/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/)
$ npx -y skills add awslabs/agent-plugins --skill directory-management --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/directory-management
Context preview
The summary Claude sees to decide when to auto-load this skill.
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/)
SKILL.md
directory-management.SKILL.mdname: directory-management
description: 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.
metadata:
version: "1.0.0"
Directory Management
Project Setup
Before any work begins, resolve the project name:
1. If the project name is already known from conversation context, use it. 2. 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. 3. 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:
1. 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 stepsRead more
name: directory-management description: 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. metadata: version: "1.0.0"
Directory Management
Project Setup
Before any work begins, resolve the project name:
1. If the project name is already known from conversation context, use it. 2. 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. 3. 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:
1. 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 stepsRead this in other languages: 日本語 Generative AI can make mistakes. You should consider reviewing all output and costs generated by your chosen AI model and agentic coding assistant. See AWS Responsible AI Policy.
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