Directory management
Skill awslabs/agent-plugins/plugins/sagemaker-ai/skills/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/, manifests/, agent_memory/) and resolves project naming.From its SKILL.md
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.
SKILL.md
1.7 KB, 297 tokens by cl100k_base, 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
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.