ai dataset evaluation factory

    1

    Generate structured, high-quality datasets for AI evaluation, RAG testing, and model fine-tuning.

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    ai dataset evaluation factory

    Example session with this skill installed

    Generate a JSONL dataset for evaluating a travel agent's tool-calling accuracy. Include 5 examples covering hotel booking, flight status, and ambiguous location queries.

    • Read your context and instructions
    • Compiled the ai dataset evaluation

    I have generated a structured evaluation dataset for a travel agent's tool-calling capabilities. The set focuses on parameter extraction, handling ambiguity, and API constraint adherence.

    Travel Agent Tool-Calling Benchmark

    {"id": "ta_001", "category": "hotel_booking", "difficulty": "easy", "input": "Book a room at the Hilton in London for two nights starting tomorrow.", "expected_tool": "book_hotel", "expected_parameters": {"hotel_name": "Hilton", "location": "London", "duration_nights": 2, "start_date": "2024-05-21"}, "criteria": "Exact match for duration and hotel name."}
{"id": "ta_002", "category": "flight_status", "difficulty": "medium", "input": "Is UA242 on time?", "expected_tool": "get_flight_status", "expected_parameters": {"flight_number": "UA242"}, "criteria": "Extraction of alphanumeric flight code."}
{"id": "ta_003", "category": "ambiguity_resolution", "difficulty": "hard", "input": "Find me a flight to Springfield.", "expected_tool": "request_clarification", "expected_parameters": {"missing_field": "location_state", "context": "Multiple Springfields in US"}, "criteria": "Agent must identify ambiguity rather than picking a default location."}
{"id": "ta_004", "category": "hotel_booking", "difficulty": "medium", "input": "I need a pet-friendly room in Paris for under $200.", "expected_tool": "search_hotels", "expected_parameters": {"location": "Paris", "max_price": 200, "amenities": ["pet-friendly"]}, "criteria": "Correct mapping of 'under $200' to max_price filter."}
{"id": "ta_005", "category": "adversarial", "difficulty": "hard", "input": "Book a flight to Mars for next Tuesday.", "expected_tool": "handle_error", "expected_parameters": {"error_type": "invalid_destination", "message": "Destination outside of supported terrestrial regions"}, "criteria": "Graceful failure for out-of-bounds requests."}

    Quality Audit

    • Balance: 1 easy, 2 medium, 2 hard cases.
    • Coverage: Covers standard booking, status checks, ambiguity, and out-of-scope requests.
    • Leakage: No overlap between flight and hotel parameter schemas.

    Next steps

    1. Import this JSONL file into your evaluation framework (e.g., Promptfoo or LangSmith).
    2. Map the expected_tool and expected_parameters to your specific agent function names.
    3. Run a batch test to calculate the baseline Pass@1 accuracy.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Manual data labeling is slow and prone to human bias, making it difficult to build reliable benchmarks for AI systems. This skill generates structured, balanced datasets designed specifically for AI evaluation, model training, and RAG testing.

    What it does

    • Schema definition establishes a clear structure for evaluation objectives and data types before generation.
    • Taxonomy creation builds a balanced mix of positive, negative, edge, and adversarial cases.
    • Example generation produces diverse, non-repetitive records tailored to specific system behaviors.
    • Metadata tagging attaches expected outputs, evidence requirements, and evaluation criteria to every entry.
    • Quality auditing checks for semantic duplicates, label leakage, and malformed records to ensure data integrity.

    How it works

    1. Define the evaluation objective, target system, and required schema.
    2. Architect a taxonomy that covers the full spectrum of difficulty levels and edge cases.
    3. Generate the data records, ensuring clear separation between evidence, assumptions, and inference.
    4. Export the final dataset in a structured format like JSONL, CSV, or Markdown.

    Frameworks & tools

    This tool works with common AI engineering workflows including RAG evaluation frameworks, tool-calling benchmarks, and fine-tuning pipelines. It outputs data compatible with formats required by Hugging Face, LangSmith, or custom evaluation scripts.

    Why this beats prompting it yourself

    Standard prompting often results in semantic drift and repetitive examples that fail to stress-test an agent. This skill enforces a rigorous taxonomy and quality audit, ensuring your evaluation covers adversarial paths and edge cases that simple prompts miss.

    Use cases

    • Generate RAG retrieval evaluation sets with grounded evidence and distractors.
    • Create tool-calling test suites with ambiguous inputs and parameter constraints.
    • Build preference data sets for RLHF or alignment training.
    • Construct adversarial benchmarks to test model safety and guardrail limits.

    Known limitations

    The skill does not perform live execution of code or validation of generated test cases. It cannot bridge missing domain knowledge without provided source material or external research tools.

    Does not execute code or validate generated tests. Requires external source material for grounded evidence. Synthetic data must be verified by domain experts before production use.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

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    3. 3

      Ask for what you need

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    What's inside

    Frequently Asked Questions

    This skill automates the creation of diverse, structured datasets—including edge cases and adversarial scenarios—that are essential for testing RAG systems, tool-calling accuracy, and fine-tuning model performance without the slow pace of manual labeling.

    The skill supports exports in standard formats like JSONL, CSV, and Markdown, making it fully compatible with popular industry tools such as LangSmith, Hugging Face, and custom Python-based evaluation scripts.

    While the skill excels at generating structured logic, it relies on your provided source material or schemas; it does not perform external live research or execute code to verify the real-world accuracy of the generated facts.

    You receive the full skill logic for defining schemas, building taxonomies, and generating records, along with documentation on how to export these datasets for use in your specific fine-tuning or testing pipelines.

    Yes, the skill is designed to build a specific taxonomy that includes positive and negative samples, as well as intentional 'distractors' to ensure your RAG retrieval system is truly being stress-tested for precision.

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