Dataset Generator Skill

Dataset Generator Skill

Generate high-quality benchmark evaluation datasets with adjustable difficulty levels from custom PDF documents. Perfect for testing RAG systems, knowledge graphs, and Q&A models.

Usage

Invoke this skill with:

/​dataset-generator <pdf_directory> [output_file] [num_questions] [difficulty]

Arguments:

• $1 (required) - Path to PDF directory containing source documents
• $2 (optional) - Output JSON file path (default: benchmark_dataset.json)
• $3 (optional) - Number of questions to generate (default: 20)
• $4 (optional) - Difficulty level: easy, medium, hard, or mixed (default: mixed)

Examples

# Generate 20 mixed-difficulty questions
/​dataset-generator ./​pdfs

# Generate 30 hard questions
/​dataset-generator ./​pdfs hard_benchmark.json 30 hard

# Generate 15 easy questions for testing retrieval
/​dataset-generator ./​pdfs easy_test.json 15 easy

What This Skill Does

1. Extract Content: Reads all PDFs from specified directory using pypdf
2. Analyze Topics: Uses Claude to identify key concepts, entities, dates, and relationships
3. Generate Questions: Creates questions across 5 types:
   • Fact Retrieval: Direct facts extractable from single passages
   • Multi-hop Reasoning: Requires connecting 2-3 pieces of information
   • Comparative Analysis: Compare concepts, approaches, or entities
   • Contextual Summarization: Broad understanding across multiple sections
   • Creative Generation: Application/​scenario-based questions
4. Difficulty Calibration: Adjusts question complexity and required reasoning depth
5. Format Output: Standard benchmark JSON format with:
   • Question and ground truth answer
   • Question type classification
   • Difficulty level
   • 2-5 supporting evidence passages
   • Evidence relationship explanations

Difficulty Levels

Easy (Single-hop, Direct)

• Reasoning: Answerable from a single chunk/​passage
• Evidence: Direct quotes sufficient
• Chunk Size: 300-500 chars
• Examples:
  • "What is [Product/​Service] described in the document?"
  • "Who is mentioned as the CEO in [Year]?"
  • "What is the duration/​cost/​size of [Feature]?"

Medium (Multi-hop, Inference)

• Reasoning: Requires 2-3 pieces of information
• Evidence: Light inference and connection needed
• Chunk Size: 800-1000 chars
• Examples:
  • "How does [Concept A] affect [Concept B]?"
  • "What are the requirements for [Process/​System]?"

Hard (Synthesis, Cross-document)

• Reasoning: Requires synthesizing info across multiple documents
• Evidence: Implicit relationships, complex inference
• Chunk Size: 1200-1500 chars
• Examples:
  • "Compare [Company's] approach in [Document A] vs [Document B]"
  • "Summarize how [System] addresses [Challenge] across all documents"

Mixed (Balanced Distribution)

• Distribution: 40% easy, 40% medium, 20% hard
• Purpose: Comprehensive testing across difficulty spectrum
• Chunk Size: Adaptive (1000 chars average)

Output Format

Standard evaluation JSON format:

[
  {
    "id": "unique-hash-id",
    "question": "What is the main product described?",
    "answer": "The main product is a cloud-based solution that provides...",
    "question_type": "Fact Retrieval",
    "difficulty": "easy",
    "evidence": [
      "The product is a cloud-based solution that provides enterprise-grade features...",
      "Key capabilities include real-time processing and analytics..."
    ],
    "evidence_relations": "Evidence 1 defines the product, evidence 2 details key capabilities."
  }
]

Implementation Details

When invoked, execute Python script generate_benchmark_with_difficulty.py which:

1. Load PDFs: Extract text from all PDFs in directory
2. Adaptive Chunking:
   • Easy: 300-500 char chunks
   • Medium: 800-1000 char chunks
   • Hard: 1200-1500 char chunks with 25% overlap
3. Topic Analysis: Use Claude to identify:
   • Key entities (companies, products, people, dates)
   • Main concepts and themes
   • Relationships and connections
4. Question Generation (Claude-powered):
   • Generate questions matching difficulty requirements
   • Ensure diverse question types
   • Create comprehensive ground truth answers
   • Extract supporting evidence passages
5. Validation:
   • Verify evidence supports answer
   • Check answer completeness
   • Validate JSON structure
6. Output: Save to specified file with statistics

Statistics Reported

After generation:

• Total questions generated
• Questions per type breakdown
• Questions per difficulty
• Average answer length
• Average evidence passages per question
• Processing time

Requirements

• Python 3.8+
• pypdf library (auto-installed if missing)
• Anthropic API key (from environment)
• PDF files in specified directory

Notes

• For hard questions, ensures cross-document synthesis by analyzing multiple PDFs
• For easy questions, uses direct extraction with minimal inference
• Always includes 2-5 evidence passages per question
• Validates that evidence actually supports the answer
• Uses unique hash IDs for question tracking
• Compatible with RAGAs and other evaluation frameworks