A comprehensive auditing tool for Data Version Control (DVC) pipelines to ensure reproducibility, storage efficiency, and MLOps best practices.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install dvc-pipeline-auditor
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 dvc-pipeline-auditor 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 DVC Pipeline Auditor?
The DVC Pipeline Auditor is a specialized utility designed to act as a senior ML engineer, scrutinizing your data versioning and pipeline infrastructure. It systematically reviews dvc.yaml definitions, .dvc tracking metadata, and remote storage configurations to guarantee that your machine learning workflows are both robust and reproducible. By leveraging this tool within the ecosystem of Openclaw Skills, developers can maintain high-integrity data pipelines without manual oversight and ensure that every stage of the machine learning lifecycle is correctly versioned and tracked.
DVC Pipeline Auditor Use Cases
- Reviewing DVC setup for new machine learning projects.
- Auditing data pipelines for missing dependencies or circular links.
- Troubleshooting DVC remote storage authentication and push/pull issues.
- Identifying large files that are bloating Git history instead of being tracked by DVC.
- Verifying reproducibility by checking environment pinning and random seed consistency.
How DVC Pipeline Auditor Works
- Discover the DVC project structure by scanning for .dvc directories, dvc.yaml files, and lock files.
- Audit individual pipeline stages to ensure scripts, parameters, and outputs are correctly mapped.
- Validate the dependency graph to detect orphan stages or implicit dependencies that bypass DVC tracking.
- Verify lock file integrity to detect staleness where results don't match the current codebase.
- Analyze .dvc file tracking to find hash mismatches or large untracked data files.
- Review remote storage configurations for proper authentication and backup status.
- Generate a final report with a health score and actionable remediation steps.
DVC Pipeline Auditor Setup
To initialize the DVC Pipeline Auditor, ensure you are in the root directory of your project. Run the following command to begin the audit:
# Audit the current project directory
# The agent will look for .dvc/ and dvc.yaml
find /path/to/project -name ".dvc" -type d
You may also need to provide access to your requirements.txt or source code directory for a full environment and parameter analysis as part of the Openclaw Skills workflow. Point the agent at your project root where the .dvc directory resides for the most accurate results.
DVC Pipeline Auditor Data Schema & Taxonomy
The auditor organizes findings into a structured report covering multiple technical domains:
| Category | Metrics Tracked |
|---|---|
| Pipeline Stages | Deps, Outs, Params, Metrics, Plots |
| Integrity | Lock hashes, Git tracking, .dvc metadata |
| Storage | Hash mismatches, Large file detection, Remote auth |
| Reproducibility | Python versions, Pinned dependencies, Random seeds |
It generates a final health score (0-100) with a categorized breakdown of critical issues and high-priority fixes to maintain the quality standards of Openclaw Skills.
DVC Pipeline Auditor Advanced Features
- Automated staleness detection between dvc.lock and source code.
- Suggestion of optimized data formats like Parquet for significant storage efficiency gains.
- Detection of hardcoded hyperparameters within Python source scripts that should be moved to params.yaml.
- Verification of remote disaster recovery status by checking backup remote push history.
- Seamless integration with other MLOps-focused Openclaw Skills for end-to-end workflow monitoring.
SKILL.md
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