Agent-plugins directory-management

Manages project directory setup and artifact organization. Use when starting a new project, resuming an existing one, or when a PLAN.md needs to be associated with a project directory. Creates the project folder structure (specs/, scripts/, notebooks/) and resolves project naming.

install

source · Clone the upstream repo

git clone https://github.com/awslabs/agent-plugins

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/awslabs/agent-plugins "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/sagemaker-ai/skills/directory-management" ~/.claude/skills/awslabs-agent-plugins-directory-management && rm -rf "$T"

manifest: plugins/sagemaker-ai/skills/directory-management/SKILL.md

source content

Directory Management

Project Setup

Before any work begins, resolve the project name:

If the project name is already known from conversation context, use it.
Otherwise, scan for existing
```
*/PLAN.md
```
files in the current directory. If found, ask the user if they are resuming an existing project and load that
```
PLAN.md
```
into context.
If no existing projects are found, recommend a ≤64-char lowercase slug based on what you know from the conversation (only
```
[a-z0-9-]
```
), or ask directly if there isn't enough context. Present the recommended name and wait for user confirmation.

Once project name is resolved:

Create and/or use the
```
<experiment-name>/
```
directory using the confirmed name for storing all the artifacts

Directory Structure

When working with the agent, all generated files are organized under an project directory.

<project-name>/
├── specs/  
│   ├── PLAN.md             # Your customization plan
├── scripts/                # Generated Python scripts
│   ├── <project-name>_transform_fn.py
└── notebooks/              # Generated Jupyter notebooks
    ├── <project-name>_training.ipynb