wheelspotter（wheelspotter 搜索）

WheelSpotter (v1.0)

🎯 WheelSpotter — Your wheel-spotting scout. Spots reusable solutions before you build from scratch.

Core principle: Solutions must be directly integrable—not flashy but unusable toys.

When to Use

✅ Trigger Scenarios

Load this skill when the user expresses these intents:

Keyword matches: is there, existing, wheel, library, framework, API, tool, solution, spot

❌ Do NOT Trigger

Design Principles

Prerequisites

pip install -r requirements.txt

Environment:

• Python 3.8+
• Internet access for API calls
• GitHub Token (optional, increases API limit to 5000 req/hour)

Input/Output Specification

Input Format

# Method 1: Natural language (parsed by agent)
user_input = "I need a Python library to process Excel files"

# Method 2: Structured input (optional)
{
    "requirement": "process Excel files",
    "tech_stack": ["Python"],
    "intent": "library",
    "constraints": {
        "license": "MIT",
        "min_stars": 100,
        "last_updated": "12m"
    }
}

Output Format

{
    "status": "found",
    "recommendations": [
        {
            "name": "openpyxl",
            "source": "pypi",
            "url": "https://openpyxl.readthedocs.io/",
            "match_score": 0.92,
            "integration_score": 0.95,
            "action": "pip install openpyxl",
            "license": "MIT",
            "stars": 1200,
            "last_updated": "2 months ago",
            "warnings": [],
            "advice": "Recommended, mature and stable"
        }
    ],
    "fallback": null,
    "cost": {
        "tokens_used": 420,
        "time_seconds": 3.2,
        "estimated_time_saved": "~4 hours"
    }
}

Status values:

• found: Suitable solutions found
• not_found: Recommend self-build
• needs_clarification: Requirement unclear, need follow-up
• error: Search failed, return error info

Core Workflow

User Input
  ↓
[M0] Complexity Grading (~30 tokens)
  ↓
[M1] Intent Classification (~60 tokens)
  ↓
[Optional] Clarification (1-2 rounds if needed)
  ↓
[M2] Extract Keywords + Tech Entities (~150 tokens)
  ↓
[Search] Activate platforms by intent, parallel API calls
  ↓
[Hard Filter] Deprecated/activity/form matching
  ↓
[LLM Refinement] Multi-dimensional eval for ≤5 candidates (~300 tokens)
  ↓
Output recommendations + action commands + cost report

Implementation Details

Step 1: Complexity Grading (M0)

Prompt Template:

You are a development complexity assessment expert. Evaluate the requirement:
- L1: Simple function/tool, solvable with dozens of lines
- L2: Medium module, requires interface design
- L3: Complex system, involves multiple components

Requirement: {requirement}
Output JSON only: { "complexity": "L2", "reason": "..." }

Impact on Search Strategy:

Step 2: Intent Classification (M1)

Prompt Template:

Analyze the requirement, determine desired output form (multiple allowed):
- library: Library/framework integrable into code
- service: Callable external API/service
- tool: Standalone executable tool/CLI
- reference: Code template/example/architecture reference
- assistant: Conversational assistant (usually not a wheel, use cautiously)

Requirement: {requirement}
Output JSON only: { "intent": [...], "reason": "..." }

Important: If intent only contains assistant, return guidance without triggering search.

Step 3: Platform Selection Matrix

Step 4: Hard Filtering Rules

def hard_filter(candidate, complexity, intent):
    """Adaptive hard filtering"""
    
    # 1. Archived/Deprecated check
    if candidate.archived or candidate.deprecated:
        return False, "Archived or deprecated"
    
    # 2. Dynamic star threshold
    thresholds = {"L1": 10, "L2": 50, "L3": 100}
    if candidate.stars < thresholds[complexity]:
        return False, f"Insufficient stars ({candidate.stars} < {thresholds[complexity]})"
    
    # 3. Update time check
    if months_since_update > 24:
        return False, "Not updated in 24+ months"
    
    # 4. Form consistency check
    if intent == "library" and not has_package_indicator(candidate):
        return False, "Form mismatch: no library indicators"
    
    return True, "Passed"

Step 5: LLM Refinement

Multi-dimensional Scoring:

Final Score = Semantic Similarity × 0.5 
            + Integration Feasibility × 0.3 
            + Activity Normalization × 0.2

Refinement Prompt:

You are a technical solution evaluator. Assess this candidate:

Requirement: {requirement}
Candidate: {candidate}

Evaluation dimensions:
1. Semantic match (0-1): Does it truly solve the need?
2. Integration feasibility (0-1): Can user try within 1 hour?
3. Activity score (0-1): Based on stars, update frequency
4. License compatibility: Common open source license?
5. Known issues: Major bugs or security vulnerabilities?

Output JSON:
{
    "semantic_score": 0.9,
    "integration_score": 0.85,
    "activity_score": 0.7,
    "final_score": 0.83,
    "license_ok": true,
    "warnings": [],
    "advice": "Recommended, but note..."
}

Search Script

See scripts/search.py for the standalone implementation.

Usage:

# Basic usage
python scripts/search.py --query "python pdf parser" --complexity L2 --intent library

# Multiple platforms
python scripts/search.py -q "python excel read write" -c L2 -i library -p github,pypi

# With GitHub token (recommended)
python scripts/search.py -q "react charting library" -c L3 --token $GITHUB_TOKEN

Parameters:

Error Handling

Cost Control

Three-Tier Budget System

Early Termination Conditions

• Hard filter yields 0 candidates → Immediately output "not found, recommend self-build"
• Intent is only assistant → Don't trigger search
• High-match result found (score > 0.9) → Early termination

Graceful Degradation

⚠️ Search cost approaching or exceeding self-build cost (estimated self-build: X hours).

Found partial matches:
- [Project Name] (match score: 0.65)

Suggestion: Try the above first. If requirements not met, direct implementation may be faster.

Usage Examples

Example 1: Library Search (L2)

Input:

I need a Python library to read and write Excel files

Agent Analysis:

• Complexity: L2 (medium module)
• Intent: library
• Keywords: python, excel, read, write

Script Call:

python scripts/search.py -q "python excel read write" -c L2 -i library -p github,pypi

Output:

{
  "status": "found",
  "recommendations": [
    {
      "name": "openpyxl/openpyxl",
      "action": "pip install openpyxl",
      "match_score": 0.92,
      "advice": "Recommended, comprehensive features"
    },
    {
      "name": "pandas-dev/pandas",
      "action": "pip install pandas",
      "match_score": 0.88,
      "advice": "Use pandas for data analysis needs"
    }
  ]
}

Example 2: Simple Requirement (L1 - Quick Abandonment)

Input:

I need to validate email format

Agent Judgment:

• Complexity: L1 (~10 lines of regex)
• Recommendation: Direct implementation is faster than searching

Output:

{
  "status": "not_found",
  "message": "This is an L1 simple requirement. Recommend direct implementation.",
  "code_snippet": "import re\
re.match(r'^[\\w.-]+@[\\w.-]+\\.\\w+$', email)"
}

Example 3: Service Discovery (L3)

Input:

I need an OCR service that can batch process PDFs via API, supporting Chinese and English

Agent Analysis:

• Complexity: L3 (complex system)
• Intent: service
• Keywords: ocr, api, pdf, batch, chinese, english

Output:

{
  "status": "found",
  "recommendations": [
    {
      "name": "Tesseract OCR",
      "type": "library + CLI",
      "action": "pip install pytesseract or docker run tesseract",
      "match_score": 0.85,
      "warnings": ["Requires self-hosting"]
    },
    {
      "name": "Google Cloud Vision API",
      "type": "cloud service",
      "action": "Apply for API key then call",
      "match_score": 0.90,
      "warnings": ["Paid service"]
    }
  ]
}

Implementation Roadmap

Recommendation: M1 is production-ready. M2-M5 are optional enhancements.

Limitations

Resource Index

Best Practices

1. Extract specific keywords before calling the script
2. Classify complexity and intent accurately - determines search strategy
3. Check license compatibility before final recommendation
4. Provide context when requirements are ambiguous
5. Respect early termination - L1 requirements should self-build if not found

Why WheelSpotter Works

WheelSpotter isn't a "comprehensive search engine" — it's your wheel-spotting scout:

• 🎯 First determines if search is worthwhile - Complexity grading
• 📍 Then determines where to search most accurately - Intent-driven platform selection
• 💰 Gets decision evidence at lowest cost - Budget control
• ✅ Always provides next action - Closed-loop delivery

Changelog