数据测试
- 作者仓库星标 39
- 作者更新于 实时读取
- 作者仓库 awesome-omni-skill
- 领域
- 运维部署
- 兼容 Agent
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- 信任分
- 88 / 100 · 社区维护
- 作者 / 版本 / 许可
- @diegosouzapw · 未声明 license
- Token 消耗评级
- 低消耗
- 接入复杂程度
- 即装即用
- 是否需要外部 API Key
- 不需要
- 兼容的系统
- Windows
- 底层运行要求
- 无特殊要求
- 文件与系统权限
-
- 只读
- 允许写入 / 修改
- 网络行为
- 仅限本地
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生，可能与作者实际意图存在差异。
需要注意： 未限定 allowed-tools，默认拥有全部工具权限。
---
name: debugging-dags
description: Comprehensive DAG failure diagnosis and root cause analysis. Use for complex debugging requests…
category: 运维部署
runtime: 无特殊运行时
---
# debugging-dags 输出预览
## PART A: 任务判断
- 适用问题：部署、CI、环境检查、发布或运维排障。
- 输入要求：目标材料、限制条件、期望输出和验收方式。
- 证据边界：围绕“Running the CLI / Step 1: Identify the Failure / Step 2: Get the Error Details”读取原文规则，不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断：确认你的需求是否属于部署、CI、环境检查、发布或运维排障，并标出输入、限制和预期结果。
- **02** 执行计划：优先按“Running the CLI / Step 1: Identify the Failure / Step 2: Get the Error Details”拆成步骤，说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果：给出可复制的命令、文件改动、检查清单或内容草稿，并说明如何继续迭代。
- **04** 风险边界：结合 读取文件、写入/修改文件、主要在本地完成、通常不需要额外 API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件；主要在本地完成；通常不需要额外 API Key。
- 先小样例验证，再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。原文没有稳定的斜杠命令要求。安装验证后通常全局生效，直接在对话里点名这个 Skill 并描述任务即可。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是：读取文件、写入/修改文件。
先用一个小任务确认它会围绕“Running the CLI / Step 1: Identify the Failure / Step 2: Get the Error Details”工作；涉及文件或命令时，先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作；如果输出泛泛而谈，就补充输入、边界和验收标准后重跑。
---
name: debugging-dags
description: Comprehensive DAG failure diagnosis and root cause analysis. Use for complex debugging requests…
category: 运维部署
source: diegosouzapw/awesome-omni-skill
---
# debugging-dags
## 什么时候使用
- 把部署运维方向的常用动作沉淀成 Agent 可调用的技能 适合处理部署、CI、发布、回滚、环境检查和运维排障，核心价值是把输入、判断、执行、验证和交付边界固定下来，避免 Agent 泛泛回答。 把任务拆成可执行、可检查、可继续迭代的步骤…
- 面向部署、CI、环境检查、发布或运维排障，优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「Running the CLI / Step 1: Identify the Failure / Step 2: Get the Error Details」组织步骤，不把推断写成作者事实。
- 读取文件、写入/修改文件；主要在本地完成；通常不需要额外 API Key。
- 先跑小样例，确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown，不编造命令、平台或依赖。作者原文负责流程事实；仓库文件负责来源和命令；流狐只补充适用场景、限制和质量判断。
skill "debugging-dags" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> Running the CLI / Step 1: Identify the Failure / Step 2: Get the Error Details
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> 无特殊运行时 | 读取文件、写入/修改文件 | 主要在本地完成
安全层 -> 通常不需要额外 API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
}DAG Diagnosis
You are a data engineer debugging a failed Airflow DAG. Follow this systematic approach to identify the root cause and provide actionable remediation.
Running the CLI
Run all af commands using uvx (no installation required):
uvx --from astro-airflow-mcp af <command>Throughout this document, af is shorthand for uvx --from astro-airflow-mcp af.
Step 1: Identify the Failure
If a specific DAG was mentioned:
- Run
af runs diagnose <dag_id> <dag_run_id>(if run_id is provided) - If no run_id specified, run
af dags statsto find recent failures
If no DAG was specified:
- Run
af healthto find recent failures across all DAGs - Check for import errors with
af dags errors - Show DAGs with recent failures
- Ask which DAG to investigate further
Step 2: Get the Error Details
Once you have identified a failed task:
- Get task logs using
af tasks logs <dag_id> <dag_run_id> <task_id> - Look for the actual exception - scroll past the Airflow boilerplate to find the real error
- Categorize the failure type:
- Data issue: Missing data, schema change, null values, constraint violation
- Code issue: Bug, syntax error, import failure, type error
- Infrastructure issue: Connection timeout, resource exhaustion, permission denied
- Dependency issue: Upstream failure, external API down, rate limiting
Step 3: Check Context
Gather additional context to understand WHY this happened:
- Recent changes: Was there a code deploy? Check git history if available
- Data volume: Did data volume spike? Run a quick count on source tables
- Upstream health: Did upstream tasks succeed but produce unexpected data?
- Historical pattern: Is this a recurring failure? Check if same task failed before
- Timing: Did this fail at an unusual time? (resource contention, maintenance windows)
Use af runs get <dag_id> <dag_run_id> to compare the failed run against recent successful runs.
On Astro
If you're running on Astro, these additional tools can help with diagnosis:
- Deployment activity log: Check the Astro UI for recent deploys — a failed deploy or recent code change is often the cause of sudden failures
- Astro alerts: Configure alerts in the Astro UI for proactive failure monitoring (DAG failure, task duration, SLA miss)
- Observability: Use the Astro observability dashboard to track DAG health trends and spot recurring issues
On OSS Airflow
- Airflow UI: Use the DAGs page, Graph view, and task logs to inspect recent runs and failures
Step 4: Provide Actionable Output
Structure your diagnosis as:
Root Cause
What actually broke? Be specific - not "the task failed" but "the task failed because column X was null in 15% of rows when the code expected 0%".
Impact Assessment
- What data is affected? Which tables didn't get updated?
- What downstream processes are blocked?
- Is this blocking production dashboards or reports?
Immediate Fix
Specific steps to resolve RIGHT NOW:
- If it's a data issue: SQL to fix or skip bad records
- If it's a code issue: The exact code change needed
- If it's infra: Who to contact or what to restart
Prevention
How to prevent this from happening again:
- Add data quality checks?
- Add better error handling?
- Add alerting for edge cases?
- Update documentation?
Quick Commands
Provide ready-to-use commands:
- To clear and rerun the entire DAG run:
af runs clear <dag_id> <run_id> - To clear and rerun specific failed tasks:
af tasks clear <dag_id> <run_id> <task_ids> -D - To delete a stuck or unwanted run:
af runs delete <dag_id> <run_id>
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核