Directory management
Skill awslabs/agent-plugins/plugins/sagemaker-ai/skills/directory-management
Agent Plugins for AWS equip AI coding agents with the skills to help you architect, deploy, and operate on AWS.
npx -y skills add awslabs/agent-plugins --skill directory-managementAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
What its author says it does
Copied from the file, not written here
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/, manifests/, agent_memory/) and resolves project naming.
SKILL.md
1.7 KB, as published. Nobody here has run it
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.mdfiles in the current directory. If found, ask the user if they are resuming an existing project and load thatPLAN.mdinto 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>.ipynb
├── manifests/ # Machine-readable outputs (JSON)
└── agent_memory/ # Session persistence (git-ignored)
└── session-notes.md # Progress, artifacts, next steps