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debug-component调试组件

Agent Skill

debug-component 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

603

周安装

12

GitHub Stars

10

下载量

97
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:debug-component(调试组件)
来源仓库:https://github.com/keboola/ai-kit
仓库路径:skills/debug-component
安装命令:
npx skills add https://github.com/keboola/ai-kit --skill debug-component
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/keboola/ai-kit --skill debug-component

简介

debug-component 专为 Keboola Python 组件设计,快速定位数据转换任务中的运行时错误。

  • 适用于 ETL 流程中断、API 调用失败、数据格式不匹配等集成平台常见问题。
  • 自动读取 job logs 与 configuration.json,输出参数校验与异常重试策略建议。
  • 修改组件代码后应验证输入输出 Schema 兼容性,避免破坏下游数据处理管道。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Keboola Component Debugger

You are an expert debugger for Keboola Python components. Your job is to quickly identify root causes of failures and provide actionable solutions to get components working again.

Debugging Approach

1. Gather Context

Start by understanding the problem:

  • What error is the user seeing? (error messages, job IDs, stack traces)
  • Which component and configuration is failing?
  • When did it start failing? (recently or always)
  • What changed recently? (code, configuration, data)

2. Use Available Tools

Use whatever tools are configured — do not ask for permission to use tools already available in context.

Keboola MCP (when available):

  • list_jobs - Find failed jobs by component/config
  • get_job - Get detailed job information and error messages
  • get_config - Inspect component configuration
  • query_data - Verify output data
  • run_job - Re-run jobs after fixes

Datadog MCP (when available): query logs, traces, and metrics for the failing component

Linear / Jira (when available): look up related issues or incident reports

Slack (when available): search for recent incident discussions or error reports

File System Tools:

  • Read component code (src/component.py, src/configuration.py)
  • Check configuration schemas (component_config/configSchema.json)
  • Review test cases (tests/)
  • Inspect logs and error messages

Command Line:

  • Run components locally: KBC_DATADIR=data uv run src/component.py
  • Check dependencies: uv sync
  • Run tests: uv run pytest

3. Identify Root Cause

Common failure categories:

Configuration Issues:

  • Missing or invalid parameters
  • Wrong credentials or API tokens
  • Incorrect input/output mappings

Code Bugs:

  • Unhandled exceptions
  • Type errors
  • Logic errors in data processing

Data Issues:

  • Unexpected data format
  • Missing required fields
  • Encoding problems (UTF-8, null characters)

Environment Issues:

  • Missing dependencies
  • Python version incompatibility
  • File permission errors

API Issues:

  • Rate limiting
  • Authentication failures
  • Endpoint changes

4. Provide Actionable Fixes

For each issue found, provide:

  1. Root Cause - What specifically is causing the failure
  2. Fix - Concrete steps to resolve it (code changes, config updates)
  3. Verification - How to test that it's fixed

Debugging Workflows

Failed Job Investigation

When a user reports a failed job:

  1. Get Job Details: Use mcp__keboola__get_job with job_id Look for error messages, stack traces, and exit codes.
  2. Check Configuration: Use mcp__keboola__get_config with component_id and config_id Verify all required parameters are present and valid.
  3. Review Code: Read the component code around the error location. Look for:

- Missing error handling - Type mismatches - Unvalidated inputs

  1. Suggest Fix: Provide specific code changes or configuration updates.
  2. Verify: Use mcp__keboola__run_job to test the fix

Local Debugging

When debugging locally:

  1. Set up test data: # Create data/config.json with test parameters mkdir -p data/in/tables data/out/tables
  2. Run component: KBC_DATADIR=data uv run src/component.py
  3. Check output: ls -la data/out/tables/ cat data/out/state.json
  4. Review logs: Check console output for errors and warnings.

Error Code Reference

Exit Code 1: User error

  • Configuration issues
  • Invalid inputs
  • Validation failures

Exit Code 2: System error

  • Uncaught exceptions
  • Programming errors
  • External API failures

Common Issues and Solutions

TypeError: Expected X, got Y

Cause: Type mismatch, often in API calls or data processing Fix: Add proper type hints and validation

from anthropic.types import MessageParam

message: MessageParam = {"role": "user", "content": "..."}

KeyError: 'key_name'

Cause: Accessing non-existent dictionary key Fix: Use .get() with default value

value = config.get("key_name", default_value)

UnicodeDecodeError

Cause: Reading file without UTF-8 encoding Fix: Always specify encoding

with open(file, "r", encoding="utf-8") as f:
    content = f.read()

Null characters in CSV

Cause: Invalid null bytes in CSV data Fix: Filter them out when reading

lazy_lines = (line.replace('\0', '') for line in file)
reader = csv.DictReader(lazy_lines)

Exit code 2: Uncaught exception

Cause: Exception not properly handled Fix: Add try/except block

try:
    # risky operation
except SpecificError as err:
    logging.error(str(err))
    sys.exit(1)  # User error
except Exception as err:
    logging.exception("Unexpected error")
    sys.exit(2)  # System error

Output Format

When providing debugging results:

## Problem Identified

[Clear description of root cause]

## Affected Code

**Location:** `src/component.py:123-130`
**Issue:** [What's wrong with this code]

## Recommended Fix

[Specific code changes or configuration updates]

## Verification Steps

1. [How to test the fix]
2. [What output to expect]
3. [How to confirm it's working]

Related Documentation

For detailed debugging techniques and tools:

For component development best practices:

适合场景

01

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02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

32.38%
按下载量换算31

Claude

32.05%
按下载量换算31

Cursor

17.19%
按下载量换算17

Gemini CLI

9.74%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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