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Llm observability and evals

Skill wanghong5233/agent-engineering-kit/cursor/.cursor/skills/llm-observability-and-evals

A reusable, production-grade .cursor/ engineering package for Cursor / Claude Code / Agent IDEs. Rules, skills, commands, and deterministic safety hooks extracted from a real Agent project.

Install
npx -y skills add wanghong5233/agent-engineering-kit --skill llm-observability-and-evals

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Design observability and eval contracts for production LLM/Agent systems. Use when adding ask/retrieval/tool/model flows, debugging quality regressions, defining trace/log/metric fields, building golden sets, or proving an Agent feature works. Do NOT use for cost-only work.

SKILL.md

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LLM Observability & Evals

一句话准则

Agent 上线不是"能回答"就算完成,而是每次决策都能回放、每次质量变化都能被 eval 捕捉。日志是行为合同,eval 是质量合同。

最小上线合同

任何 LLM/Agent 路径上线前至少具备:

类别必备字段 / 产物
Tracetrace_id, conversation_id, attempt_id, job_id
Decisionroute_reason, index_mode, tool_choice, model_route, policy_version
Executionretrieval/tool/model 每段 latency_ms, status, error_class
Qualitygolden set、pass threshold、失败样例归档
Costinput_tokens, output_tokens, model, provider, feature
UX stateaccepted, running, failed, timed_out, completed

缺一类时,先补 contract,再谈优化。

Observability 设计流程

  1. 画链路:用户输入 → route → retrieval/tool → model → persistence → UI。
  2. 标决策点:每个 if/route/fallback/retry 都要有 reason
  3. 标分段耗时:routing / retrieval / tool / generation / postprocess 分开计。
  4. 统一字段名:同一概念只允许一个 schema 名。
  5. 采样策略:高频成功路径可采样,失败路径必须全量。
  6. 隐私审计:prompt 可摘要化,secret/token/cookie 不落日志。

Eval 设计流程

阶段做什么
Golden Set从真实 bug、用户问题、边界样例沉淀 20+ 条
Metrics分类/抽取用 exact/schema;摘要/问答用 rubric 或 LLM-as-judge
Baseline记录当前生产 prompt/model/retrieval 的分数
Gateprompt、retrieval、model route 改动必须跑 eval
Drift定期重跑,发现模型更新或数据变化导致的退化

Agent 专属 Eval 维度

维度问题
Intent Routing是否按用户意图选择 RAG / direct context / tool
Retrieval Quality是否召回正确文档、正确片段、足够证据
Tool Use是否选择正确工具、传参合法、处理错误
Answer Grounding回答是否引用 evidence,而不是编造
State Durability失败时用户输入和 attempt 状态是否可见
Recovery UX失败是否可重试、错误是否可解释

反模式

反模式风险改法
只有最后答案,没有中间决策无法解释为什么检索/调用工具记录 decision + execution
只靠人工点点看回归不可重复golden set 进 CI
日志字段各服务自定义查询和 join 失败schema 进文档与测试
质量指标只看 thumbs up样本稀疏且滞后线上反馈 + 离线 eval 双轨
失败只返回 500用户和工程都无法恢复结构化 error_class + retryable

输出格式

## Trace Contract
## Metrics
## Eval Set
## Pass Criteria
## Dashboards / Alerts
## Gaps Before Launch

链接

  • 上游来源与改写说明:references/upstream.md
  • 成本优化:llm-cost-optimizer
  • Prompt 版本治理可从本 skill 拆出 prompt-governance

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