Automates the reconciliation of multiple data sources using stable identifiers to produce detailed exception reports and prevent silent pipeline failures.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install data-reconciliation-exceptions
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-reconciliation-exceptions 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 Reconciliation & Exceptions Reporting?
This skill provides a robust framework for comparing datasets, such as payroll exports and compliance registers, using high-integrity identifiers like Pay Numbers and document IDs. It is designed to eliminate the risk of silent failures by implementing strict gates and categorical exception reporting. By utilizing Openclaw Skills for data reconciliation, developers and analysts can ensure that every record is accounted for and categorized with clear reason codes for further investigation.
The tool focuses on deterministic matching logic rather than open-ended fuzzy matching, making it ideal for financial, HR, or compliance-heavy environments where precision is non-negotiable. It synthesizes complex datasets into actionable exception reports that highlight missing records, field mismatches, and duplicates.
Data Reconciliation & Exceptions Reporting Use Cases
- Comparing payroll exports against compliance registers to identify discrepancies in real-time.
- Generating weekly variance reports for missing records, duplicates, or date gaps across multiple system exports.
- Building 'no silent failure' check gates that halt data pipelines if record counts fall outside specified tolerances.
- Designing data quality scorecards with automated red flags for missing or invalid identifiers.
How Data Reconciliation & Exceptions Reporting Works
- Identify and prioritize primary and secondary identifiers, such as Pay Number, Driver Card, or Driving Licence.
- Normalize data columns by trimming whitespace, standardizing case, and stripping punctuation from document numbers.
- Validate keys to flag blanks, invalid formats, or internal duplicates within each source.
- Execute exact joins on the primary key, followed by secondary joins only for the remaining unmatched items.
- Categorize all exceptions into standardized reason codes like MISSING_IN_B, MISMATCH, or DUPLICATE_KEY.
- Apply no silent failure gates to verify that unmatched rates and duplicate spikes stay within defined thresholds.
Data Reconciliation & Exceptions Reporting Setup
To integrate this skill into your workflow, ensure your datasets are available in CSV or XLSX format and define your identifier hierarchy.
# Initialize the reconciliation workflow using Openclaw Skills
openclaw install data-reconciliation-exceptions
Specify which fields must match (e.g., Name, Expiry Date) and set your optional normalization rules or threshold gates in your configuration file.
Data Reconciliation & Exceptions Reporting Data Schema & Taxonomy
The skill generates a structured exception report in CSV format to ensure clear audit trails.
| Column | Description |
|---|---|
| exception_type | The category of the error (e.g., MISMATCH, MISSING_IN_A) |
| reason | A descriptive code for the specific failure |
| source_a_id | The primary identifier from the first source |
| source_b_id | The primary identifier from the second source |
| pay_number | The stable Pay Number used for the join |
| field | The specific field where a mismatch was detected |
| source_a_value | The value present in the first data source |
| source_b_value | The value present in the second data source |
Data Reconciliation & Exceptions Reporting Advanced Features
- Multi-layered join strategy that allows for fallback to secondary identifiers when the primary key is missing.
- Customizable no silent failure gates with configurable percentage-based tolerances to stop pipelines on anomalies.
- Automated generation of reason codes for streamlined manual review and faster data auditing.
- Deterministic matching logic that prioritizes data integrity and strictly avoids unauthorized fuzzy matching.
- Seamless integration with Openclaw Skills for automated data quality scorecards and red-flagging.
SKILL.md
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