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    Prompt Dataset Generator

    1

    Generate, clean, and augment structured prompt datasets for LLM training and evaluation.

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    Prompt Dataset Generator

    Prompt Dataset Generator

    Example session with this skill installed

    Create 5 instruction-tuning records in JSONL for a customer support agent. Categories: billing, technical, account. Vary difficulty from easy to hard.

    • Read your context and instructions
    • Compiled the prompt dataset generator

    I have generated 5 diverse instruction-tuning records for customer support, covering billing, technical, and account categories across varying difficulty levels.

    Customer Support Dataset

    {"id": "CS_001", "instruction": "Explain the late fee policy to a customer.", "input": "Customer: Why was I charged an extra $15 this month?", "output": "Our policy includes a $15 late fee for payments received more than 3 days after the due date. You can avoid this by setting up autopay in your settings.", "category": "billing", "difficulty": "easy"}
    {"id": "CS_002", "instruction": "Troubleshoot a synchronization error between the mobile app and desktop client.", "input": "User reports that their notes are not appearing on their phone after saving on their PC.", "output": "First, verify both devices are on the same version. Then, check if 'Cloud Sync' is toggled on in the mobile settings under 'Account > Data'. If the issue persists, clear the app cache.", "category": "technical", "difficulty": "hard"}
    {"id": "CS_003", "instruction": "Process a request to update an email address.", "input": "I need to change my login email from old@mail.com to new@mail.com.", "output": "To update your email, go to Profile > Security > Update Email. You will receive a verification link at your new address to confirm the change.", "category": "account", "difficulty": "easy"}
    {"id": "CS_004", "instruction": "Handle a disputed charge where the customer claims they already cancelled.", "input": "I cancelled my trial last week but was still billed for a full month.", "output": "I apologize for the oversight. I have verified your cancellation date of Oct 14th. I am processing a full refund for the charge, which should appear in 3-5 business days.", "category": "billing", "difficulty": "medium"}
    {"id": "CS_005", "instruction": "Diagnose a recurring login loop issue.", "input": "Every time I enter my 2FA code, the page just refreshes back to the login screen.", "output": "This usually indicates a browser cookie conflict. Please clear your browser cookies for our domain or try opening the site in an incognito window to bypass the loop.", "category": "technical", "difficulty": "medium"}
    

    Quality Summary

    • Records processed: 5 created.
    • Categories covered: billing (2), technical (2), account (1).
    • Duplicate status: 0 duplicates identified; high lexical diversity.
    • Schema status: Validated JSONL format.

    Next steps

    1. Review the hard technical record for domain-specific accuracy.
    2. Export the JSONL to your local environment for model fine-tuning.
    3. Request 50+ additional records to improve class balance for the 'account' category.

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

    About this skill

    The problem

    Manually creating diverse datasets for model fine-tuning or evaluation is slow and prone to repetition. Developers often end up with imbalanced labels, inconsistent schemas, or near-duplicate records that degrade model performance.

    What it does

    • Generates structured datasets for instruction tuning, classification, and evaluation.
    • Augments existing seed examples by varying wording and context while preserving intent.
    • Cleans up raw data by identifying duplicates, repairing schemas, and flagging ambiguities.
    • Performs quality checks on label consistency, class balance, and JSON/JSONL structure.

    Frameworks & tools

    Generates outputs compatible with Hugging Face Datasets, PyTorch, and TensorFlow. Supports JSON, JSONL, and custom CSV/Markdown schemas.

    Why this beats prompting it yourself

    Standard LLM prompts often produce repetitive or "hallucinated" data structures. This skill enforces strict schema validation and applies specific diversification modes to ensure your training data covers edge cases and maintains class balance without manual oversight.

    Use cases

    • Generate 100+ instruction-tuning pairs for a specific domain like legal or medical.
    • Build a balanced sentiment analysis dataset with specific custom labels.
    • Create an evaluation suite to test a model's failure modes in complex reasoning.
    • Normalize and de-duplicate a messy collection of scraped prompt ideas.

    Known limitations

    Cannot provide statistical representativeness or quality metrics without external evidence. Does not support generation of malicious or unauthorized security datasets.

    Does not provide statistical evidence for quality metrics. Will not generate datasets for malicious purposes or credential theft. Limited to text-based dataset generation and cleanup.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Recently published to Agensi

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    Security scan

    Security scan passed 1 month ago

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    Listed1 month ago

    What's inside

    Frequently Asked Questions

    This skill automates the creation and refinement of training data by enforcing strict schema validation and applying diversification techniques to prevent the repetitive patterns common in manual LLM prompting.

    The generator produces structured data in JSON, JSONL, CSV, and Markdown formats, making it natively compatible with Hugging Face, PyTorch, TensorFlow, and most fine-tuning pipelines.

    Yes, the skill includes a dedicated augmentation mode that takes your existing seed examples and generates high-variance versions while maintaining the original intent and label integrity.

    The skill includes built-in quality checks for JSON/JSONL schema validity, class balance across labels, and the identification of near-duplicate records to ensure high-quality training inputs.

    You receive the full Prompt Dataset Generator skill, including the logic for instruction tuning, classification building, and data cleaning, along with documentation on supported schemas and best practices.

    New in LLM Ops