A production-grade framework for managing batch processing, task queues, and resilient long-running orchestrations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install batch-processing-patterns
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 batch-processing-patterns 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 Batch Processing Patterns?
This skill provides a comprehensive architecture for handling batch operations and long-duration tasks within AI agent environments. It focuses on production-level reliability, covering essential patterns such as dynamic concurrency scheduling, circuit breakers, and intelligent interruption recovery. By implementing these Openclaw Skills, developers can ensure their agents handle high-volume file processing or API-intensive workflows without resource exhaustion.
The framework is designed to bridge the gap between simple loops and robust enterprise task management. It integrates seamlessly into existing TypeScript/JavaScript projects, providing standardized methods for progress reporting, error classification, and anti-scraping strategies for network-heavy operations.
Batch Processing Patterns Use Cases
- High-volume media processing including batch transcoding, image compression, and automated watermarking.
- Managing remote AI task polling for services like video generation or long-form speech synthesis.
- Resilient web scraping and batch HTTP requests requiring sophisticated rate limiting and risk control.
- Background task orchestration with checkpoint support for resuming interrupted operations.
How Batch Processing Patterns Works
- The task queue is initialized with an Adaptive Scheduler that dynamically scales worker count based on task latency.
- Each item in the queue is processed individually, triggering granular progress updates for both item-level and batch-level visibility.
- An AbortController monitors for interruption signals between task items, ensuring immediate and clean process termination when requested.
- A Circuit Breaker monitors failure rates; if consecutive errors exceed a threshold, it trips to pause the queue and prevent resource waste.
- For remote service integration, a recursive polling mechanism with exponential backoff manages status updates until tasks reach a terminal state.
Batch Processing Patterns Setup
To integrate these patterns into your project, ensure you have a TypeScript environment ready. Use the following structure to initialize the adaptive scheduler:
npm install @openclaw/task-orchestrator
Configure your task limits based on the workload type:
// Example for CPU intensive tasks
const scheduler = new AdaptiveScheduler(1, CPU_CORES, 3000);
Batch Processing Patterns Data Schema & Taxonomy
The skill organizes task data and metadata using a structured taxonomy to ensure consistent reporting and recovery:
| Schema Type | Attributes | Description |
|---|---|---|
| BatchProgress | current, total, percentage | Tracks the overall lifecycle of the batch operation. |
| TaskStatus | pending, processing, completed, failed | Defines the current state of an individual queue item. |
| ErrorClassification | transient, fatal, rate_limit | Categorizes errors to determine if a retry or halt is necessary. |
| SkipRegistry | path, reason | Maintains a record of completed items for interruption recovery. |
Batch Processing Patterns Advanced Features
- Adaptive Concurrency Pool that automatically optimizes worker counts for CPU-bound or I/O-bound tasks.
- Intelligent Circuit Breaker with auto-reset capabilities to protect downstream services from overload.
- Exponential Backoff with Jitter to handle 429 Too Many Requests and transient network instability.
- Anti-Risk Protection Layer featuring User-Agent rotation, domain-specific throttling, and response content verification.
- Checkpoint Persistence allowing agents to resume large-scale batches from the exact point of interruption.
SKILL.md
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