datalad-run

Wrap shell commands in datalad run to capture provenance.

1|Updated Feb 25, 2026

Search Tags:#automation #provenance #reproducibility #dataset #inputs #outputs #datalad

Ready to run

npx skills add https://github.com/bcmcpher/my-skills --skill datalad-run

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: datalad-run
Source: https://github.com/bcmcpher/my-skills/tree/main/plugins/datalad-cli/skills/datalad-run
Command: npx skills add https://github.com/bcmcpher/my-skills --skill datalad-run

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Wraps a command in datalad run to capture provenance, recording inputs, outputs, and the exact command used in the dataset history so analyses are reproducible and auditable.

Core Features & Use Cases

Wraps a shell command in datalad run to automatically record provenance in a DataLad dataset.
Validates dataset context, collects required inputs (-i), outputs (-o), and a meaningful commit message (-m) before execution.
Supports safety checks, dry-run capability, and explicit flags to ensure reproducible, auditable runs.

Quick Start

Provide the exact command you want to run and DataLad will wrap it with provenance and execute it.

Frequently Asked Questions about datalad-run

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I record shell command provenance in a dataset?▼

To track dataset inputs and outputs, declare them explicitly with -i and -o flags when wrapping your command. This ensures DataLad properly records all dependencies and generated files in the dataset history.

What is datalad run used for in reproducible workflows?▼

Datalad run is used to execute commands while automatically capturing provenance for reproducible workflows. It validates dataset context and enforces meaningful commit messages to create auditable analysis records.

Can I do a dry run before executing a command in DataLad?▼

Yes, you can perform a dry run before executing a command in DataLad. This capability allows you to verify explicit flags and safety checks to ensure safe, auditable runs without modifying the dataset.

How do I ensure meaningful commit messages when running dataset commands?▼

You ensure meaningful commit messages by providing a descriptive message with the -m flag when executing datalad run. The Skill enforces this requirement to maintain clear, auditable provenance records in your dataset history.

Do I need an existing DataLad dataset to track command provenance?▼

Yes, you need an existing DataLad-managed dataset to track command provenance. The Skill validates the dataset context and checks the dataset state before execution to ensure proper input and output tracking.