# Datalad Container Run

> >

- **Type:** Skill
- **Install:** `agentstack add skill-neuroanalytics-data-science-harness-datalad-container-run`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [neuroanalytics](https://agentstack.voostack.com/s/neuroanalytics)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [neuroanalytics](https://github.com/neuroanalytics)
- **Source:** https://github.com/neuroanalytics/data-science-harness/tree/main/plugins/datalad-cli/skills/datalad-container-run

## Install

```sh
agentstack add skill-neuroanalytics-data-science-harness-datalad-container-run
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Skill: datalad-container-run

Wrap a command in `datalad containers-run` to record the container image, the command,
inputs, and outputs in the dataset history. The container image content hash is annexed,
so the full computational environment is reproducible via `datalad rerun`.

Always load `${CLAUDE_PLUGIN_ROOT}/references/container-run.md` before any container
registration or container-run step.

## Steps

1. **Identify command and container** — read `$ARGUMENTS` or extract from conversation
   context. Determine:
   - The container image (`.sif` path, Docker image name, Singularity/Apptainer URL)
   - The command to run inside the container
   If either is ambiguous or missing, ask the user to specify both before continuing.

2. **Verify DataLad context** — check whether cwd is inside a DataLad dataset:
   ```bash
   ls .datalad/
   ```
   - **Dataset found**: continue.
   - **No dataset found**: ask the user:
     > "No DataLad dataset detected. Would you like to:
     > 1. Initialize one here with `/datalad-init`
     > 2. Run the container command bare (no provenance tracking)"
     Wait for their choice.

3. **Check for unsaved changes** — run `datalad status`. If the output shows modified
   or untracked files, prompt the user to save first with `/datalad-save` or confirm
   they want to proceed anyway.

4. **Container registration check** — run `datalad containers-list` to see registered
   containers:
   - **Container already registered**: proceed to step 5.
   - **Not registered**: guide the user through `datalad containers-add`:
     a. Ask for a short name (identifier) for the container.
     b. Confirm the image URL or local `.sif` path (`--url`).
     c. Ask which runtime to use; load `${CLAUDE_PLUGIN_ROOT}/references/container-run.md`
        and show the call-format options table (Singularity, Apptainer, Docker).
     d. Construct and display the `datalad containers-add` command:
        ```
        datalad containers-add  \
          --url  \
          --call-fmt ""
        ```
     e. Execute after user confirmation. On failure, show error and stop.

5. **Gather run parameters** — ask the user (or infer from context) for:
   - **`-i` (inputs)**: files or glob patterns the command reads (omit if none)
   - **`-o` (outputs)**: files or glob patterns the command writes (required if outputs exist)
   - **`-m` (message)**: a meaningful description of what this run does (required)

6. **Construct and show the full command** — build the complete invocation and display
   it before executing:
   ```
   datalad containers-run \
     --container-name  \
     -m "" \
     [-i ...] \
     [-o ...] \
     ""
   ```
   Then ask: > "Ready to execute? (yes / edit)"

7. **Execute and report** — after user confirmation, run the constructed command.
   - On **success**: report the commit hash (includes container image hash), output files
     recorded, and that the container image and command are now in dataset history.
   - On **failure**: show error output; note that nothing was committed.

## Removing a registered container (`datalad containers-remove`)

When the user wants to deregister a container:

1. **List registered containers** to confirm the name:
   ```bash
   datalad containers-list
   ```

2. **Warn the user** — removing a container drops its registration and annexed image
   from the dataset. Runs that used this container remain in history but cannot be
   replayed (the image would need to be re-added):
   > "Removing '' will drop the container image from the dataset. Past runs
   > referencing it will no longer be replayable unless you re-add the image. Proceed?"

3. **Remove after confirmation**:
   ```bash
   datalad containers-remove 
   ```

4. **Save the change**:
   ```bash
   datalad save -m "remove container "
   ```

## Constraints

- Always load `${CLAUDE_PLUGIN_ROOT}/references/container-run.md` before any container
  registration or container-run step.
- Never call `datalad containers-run` with an unregistered container name — always verify
  with `datalad containers-list` first.
- Never skip showing the full constructed command before executing.
- Always require a meaningful `-m` message — never use empty or placeholder messages.
- For pipelines inside the container, wrap as `"bash -c 'cmd1 | cmd2'"` passed to the
  container call.
- The `--call-fmt` must contain both `{img}` and `{cmd}` placeholders — if either is
  missing, the container will be invoked incorrectly. Flag this to the user.
- For `containers-remove`: always confirm before executing and always follow with
  `datalad save` to record the removal.

## Source & license

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

- **Author:** [neuroanalytics](https://github.com/neuroanalytics)
- **Source:** [neuroanalytics/data-science-harness](https://github.com/neuroanalytics/data-science-harness)
- **License:** MIT

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

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-neuroanalytics-data-science-harness-datalad-container-run
- Seller: https://agentstack.voostack.com/s/neuroanalytics
- Browse the marketplace: https://agentstack.voostack.com/browse

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