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    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+22 more

    dpp supplier evidence normalizer

    1

    Normalize messy supplier CSVs into audit-ready Digital Product Passport records with strict field-level provenance.

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    dpp supplier evidence normalizer

    dpp supplier evidence normalizer

    Example session with this skill installed

    Process the attached supplier_data_v2.csv and mapping_spec.json. Focus on material composition and recyclability claims for the 'EcoFrame' product line.

    • Read your context and instructions
    • Compiled the dpp supplier evidence
    • Generated the document

    I have normalized the supplier data for the 'EcoFrame' product line. I identified two conflicting claims regarding recycled aluminum content and mapped four verified attributes to the DPP schema.

    DPP Evidence Normalization Report

    FieldNormalized ValueSource IDStatusProvenance/Note
    Material: Aluminum[CONFLICT]CSV_Row_12, CSV_Row_45FlaggedRow 12 claims 40%, Row 45 claims 65%.
    Coating TypePowder Coat (PE)CSV_Row_14VerifiedMatches spec sheet hash sha256:e3b0c442...
    Recyclability Index0.85CSV_Row_22VerifiedUnit: Ratio (0-1).
    Repairability ScoreUNKNOWNN/AMissingNo data found in columns 'Ease of Repair' or 'Score'.
    Manufacturer IDMF-99821CSV_Row_01VerifiedPreserved original supplier ID.

    Missing Evidence Requests

    • Conflict Resolution: Provide secondary verification (ISO 14021) for recycled aluminum percentage.
    • Missing Field: 'Repairability Score' or disassembly instructions are required for the 'EcoFrame' line.
    • Documentation: Missing technical data sheet for the 'BioPolymer' sealant mentioned in the BOM.

    Next steps

    1. Resolve the aluminum content discrepancy with the supplier using the flagged Row IDs.
    2. Request the BioPolymer technical data sheet to complete the sealant provenance.
    3. Run scripts/normalize.py --final once the missing attributes are received to update the record.

    dpp-supplier-evidence-normalizer.pdf

    PDF · document

    Generated

    Example file from a real run - the skill writes it into your workspace.

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

    What you get

    Map supplier CSV exports to standardized DPP product records.Flag conflicting sustainability claims across multiple supply chain sources.Generate automated missing-evidence reports for procurement teams.Maintain immutable provenance chains for regulatory product audits.

    About this skill

    The problem

    Supplier data exports for Digital Product Passports (DPP) are usually inconsistent, messy, and riddled with conflicting claims. Manually mapping CSV fields while maintaining strict provenance for regulatory compliance is slow and prone to human error.

    What it does

    • Maps raw supplier CSV exports to standardized product records without losing original identifiers.
    • Flags conflicting claims between different data sources instead of automatically picking one.
    • Generates specific missing-evidence requests to send back to suppliers.
    • Maintains field-level provenance including document hashes and cell coordinates.
    • Distinguishes between supplied observations and verified live test results.

    Frameworks & tools

    Python (scripts/normalize.py), CSV, JSON fixtures.

    Why this beats prompting it yourself

    Standard LLM prompts tend to "hallucinate" missing data or smooth over contradictions to be helpful. This skill enforces strict evidence discipline, ensuring that unknowns and contradictions are preserved as required for audit-grade DPP records.

    Use cases

    • Processing diverse supplier sustainability exports into a unified schema.
    • Identifying gaps in technical documentation for circular economy regulations.
    • Auditing supply chain data for conflicting material composition claims.
    • Preparing clean data for Digital Product Passport registry uploads.

    Known limitations

    Does not perform OCR; PDFs and workbooks must be converted to CSV first. It does not determine regulatory applicability or legal certification status.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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    Recently published to Agensi

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    Trust & safety

    Security scanned

    Verified clean 13 days ago

    • Passed all security checks, Safe to install

    Needs access to

    Europa
    Json Schema

    Listed13 days ago

    What's inside

    Frequently Asked Questions