directory-management

$npx mdskill add awslabs/agent-plugins/directory-management

Sets up project directories and resolves project naming for artifact organization.

  • Creates standard folder structure (specs/, scripts/, notebooks/, manifests/, agent_memory/) for a new project.
  • Depends on filesystem access to create directories and read existing PLAN.md files.
  • Resolves project name from context, existing projects, or by recommending a slug based on conversation.
  • Delivers a confirmed project directory with all subfolders and loads PLAN.md into context if resuming.

SKILL.md

.github/skills/directory-managementView on GitHub ↗
---
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 steps
```

More from awslabs/agent-plugins

SkillDescription
amazon-location-serviceIntegrates Amazon Location Service APIs for AWS applications. Use this skill when users want to add maps (interactive MapLibre or static images); geocode addresses to coordinates or reverse geocode coordinates to addresses; calculate routes, travel times, or service areas; find places and businesses through text search, nearby search, or autocomplete suggestions; retrieve detailed place information including hours, contacts, and addresses; monitor geographical boundaries with geofences; or track device locations. Covers authentication, SDK integration, and all Amazon Location Service capabilities.
amplify-workflow'Build and deploy full-stack web and mobile apps with AWS Amplify Gen2
aws-architecture-diagramGenerate validated AWS architecture diagrams as draw.io XML using official AWS4 icon libraries. Use this skill whenever the user wants to create, generate, or design AWS architecture diagrams, cloud infrastructure diagrams, or system design visuals. Also triggers for requests to visualize existing infrastructure from CloudFormation, CDK, or Terraform code. Supports two modes: analyze an existing codebase to auto-generate diagrams, or brainstorm interactively from scratch. Exports .drawio files with optional PNG/SVG/PDF export via draw.io desktop CLI.
aws-lambdaDesign, build, deploy, test, and debug serverless applications with AWS Lambda. Triggers on phrases like: Lambda function, event source, serverless application, API Gateway, EventBridge, Step Functions, serverless API, event-driven architecture, Lambda trigger. For deploying non-serverless apps to AWS, use deploy-on-aws plugin instead.
aws-lambda-durable-functions>
aws-lambda-managed-instances>
aws-lambda-microvms>
aws-serverless-deploymentAWS SAM and AWS CDK deployment for serverless applications. Triggers on phrases like: use SAM, SAM template, SAM init, SAM deploy, CDK serverless, CDK Lambda construct, NodejsFunction, PythonFunction, SAM and CDK together, serverless CI/CD pipeline. For general app deployment with service selection, use deploy-on-aws plugin instead.
aws-step-functionsBuild workflows with AWS Step Functions state machines using the JSONata query language. Covers Amazon States Language (ASL) structure, state types, variables, data transformation, error handling, AWS service integration, and migrating from the JSONPath to the JSONata query language.
aws-transformMigrate, modernize, and upgrade codebases to AWS. Run analysis on repos for tech debt, security vulnerabilities, and modernization opportunities. Transforms .NET Framework to .NET 8/10, mainframe COBOL to Java, VMware VMs to EC2, SQL Server to Aurora, and upgrades Java/Python/Node.js versions and AWS SDKs. Use when the user says "migrate .NET to AWS", "upgrade Java to 17/21", "modernize COBOL", "modernize mainframe", "move VMware to EC2", "convert SQL Server to Aurora", "upgrade Python version", "migrate AWS SDK", "transform this codebase", "analyze for issues", "find tech debt", "what tech debt", "security vulnerabilities", "CVEs", "what's wrong with my code", "assess my repos", "where do I start", "find what's outdated", "analyze my repos", "AWS Transform - continuous modernization", "continuous modernization" or "continuous-modernization". Don't use for infrastructure provisioning, CI/CD pipelines, or general coding tasks.