A sophisticated framework for managing AI agent reliability through smart deduplication, severity-based routing, and automated incident remediation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install alerts
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 alerts 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 Alerts?
Alerts is a technical framework designed to solve the complexities of monitoring modern AI agent ecosystems. It provides standardized patterns for preventing alert fatigue by grouping symptoms under root causes and implementing a rigorous severity hierarchy from P0 to P3. By utilizing Openclaw Skills, developers can move beyond simple threshold monitoring to implement sophisticated behavioral drift detection and cost-aware alerting.
This skill ensures that your agentic workflows remain stable by monitoring for infinite loops, silent failures, and API usage spikes. It bridges the gap between raw agent logs and actionable engineering intelligence, ensuring that the right domain experts are notified at the right time through high-reliability webhook patterns.
Alerts Use Cases
- Deduplicating hundreds of individual pod failures into a single service-level alert.
- Monitoring AI agent token consumption and costs to prevent unexpected billing spikes.
- Detecting behavioral drift and quality degradation in LLM responses.
- Automating incident escalation paths to ensure P0 issues reach on-call engineers via SMS or phone.
- Providing instant access to runbooks directly within Slack or PagerDuty notifications.
How Alerts Works
- Alerts are ingested and grouped by logical labels like service or cluster to prevent notification storms.
- A severity hierarchy is applied to determine the notification channel and required response time.
- AI-specific monitors track token velocity, correlation IDs, and response success rates.
- Routing logic directs the incident to specific expertise domains (e.g., Database or API teams) rather than generic schedules.
- Automated remediation triggers attempt to resolve known issues while status pages are updated via webhooks.
Alerts Setup
To implement these patterns within your Openclaw Skills environment, configure your alert manager using the following structures:
# Configure grouping and repeat intervals
group_by: ['alertname', 'service']
repeat_interval: 5m
# Set up webhook verification
export WEBHOOK_SECRET="your_hmac_secret"
Ensure your agent monitoring logic includes behavioral baselines and correlation ID generation for every workflow lifecycle.
Alerts Data Schema & Taxonomy
| Component | Description | Format |
|---|---|---|
| Alert Labels | Root cause identifiers (service, cluster, alertname) | Key-Value Pairs |
| Severity | Impact levels from P0 (Immediate) to P3 (Review) | Enum |
| Correlation ID | UUID for tracking alert lifecycle across systems | String/UUID |
| Inhibition Rules | Logic to suppress symptoms when root causes fire | Boolean Logic |
| Metrics | Token usage, latency, and success rate data | Float/Integer |
Alerts Advanced Features
- Inhibition rules to silence downstream API alerts when a core database is unreachable.
- Exponential backoff and circuit breaker patterns for resilient webhook delivery.
- Automated remediation for self-healing infrastructure (e.g., auto-restarting stuck services).
- Behavioral drift detection that compares current LLM outputs against historical success baselines.
- Context-aware routing that escalates based on the number of users affected regardless of initial severity.
SKILL.md
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