A professional diagnostic tool that audits Celery configurations for reliability, security vulnerabilities, and operational performance.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install celery-task-analyzer
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 celery-task-analyzer 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 Celery Task Analyzer?
The Celery Task Analyzer is a specialized utility designed for developers who need to maintain high-performance distributed systems using Openclaw Skills. It functions as a virtual senior systems engineer, scanning your Python codebase to detect critical configuration errors, inefficient worker settings, and security risks like unsafe pickle serialization. By synthesizing best practices for task queues, this skill helps teams prevent worker deadlocks, eliminate poison messages, and ensure that periodic tasks run without contention.
This tool is essential for teams scaling their asynchronous workloads, providing a comprehensive quality report that covers everything from broker-specific optimizations to task-level design patterns. Whether you are using RabbitMQ, Redis, or SQS, the analyzer provides tailored feedback to harden your infrastructure and improve overall system health.
Celery Task Analyzer Use Cases
- Auditing task definitions for production readiness and reliability.
- Identifying security vulnerabilities in message serialization and content acceptance.
- Optimizing worker concurrency and prefetch settings for specific workload types.
- Resolving overlapping schedules in Celery Beat to prevent database lock contention.
- Troubleshooting task failures by reviewing retry policies and backoff configurations.
How Celery Task Analyzer Works
- The skill initiates a project-wide discovery phase to locate Celery application configurations, task definitions, and beat schedules.
- It parses the extracted Python code to identify decorators, app settings, and broker-specific transport options.
- The analyzer performs a multi-point audit, checking for missing time limits, improper task naming, and non-serializable arguments.
- It evaluates the worker configuration against the detected workload (I/O-bound vs CPU-bound) to suggest optimal memory and concurrency limits.
- It generates a detailed health report, including a weighted score and prioritized remediation code snippets.
Celery Task Analyzer Setup
To use this tool within the suite of Openclaw Skills, ensure your AI agent has read access to your project repository. You can trigger an analysis by pointing the agent to your tasks directory or configuration module.
# Example: Triggering a full audit through your AI agent
Analyze the Celery configurations in /project/settings.py and all tasks in /project/apps/
Celery Task Analyzer Data Schema & Taxonomy
The analyzer organizes its findings into a structured report using the following metadata taxonomy:
| Data Point | Description |
|---|---|
| Health Score | A 0-100 overall rating based on distributed system best practices. |
| Category Metrics | Granular scores for Security, Routing, Worker Config, and Task Design. |
| Critical Issues | A prioritized list of risks that could cause system crashes or data loss. |
| Routing Map | A matrix showing how tasks are assigned to specific queues and exchanges. |
| Beat Timeline | A visual analysis of periodic tasks to detect execution overlaps. |
Celery Task Analyzer Advanced Features
- Broker-specific logic that adapts recommendations for RabbitMQ, Redis, or SQS environments.
- Intelligent detection of anti-patterns like synchronous .get() calls within tasks that lead to deadlocks.
- Security-first auditing that flags unsafe pickle usage and suggests JSON migration paths.
- Detailed worker tuning suggestions for memory management, including max_tasks_per_child and memory limits.
- Automated generation of Python remediation code to fix identified configuration issues immediately.
SKILL.md
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