A specialized data integrity skill that standardizes raw records from multiple sources to ensure accurate identity resolution and enrichment.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install data-normalization
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install data-normalization using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Data-Normalization (Artledger)?
This skill acts as a critical preprocessing layer for AI agents working with diverse data sources such as blockchains, marketplaces, and auction houses. By implementing strict normalization logic for artist names, wallet addresses, and financial metrics, it prevents the common pitfalls of data silos and identity mismatches. Using this within Openclaw Skills ensures that every piece of incoming data is formatted to a universal standard before it hits your production database.
The primary purpose is to act as a gatekeeper in the data pipeline, ensuring that downstream processes like entity resolution and DB2 writes receive high-quality, sanitized information. It handles complex tasks like Ethereum checksumming, ISO 8601 date conversion, and social handle stripping, making it an essential tool for developers building reliable data-driven agents.
Data-Normalization (Artledger) Use Cases
- Preprocessing raw database records before performing entity resolution or enrichment.
- Standardizing cross-chain wallet addresses for Ethereum, Tezos, Solana, and Bitcoin Ordinals.
- Normalizing auction house sales data and hammer prices into ISO-compliant formats.
- Stripping social media handles and platform-specific URLs to create uniform identifiers.
- Validating date timestamps and currency amounts to catch data entry errors before they propagate.
How Data-Normalization (Artledger) Works
- The agent triggers the normalization skill whenever raw DB1 records are prepared for enrichment.
- Input data is parsed through specific logic gates tailored for names, wallets, dates, prices, and social handles.
- The skill applies transformation rules, such as converting names to Title Case or checksumming blockchain wallet addresses.
- Specific data quality flags are assigned to records that appear suspicious, such as common names or future-dated transactions.
- A comprehensive normalization report is generated, including arrays of fields changed, flagged, or skipped.
- The record is assigned a pass/fail boolean, determining if it is safe to proceed to the entity-resolution phase.
Data-Normalization (Artledger) Setup
To integrate this skill into your workflow, ensure it is included in your Openclaw Skills workspace configuration. You can initialize the environment using the following command:
npm install @openclaw/skill-data-normalization
# Or define it in your agent's manifest
openclaw skill add data-normalization
Configure your environment variables to specify the preferred currency conversion API if price normalization is required.
Data-Normalization (Artledger) Data Schema & Taxonomy
| Field | Type | Description |
|---|---|---|
fields_normalized |
Array | List of specific keys that underwent transformation. |
fields_flagged |
Array | Contains keys with quality issues (e.g., common_name, malformed_wallet). |
normalization_passed |
Boolean | A critical status flag for the enrichment pipeline. |
original_amount |
String/Float | The raw price as recorded in the source data. |
usd_equivalent |
Float | The normalized value for cross-platform comparison. |
Data-Normalization (Artledger) Advanced Features
- Automated handle identification using pattern matching on name fields.
- Multi-chain wallet validation logic that respects case-sensitivity rules for Solana.
- Specialized auction data handling to distinguish between hammer prices and buyer premiums.
- Low-quality record weighting to protect downstream model accuracy from noisy data.
- Non-destructive processing architecture that preserves raw DB1 provenance for auditing.
SKILL.md
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