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    Prompt Datasets Builder

    2

    Professional curation skill for LLM fine-tuning datasets, deduplication, and leakage auditing.

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    Prompt Datasets Builder

    Prompt Datasets Builder

    Example session with this skill installed

    Review this set of 3 prompts for a customer support bot fine-tuning task. Check for duplicates, quality issues, and suggest a JSON schema for export.

    1. How do I reset my password?
    2. I need to change my password, how?
    3. Tell me a joke.
    • Read your context and instructions
    • Compiled the prompt datasets builder

    I have analyzed the provided prompts for duplication, relevance, and quality. I detected one near-duplicate pair and one out-of-scope record.

    Prompt Dataset Curation Report

    Dataset Inventory

    FieldTypePurposeMissingnessNotes
    raw_textStringUser intent0%Primary input

    Curation Findings

    IDRecordLabelRecommendationReason
    001How do I reset my password?High QualityKeepClear, specific intent.
    002I need to change my password, how?Near DuplicateMerge/RemoveSemantic overlap with 001.
    003Tell me a joke.Out of ScopeRemoveDoes not match support bot objective.

    Dataset Schema

    {
      "id": "string",
      "prompt": "string",
      "category": "account_management",
      "intent": "password_reset",
      "quality_score": "float",
      "metadata": {
        "source": "raw_input",
        "version": "1.0.0"
      }
    }
    

    Safety & Sensitive Data

    No PII, secrets, or restricted content detected in the sample records.

    Next steps

    1. Consolidate records 001 and 002 into a single canonical "password_reset" instruction.
    2. Define a "support_functions" taxonomy to filter out generic conversational queries like jokes.
    3. Apply the provided JSON schema to your full dataset for export readiness.

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

    About this skill

    The problem

    Fine-tuning and evaluation pipelines fail when fed noisy, duplicate, or inconsistent prompt data. Manual curation is slow, error-prone, and often misses subtle train/eval leakage or safety risks.

    What it does

    • Identifies exact and near-duplicate prompt-response pairs to ensure dataset diversity.
    • Performs quality audits to flag ambiguous, incomplete, or contradictory records.
    • Detects data leakage between training and evaluation splits to prevent inflated performance metrics.
    • Scans for sensitive data, API keys, and credentials without reproducing them in the report.
    • Generates structured JSON/JSONL schemas and curation reports for immediate export readiness.

    Frameworks & tools

    Designed for LLM-ops workflows, supporting JSON, JSONL, CSV, and TSV formats for fine-tuning on platforms like OpenAI, Anthropic, or Hugging Face.

    Why this beats prompting it yourself

    Writing individual prompts to clean data often results in hallucinated examples or inconsistent labeling. This skill uses a multi-modal curation workflow that enforces strict provenance, prevents fabrication of metadata, and applies a systematic 23-point quality control process that simple prompts lack.

    Use cases

    • Cleaning raw instruction-tuning data before model training.
    • Auditing evaluation sets for leakage against training benchmarks.
    • Building consistent label taxonomies for multi-turn conversational datasets.
    • Redacting PII and secrets from proprietary prompt logs.

    Known limitations

    Does not execute dataset exports to external storage; it generates the structured content and reports for you to save. Cannot verify the factual accuracy of responses without external ground-truth data.

    Requires data to be provided in structured text formats. Cannot perform automated bulk file I/O or direct database writes. Limited by the context window for extremely large datasets.

    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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    Listed1 month ago

    What's inside

    Frequently Asked Questions

    The skill accepts JSON, JSONL, CSV, and TSV formats, ensuring seamless compatibility with popular training platforms like OpenAI, Anthropic, and Hugging Face.

    This skill utilizes a systematic 23-point quality control process and a multi-modal workflow that prevents the hallucinations and inconsistent labeling common when using basic, manual prompts for data cleaning.

    While it identifies PII, secrets, and quality issues, the skill does not verify factual accuracy against real-world data, nor does it automatically export files to external cloud storage.

    Yes, the skill is specifically designed to detect exact and near-duplicate entries across training and evaluation splits to ensure your model's performance metrics are accurate and not inflated by data leakage.

    You will receive the full Prompt Datasets Builder skill, including the specialized curation workflows, structured reporting templates, and the comprehensive 23-point quality audit logic.

    New in LLM Ops