New Research: Supply Chain Attack on Axios Pulls Malicious Dependency from npm.Details →
Socket
Book a DemoSign in
Socket

@dimples/lra

Package Overview
Dependencies
Maintainers
1
Versions
8
Alerts
File Explorer

Advanced tools

Socket logo

Install Socket

Detect and block malicious and high-risk dependencies

Install

@dimples/lra

CLI tool for managing long-running AI agent projects across multiple sessions. Track features, progress, and state persistence.

latest
Source
npmnpm
Version
1.4.0
Version published
Maintainers
1
Created
Source

@dimples/lra

Long-Running Agent CLI

Manage complex AI projects across multiple sessions

npm version License: MIT Node.js Version

English · 中文

Overview

A CLI tool for managing long-running AI agent projects. When building complex applications with AI assistants like Claude Code, context windows can fill up before the project is complete. LRA provides structured state management so AI agents can seamlessly continue work across multiple sessions.

Key Features:

  • 📋 Feature Tracking - Maintain a structured list of features with priorities
  • 📊 Progress Management - Track completion status across sessions
  • 🔄 State Persistence - JSON-based state that survives context resets
  • 🤝 AI Integration - Generates .claude/CLAUDE.md for seamless Claude Code integration
  • 📝 Git Integration - Automatic commits with progress tracking

Installation

# Quick use (recommended)
npx @dimples/lra init my-project

# Or install globally
npm install -g @dimples/lra
lra init my-project

Quick Start

# 1. Initialize a new project
npx @dimples/lra init my-app --type web
cd my-app

# 2. Add features to track
npx @dimples/lra add "User authentication" --priority critical
npx @dimples/lra add "Dashboard view" --priority high
npx @dimples/lra add "Settings page" --priority medium

# 3. Check status
npx @dimples/lra status

# 4. Get next feature to work on
npx @dimples/lra next

# 5. After AI completes a feature
npx @dimples/lra done feat-001
npx @dimples/lra commit feat-001

Commands

CommandDescription
init [name]Initialize a new LRA project
statusShow project progress and statistics
add <description>Add a new feature
nextGet the next pending feature (by priority)
done <feature-id>Mark a feature as completed
commit [feature-id]Commit progress to git
listList all features
exportExport project state

init [name]

npx @dimples/lra init my-project --type web

Options:

  • -t, --type <type> - Project type: web, api, cli, library (default: web)
  • -d, --dir <directory> - Target directory (default: .)

add <description>

npx @dimples/lra add "User login" --priority critical --steps "Open login page" "Enter credentials" "Submit"

Options:

  • -p, --priority <priority> - critical, high, medium, low (default: medium)
  • -c, --category <category> - functional, style, performance, security
  • -s, --steps <steps...> - Test steps for verification

status

npx @dimples/lra status
npx @dimples/lra status --json  # Machine-readable output

next

Returns the highest-priority pending feature.

npx @dimples/lra next
npx @dimples/lra next --json  # Machine-readable output

Project Structure

my-project/
├── .agent/
│   ├── features.json      # Feature list (the "memory")
│   ├── progress.md        # Session history
│   └── sessions/          # Detailed session logs
├── .claude/
│   └── CLAUDE.md          # Instructions for Claude Code
├── init.sh                # Development environment script
├── app_spec.txt           # Application specification
└── [your project files]

How It Works

┌─────────────────────────────────────────────────────────────┐
│                    Session Workflow                          │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  Session 1          Session 2          Session 3            │
│  ┌──────────┐      ┌──────────┐      ┌──────────┐          │
│  │   AI     │      │   AI     │      │   AI     │          │
│  │ (fresh)  │      │ (fresh)  │      │ (fresh)  │          │
│  └────┬─────┘      └────┬─────┘      └────┬─────┘          │
│       │                 │                 │                 │
│       ▼                 ▼                 ▼                 │
│  ┌─────────────────────────────────────────────────┐       │
│  │              .agent/features.json                │       │
│  │           (Persistent State/Memory)              │       │
│  └─────────────────────────────────────────────────┘       │
│                                                             │
│  Each session:                                              │
│  1. Read features.json → Know current state                 │
│  2. Work on features → Implement code                       │
│  3. Mark done → Update state                                │
│  4. Commit → Persist to git                                 │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Integration with Claude Code

When you run lra init, it automatically creates .claude/CLAUDE.md with instructions for Claude Code. Every session, Claude will:

  • Read .agent/features.json to understand project state
  • Read .agent/progress.md to see session history
  • Call lra next to get the next feature
  • Implement and test the feature
  • Call lra done and lra commit to save progress

Core Principles

  • Immutable Feature List - Features can only be marked done, never removed
  • Incremental Progress - One feature per session
  • Verification First - Test before marking done
  • State Synchronization - Always commit after changes

Why LRA?

When building complex applications with AI:

ProblemSolution
Context window fills upFeatures tracked in JSON file
AI "forgets" previous workProgress persisted across sessions
AI declares done too earlyStructured feature list prevents this
AI tries to do too much at onceOne feature at a time

License

MIT © dimple-smile

概述

跨多个会话管理复杂的 AI 项目

概述

一个用于管理长运行 AI Agent 项目的 CLI 工具。当使用 Claude Code 等 AI 助手构建复杂应用时,上下文窗口可能在项目完成前就被填满。LRA 提供结构化的状态管理,让 AI Agent 可以在多个会话中无缝继续工作。

核心功能:

  • 📋 功能追踪 - 维护带有优先级的结构化功能列表
  • 📊 进度管理 - 跨会话追踪完成状态
  • 🔄 状态持久化 - 基于 JSON 的状态,不受上下文重置影响
  • 🤝 AI 集成 - 自动生成 .claude/CLAUDE.md 与 Claude Code 无缝集成
  • 📝 Git 集成 - 自动提交并追踪进度

安装

# 快速使用(推荐)
npx @dimples/lra init my-project

# 或全局安装
npm install -g @dimples/lra
lra init my-project

快速开始

# 1. 初始化新项目
npx @dimples/lra init my-app --type web
cd my-app

# 2. 添加要追踪的功能
npx @dimples/lra add "用户认证" --priority critical
npx @dimples/lra add "仪表盘视图" --priority high
npx @dimples/lra add "设置页面" --priority medium

# 3. 查看状态
npx @dimples/lra status

# 4. 获取下一个要工作的功能
npx @dimples/lra next

# 5. AI 完成功能后
npx @dimples/lra done feat-001
npx @dimples/lra commit feat-001

许可证

MIT © dimple-smile

Keywords

ai

FAQs

Package last updated on 14 Feb 2026

Did you know?

Socket

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install

Related posts