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giuseppe-trisciuoglio/developer-kit

Comprehensive developer toolkit providing reusable skills for Java/Spring Boot, TypeScript/NestJS/React/Next.js, Python, PHP, AWS CloudFormation, AI/RAG, DevOps, and more.

89

Quality

89%

Does it follow best practices?

Impact

Pending

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Overview
Quality
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Files

devkit.lra.init.mdplugins/developer-kit-core/commands/lra/

description:
Initialize environment for long-running agent workflow (creates feature list, progress file, init.sh)
allowed-tools:
Read, Write, Edit, Bash(git:*), Bash(mkdir:*), Bash(chmod:*)
argument-hint:
[project-description]

Long-Running Agent - Initialize Environment

You are an Initializer Agent setting up the environment for a long-running coding project. Your job is to create all necessary scaffolding so that future coding agents can work effectively across multiple context windows.

Project Description

$ARGUMENTS

Your Tasks

1. Create the LRA Directory Structure

Create the .lra/ directory in the project root with the following structure:

.lra/
├── feature-list.json      # Structured list of all features
├── progress.txt           # Session-by-session progress log
└── init.sh                # Script to start the development environment

2. Create feature-list.json

Based on the project description, create a comprehensive JSON file with ALL features needed. Each feature should be atomic and testable.

Format:

{
  "project": "Project Name",
  "description": "Brief project description",
  "created_at": "ISO timestamp",
  "features": [
    {
      "id": "F001",
      "category": "core|ui|api|database|auth|testing|other",
      "priority": "critical|high|medium|low",
      "description": "Clear description of the feature",
      "acceptance_criteria": [
        "Step 1 to verify",
        "Step 2 to verify"
      ],
      "status": "pending",
      "completed_at": null,
      "notes": ""
    }
  ]
}

Guidelines:

  • Break down the project into 20-50+ atomic features minimum
  • Each feature should be completable in one coding session
  • Order features by dependency (foundational features first)
  • Use status: "pending" for all features initially
  • Include setup/configuration features
  • Include testing features

3. Create progress.txt

Initialize the progress file:

# Long-Running Agent Progress Log
# Project: [Name]
# Created: [Date]

## Session History

### Session 1 - [Date] - Initialization
- Created project scaffolding
- Generated feature list with X features
- Created init.sh script
- Initial git commit

---

4. Create init.sh

Create a shell script that future agents can run to start the development environment:

#!/bin/bash
# Long-Running Agent - Environment Initialization Script
# This script starts the development server and prepares the environment

echo "🚀 Starting Long-Running Agent Environment..."

# Add project-specific commands here:
# - Start development server
# - Run database migrations
# - Start required services
# - etc.

echo "✅ Environment ready!"

Make the script executable with chmod +x .lra/init.sh.

5. Create Initial Git Commit

After creating all files:

  1. Add all .lra/ files to git
  2. Create a commit with message: chore(lra): initialize long-running agent environment

Output

After completing all tasks, provide a summary:

  1. Number of features identified
  2. Feature breakdown by category
  3. Feature breakdown by priority
  4. Next steps for the first coding session

Execution Instructions

Agent Selection: To execute this LRA task, use the following approach:

  • Primary: Use general-purpose agent with task management and state persistence capabilities
  • Or use plan agent for complex multi-step workflows

plugins

CHANGELOG.md

context7.json

CONTRIBUTING.md

README_CN.md

README_ES.md

README_IT.md

README.md

tessl.json

tile.json