A background reporting system that prevents long-running AI tasks from stalling by providing mandatory, timed progress updates.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install longtask-progress
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install longtask-progress using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is LongTask Progress?
LongTask Progress is a critical utility within the Openclaw Skills collection designed to maintain task continuity during extended operations. It addresses the common challenge where AI agents might lose focus or appear unresponsive during time-intensive workflows such as article writing, batch image generation, or deep data analysis. By implementing a heartbeat-style reporting mechanism, it ensures the system remains active and the user remains informed.
This skill operates by running a background thread that triggers automated status reports at predefined intervals. This architecture ensures that even if the primary task logic is heavy, the progress monitoring remains fluid and non-blocking. It provides a bridge between the developer's execution logic and the need for persistent feedback in automated environments.
LongTask Progress Use Cases
- Tracking multi-chapter content creation and humanization workflows.
- Monitoring batch file downloads or large-scale media processing.
- Managing long-duration API polling and asynchronous service waiting periods.
- Maintaining execution context for AI agents during complex, multi-stage coding tasks.
How LongTask Progress Works
- The user initializes the reporter by defining the task name, the total expected steps, and the reporting interval.
- A background daemon thread is spawned to handle the timing logic without blocking the main execution path.
- As the task progresses, manual step updates are logged to provide granular completion details.
- If the main task exceeds the interval time without a manual update, a forced report is automatically generated to confirm the process is still alive.
- Upon task completion, the skill generates a final summary and safely terminates the monitoring thread.
LongTask Progress Setup
To start using this utility from the Openclaw Skills repository, import the module into your Python environment as follows:
# Ensure the skill path is in your system path
import sys
sys.path.insert(0, '~/.openclaw/workspace-bibi/skills/longtask_progress')
from longtask_progress import LongTaskProgress, track_progress
LongTask Progress Data Schema & Taxonomy
The skill organizes reporting data using a structured metadata approach. It tracks both temporal and logical progress milestones.
| Parameter | Type | Description |
|---|---|---|
| task_name | string | Unique identifier for the monitored operation |
| total_steps | integer | Total milestones used to calculate the percentage completion |
| interval | integer | The frequency of forced reports in seconds (default 300s) |
| elapsed_time | float | Total time spent on the task since start |
| status | string | The current state or last manual message provided to the reporter |
LongTask Progress Advanced Features
- Custom Callback Integration: Route progress data to external logging systems, dashboards, or message queues.
- Hierarchical Nesting: Support for multi-level task tracking, allowing sub-tasks to report independently within a parent operation.
- Thread-Safe Execution: Built using threading.Timer to ensure safe operation within concurrent or asynchronous Python environments.
- Lifecycle Management: Automatic resource cleanup through context managers and decorators to prevent orphaned background threads.
SKILL.md
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