agentleFS
letern1tyletern1ty/claude-skills/skills/llm-batch-resilience/SKILL.md

llm-batch-resilience

Skill0 starsChanged 4 months agoNo licenceView on GitHub

给「无人值守调用 LLM 的批处理/定时任务」(daily digest、批量摘要/分类/打标、定时报表、cron/launchd 里的 LLM pipeline)做韧性工程与故障排查。适用于用户说要让定时的 LLM 任务更稳、任务偶发超时/限流/卡住、产出了占位或半成品结果、要给 LLM 调用加重试/超时/自愈、排查「为什么昨晚的自动任务失败/生成了垃圾」的场景。Resilience & failure-mode engineering for unattended batch/scheduled tasks that call an LLM. Covers timeout, retry/backoff, fail-loud vs placeholder output, self-healing re-runs, rate-limit avoidance and provider-outage triage. 区别于通用 code review:本 skill 只管「调用外部 LLM 的自动任务如何不产生假成功、失败后如何自愈」。

The file

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.