source Data

Downloads & popularity
Freshness & maintenance
Community sentiment
Dependency complexity

4. Actionable Output

Ready-to-install commands
Clear reasoning for each recommendation
Prioritized list

Smart Inference Engine

Hybrid Approach: Rules + LLM Analysis

Layer 1: Rule-Based (Fast)

python

# Quick filtering using keyword matching
PAIR_RULES = [
    {"source": ["ocr", "document", "pdf"], "target": ["summarize", "translate"]},
    {"source": ["tts", "voice", "speech"], "target": ["video", "image"]},
    {"source": ["search", "research"], "target": ["summarize", "report"]},
]

Layer 2: Semantic Analysis (Agent-Assisted)

The agent can enhance recommendations using its own reasoning:

text

Agent analyzes:
1. Skill descriptions for semantic relationships
2. Functional dependencies between skills
3. User's apparent use case
4. Complementary workflow patterns

How It Works

text

User: "推荐skills给我"
        ↓
Step 1: Get installed skills (clawhub list)
        ↓
Step 2: Fetch candidate skills from ClawHub API
        ↓
Step 3: Rule-based filtering (fast, removes obvious non-matches)
        ↓
Step 4: Agent semantic analysis (understands context)
        ↓
Step 5: Generate personalized recommendations

Semantic Understanding

Scenario	Rule-Based	Agent-Assisted
china-doc-ocr + china-summarizer	✅ Detected	✅ Detected
skill-studio + research-orchestrator	❌ Missed	✅ Detected (both help create)
china-tts + china-video-gen	✅ Detected	✅ Detected
skill-advisor + any skill	❌ Missed	✅ Detected (advises before install)

The agent provides semantic understanding beyond keyword matching.

Multi-Language Support

Output language automatically matches user's conversation language:

User writes in Chinese → Output Chinese report
User writes in English → Output English report
User specifies "用英文输出" / "Output in Japanese" → Output in specified language

Supported: Chinese, English, Japanese, Korean, and 50+ languages

Error Handling

text

No skills installed     → "Let's start with basics: install X, Y, Z"
ClawHub API error       → "Using cached recommendations..."
Parse error             → "Showing best-effort results"

Notes

Recommendations update based on real-time ClawHub data
Complementary relationships inferred dynamically from skill descriptions
Multi-dimensional classification for accurate categorization
Privacy: Only reads installed skill names and public metadata
No access to config files, tokens, or sensitive data
Supports 50+ languages for output

Limitations (Honest)

Inference accuracy: Depends on quality of skill descriptions on ClawHub
New skills: May not have enough metadata for accurate classification
Edge cases: Some skills don't fit neatly into categories
API dependency: Requires ClawHub API for real-time data