请问HN：你们是如何捕捉那些“成功”但产生错误结果的定时任务（cron jobs）的？
我遇到了一个令人沮丧的问题：我的定时任务返回的退出代码是0，但结果却是错误的。
<p>例子：
备份脚本成功完成，但创建了空的备份文件
数据处理任务完成，但只处理了10%的记录
报告生成器运行没有错误，但输出的数据不完整
数据库同步完成，但计数不匹配
日志显示“成功”——退出代码为0，没有异常——但实际结果是错误的。错误可能埋藏在日志中，但我并不是每天都主动检查日志。
<p>我尝试过：
在脚本中添加验证检查（例如，如果计数<100：退出1）——有效，但必须修改每个脚本，改变阈值需要更改代码
Webhook警报——需要为每个脚本编写连接器
错误监控工具（如Sentry等）——它们捕捉异常，而不是错误结果
手动抽查——不具备可扩展性
<p>脚本内验证的方法适用于简单情况，但不够灵活。如果需要更改阈值怎么办？如果文件存在但来自昨天怎么办？如果需要检查多个条件怎么办？最终你会把监控逻辑和业务逻辑混合在一起。
<p>我构建了一个简单的监控工具，它监视作业结果，而不仅仅是执行状态。你只需将实际结果（文件大小、记录数、状态等）发送给它，如果有异常，它会发出警报。无需翻阅日志，而且你可以在不部署代码的情况下调整阈值。
<p>你是如何处理这个问题的？是为每个脚本添加验证，主动检查日志，还是使用某种工具在结果与预期不符时发出警报？你是如何捕捉这些“静默失败”的？
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I've been dealing with a frustrating problem: my cron jobs return exit code 0, but the results are wrong.<p>Examples:
Backup script completes successfully but creates empty backup files
Data processing job finishes but only processes 10% of records
Report generator runs without errors but outputs incomplete data
Database sync completes but the counts don't match
The logs show "success" — exit code 0, no exceptions — but the actual results are wrong. The errors might be buried in logs, but I'm not checking logs proactively every day.<p>I've tried:
Adding validation checks in scripts (e.g., if count < 100: exit 1) — works, but you have to modify every script, and changing thresholds requires code changes
Webhook alerts — requires writing connectors for every script
Error monitoring tools (Sentry, etc.) — they catch exceptions, not wrong results
Manual spot checks — not scalable<p>The validation-in-script approach works for simple cases, but it's not flexible. What if you need to change the threshold? What if the file exists but is from yesterday? What if you need to check multiple conditions? You end up mixing monitoring logic with business logic.<p>I built a simple monitoring tool that watches job results instead of just execution status. You send it the actual results (file size, record count, status, etc.) and it alerts if something's off. No need to dig through logs, and you can adjust thresholds without deploying code.<p>How do you handle this? Are you adding validation to every script, proactively checking logs, or using something that alerts when results don't match expectations? What's your approach to catching these "silent failures"?