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long-term-task

Coordinates task creation, state tracking and progress reporting for multi-agent workflows via CLI and SDK.

1|Updated Jan 30, 2026
Search Tags:#workflow#CLI#multi-agent#task-management#OpenClaw#long-term-task
One-click install
npx skills add https://github.com/xdrshjr/JR-Agent-Skills --skill long-term-task
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: long-term-task
Source: https://github.com/xdrshjr/JR-Agent-Skills/tree/main/long-term-task
Command: npx skills add https://github.com/xdrshjr/JR-Agent-Skills --skill long-term-task

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, and includes scripts (resource) components.

What problem does it solve?

Coordinating long-running tasks across multiple agents requires infrastructure to manage tasks, track progress, and coordinate communication. This Skill provides that backbone, enabling scalable collaboration without forcing agents to implement orchestration logic themselves.

Core Features & Use Cases

  • Self-contained task scheduling, state management, and progress reporting for multi-agent workflows.
  • Zero-intrusion: Agents interact via CLI or Python SDK without modifying existing code.
  • Supports multi-agent coordination for long-running processes like data pipelines, model training, or research tasks.
  • Real-world example: Run a daily HF paper study with goals and milestones; monitor progress and adjust strategy.

Quick Start

Use the long-term-task skill to create and manage a daily HF papers workflow: ltt create --work-dir "./.ltt" --name "daily-hf-papers" --goals "download paper, summarize, write notes" --schedule daily --interval 30

Frequently Asked Questions about long-term-task

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I coordinate long-running tasks across multiple agents without writing custom orchestration logic?▼

Multi-agent task coordination is handled by a self-contained backbone that manages task scheduling, state tracking, and progress reporting. Agents interact via CLI or Python SDK with zero intrusion, eliminating the need to implement custom orchestration logic.

What is the best way to schedule a daily data pipeline using a multi-agent workflow?▼

Scheduling a daily multi-agent data pipeline uses the CLI to define goals, set daily intervals, and specify a working directory. This creates a managed long-term task with automated state tracking and progress reporting across agents.

Can I integrate task management into existing agent code using a Python SDK?▼

Task management integrates into existing agent code via a Python SDK or CLI without requiring code modifications. This zero-intrusion approach allows agents to report progress and coordinate long-running processes seamlessly.

Does multi-agent task management support long-running processes like model training and research tasks?▼

Multi-agent task management supports long-running processes including model training, data pipelines, and research tasks. It provides real-time state tracking and milestone monitoring to adjust strategies during execution.

Do I need to install additional dependencies to use the CLI and SDK for task scheduling?▼

Task scheduling requires the Python requests library as its only dependency. The environment setup needs minimal installation to enable CLI commands and SDK integrations for multi-agent workflows.

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