Capture errors with rich context for debugging and alerting
Capture errors with structured context for debugging and alerting. Automatically triggers when exceptions occur, recording stack traces, job steps, and error types with trace IDs for grouped analysis and monitoring.
/plugin marketplace add nexus-labs-automation/backend-observability/plugin install nexus-labs-automation-backend-observability@nexus-labs-automation/backend-observabilityThis skill inherits all available tools. When active, it can use any tool Claude has access to.
An error without context is useless. Every error should answer: What, Where, Who, When, Why.
| Attribute | Example | Required |
|---|---|---|
error.type | ValidationError, TimeoutError | Yes |
error.message | "Invalid email format" | Yes |
exception.stacktrace | Full stack trace | Yes |
job.name | checkout, signup | Recommended |
job.step | payment, validation | Recommended |
| Level | Status | Action |
|---|---|---|
| Critical | 500 | Page immediately |
| Error | 500 | Alert, investigate |
| Warning | 4xx | Track, batch review |
| Info | - | Log only |
AppError {
Type string // "validation", "timeout"
Message string // Human-readable
Code string // For grouping
Retryable bool // Can retry?
Context map // Additional context
}
On error:
→ Record error on span with error.type, job.name, job.step
→ Set span status to Error
→ Log structured error with trace_id
→ Increment error counter by type/job/step
Group by: error type + message (without dynamic data) + top stack frames
Strip from fingerprints: user IDs, request IDs, timestamps, tokens
references/methodology/jtbd-for-backend.mdreferences/anti-patterns.mdUse when working with Payload CMS projects (payload.config.ts, collections, fields, hooks, access control, Payload API). Use when debugging validation errors, security issues, relationship queries, transactions, or hook behavior.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.