Datalad Init

Skill: datalad-init

Create a new DataLad dataset following YODA principles for reproducible, provenance-tracked local analysis projects. The -c yoda configuration is always applied — it sets up the correct directory layout and .gitattributes rules.

Steps

1. Determine target path — read $ARGUMENTS. If a path is given, use it. If empty,

use the current working directory. Show the resolved absolute path and ask the user to confirm before proceeding.

1. Pre-flight checks — before running any command:

• Check for an existing .datalad/ directory at the target path. If found, stop:

> "A DataLad dataset already exists at `. Re-initializing would overwrite > dataset configuration. Run datalad status` to check its state."

• Check whether .git/annex/ already exists at the target path:

``bash git annex config annex.backend 2>/dev/null ` If it returns MD5E`, warn: > "The existing annex uses the legacy MD5E backend. Migrating backends after the > fact is complex. Consider initializing a fresh dataset at a new path and > re-ingesting data." Stop unless the user explicitly asks to continue anyway.

• Check whether the target path is inside a plain git repository (not a datalad dataset):

run git rev-parse --git-dir from the target path. If a .git/ is found but no .datalad/, warn: > "Target path is inside a plain git repository. Creating a DataLad dataset here > will nest it inside an untracked git repo, which can cause unexpected behavior. > Consider initializing at the git repo root, or outside the git tree. Proceed anyway?" Wait for user confirmation before continuing.

1. Create the dataset — run:

`` datalad create -c yoda ` Show the command before executing. Report the full output. Optionally suggest adding --annex-backend SHA256E (recommended; avoids the legacy MD5E backend which causes compatibility issues with some special remotes): ` datalad create -c yoda --annex-backend SHA256E ``

1. Report the YODA structure — after creation, display what was created:

`` / ├── code/ ← version-controlled scripts and notebooks (annexed: never) ├── outputs/ ← results produced by datalad run (annexed: everything) ├── inputs/ ← input data, ideally linked as subdatasets (annexed: everything) └── README.md ← dataset description ` Explain each directory's role briefly (see ${CLAUDEPLUGINROOT}/../references/yoda-layout.md` for full detail).

1. Explain YODA principles — briefly state the three principles:

• P1 — Everything is a dataset: input data should be linked as subdatasets, not copied
• P2 — Record data origins: use datalad download-url or datalad clone with provenance
• P3 — Never modify a dataset you didn't create: work only in outputs/ and code/

1. Suggest next steps — close with:

• Put scripts and analysis code in code/
• Link input data as a subdataset: datalad clone inputs/
• Use datalad run to execute scripts so commands and outputs are recorded
• Use datalad save -m "..." to record code changes

Constraints

• Never run datalad create without explicit path confirmation from the user.
• Never re-initialize an existing DataLad dataset — always stop and explain.
• Always use -c yoda — never create a bare dataset without the YODA configuration.
• Always warn when the target path is inside a plain git repo, and require explicit

confirmation before proceeding.

• Load ${CLAUDE_PLUGIN_ROOT}/../references/yoda-layout.md if the user asks for more detail

about YODA conventions, .gitattributes behavior, or subdataset patterns.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: neuroanalytics
• Source: neuroanalytics/data-science-harness
• License: MIT

Install and usage instructions live in the source repository linked above.