nft metadata provenance

    2

    Generate and audit NFT metadata, validate asset references, and build provenance manifests for collection launches.

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    nft metadata provenance

    Example session with this skill installed

    Audit these 3 metadata files for a collection named 'Neon Runners'. Ensure the trait 'Background' is consistent and check for missing fields. Files: 0.json, 1.json, 2.json (all provided below).

    • Read your context and instructions
    • Compiled the nft metadata provenance

    I have audited the metadata for the 'Neon Runners' collection. I identified a trait naming inconsistency in token #2 and a missing description field across the set.

    Metadata Audit Report

    Token IDStatusIssues FoundRecommendations
    0.jsonPassNoneValid ERC-721 structure.
    1.jsonWarningMissing descriptionAdd collection-wide description string.
    2.jsonFailTrait Mismatch: Bg vs BackgroundRename Bg to Background for schema parity.

    Provenance Manifest Draft

    [
  {
    "token_id": 0,
    "asset": "0.png",
    "metadata": "0.json",
    "attributes_count": 4
  },
  {
    "token_id": 1,
    "asset": "1.png",
    "metadata": "1.json",
    "attributes_count": 4
  },
  {
    "token_id": 2,
    "asset": "2.png",
    "metadata": "2.json",
    "attributes_count": 4
  }
]

    Next steps

    1. Standardize the Background trait key across all JSON files to ensure marketplace filters work correctly.
    2. Populate the description field in all files before uploading the directory to IPFS.
    3. Verify the image CID once the assets are pinned to update the image URI prefix.

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    About this skill

    The problem

    Broken NFT metadata leads to failed marketplace listings and loss of holder trust. Small errors like trait typos or CID mismatches can ruin a collection launch after the contract is already deployed.

    What it does

    • Generates standardized JSON metadata following OpenSea and ERC-721/1155 standards.
    • Audits existing metadata folders for duplicate attributes, missing image references, and schema violations.
    • Constructs deterministic provenance manifests to link token IDs to specific asset hashes.
    • Validates reveal states to ensure smooth transitions from placeholder to final metadata.
    • Identifies release blockers like broken URIs or impossible trait combinations.

    Why this beats prompting it yourself

    General LLMs often hallucinate attribute structures or fail to maintain strict consistency across 10,000 items. This skill uses a systematic audit framework to catch edge cases like filename-token ID offsets and hidden trait collisions that manual prompting overlooks.

    Use cases

    • Generating 10k metadata files from a provided trait spreadsheet.
    • Auditing a folder of JSON files before pinning to IPFS.
    • Creating a provenance hash list for smart contract deployment.
    • Validating pre-reveal metadata against post-reveal asset lists.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Listed1 month ago

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