| schemaVersion | "2026-04-11T00:00:00.000Z" |
| skillId | backend/background-jobs-queues |
| name | background-jobs-queues |
| displayName | Background Jobs Queues |
| description | Use when working on queues, workers, scheduled jobs, retries, idempotency, and async workflows. Focus on idempotency, retry policy, dead-letter handling, and operational visibility. |
| aliases | ["background-jobs-queues","Background Jobs Queues","backgroundjobsqueues","后台任务队列","异步队列","任务重试","幂等任务","死信队列","后台任务","异步任务","后台作业","任务队列","定时任务","job","scheduled job","队列","worker","重试"] |
| version | 0.1.0 |
| sourceHash | sha256:69ecb0b3257ce3ca6cc0be667b7d06c65fd213ff942954e24ac0c2282c85fe30 |
| domain | backend |
| departmentTags | ["backend-platform"] |
| sceneTags | ["architecture","debug"] |
Background Jobs Queues
Use this skill when the task involves queues, workers, scheduled jobs, retries, idempotency, and async workflows.
Goal: produce reliable engineering guidance and implementation steps focused on idempotency, retry policy, dead-letter handling, and operational visibility.
Working model
- Identify the affected system, data, users, and failure modes.
- Define invariants, inputs, outputs, ownership, and rollback needs.
- Prefer small, auditable changes with explicit validation.
- Call out security, performance, concurrency, and data-loss risks when relevant.
- Finish with concrete verification steps and residual risks.
Rules
- Ground recommendations in the current codebase or runtime evidence.
- Prefer explicit contracts, typed boundaries, and defensive validation.
- Do not hide operational concerns behind generic best practices.
- Include negative cases, edge cases, and failure behavior.
- For review tasks, list findings first with file and line references when possible.
- For test or performance tasks, define the workload, success criteria, and measurement method.
Checklist
- Are assumptions and ownership boundaries explicit?
- Are risky changes reversible or safely deployable?
- Are observability and diagnostics sufficient for production issues?
- Are tests or validation steps targeted to the actual risk?
- Are security and data-integrity concerns addressed?
