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kestrakestra 搜索

Agent Skill

kestra 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

279

周安装

12

GitHub Stars

公开资料未说明

下载量

98
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:kestra(kestra 搜索)
来源仓库:https://github.com/lemig/kestra-template
仓库路径:skills/kestra
安装命令:
npx skills add https://github.com/lemig/kestra-template --skill kestra
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/lemig/kestra-template --skill kestra

简介

kestra 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。

  • 适用于关键词搜索、任务场景匹配或来源线索筛选等研究检索场景。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,需结合原始 README 确认具体用法。
  • 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操作。
  • kestra 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Kestra Workflow Generator

Kestra uses declarative YAML to define workflows. Every flow requires id, namespace, and tasks.

Core YAML Structure

id: flow-name              # Required: unique within namespace, lowercase with hyphens
namespace: company.team    # Required: dot-separated hierarchy
description: |             # Optional: supports Markdown
  Flow description
labels:                    # Optional: key-value pairs for organization
  env: prod
  team: data-engineering
inputs:                    # Optional: typed parameters
  - id: my_input
    type: STRING
    required: false
    defaults: "default value"
variables:                 # Optional: reusable values
  base_url: "https://api.example.com"
tasks:                     # Required: list of tasks to execute
  - id: task-name
    type: io.kestra.plugin...
triggers:                  # Optional: automatic execution
  - id: schedule
    type: io.kestra.plugin.core.trigger.Schedule
    cron: "0 9 * * *"
errors:                    # Optional: error handling tasks
  - id: notify
    type: io.kestra.plugin.core.log.Log
    message: "Flow failed!"

Task Generation Workflow

  1. Identify the task type needed (script, HTTP, flowable, etc.)
  2. Use fully qualified plugin type: io.kestra.plugin.[category].[TaskName]
  3. Include all required properties for the task type
  4. Use Pebble templating for dynamic values: {{inputs.name}}, {{outputs.taskId.property}}

Input Types

TypeDescriptionExample
STRINGText valuedefaults: "hello"
INTIntegerdefaults: 42
FLOATDecimaldefaults: 3.14
BOOLEANTrue/falsedefaults: true
DATETIMEISO 8601 datetimedefaults: "2024-01-01T00:00:00Z"
DATEISO 8601 datedefaults: "2024-01-01"
TIMEISO 8601 timedefaults: "09:00:00"
DURATIONISO 8601 durationdefaults: "PT1H"
FILEUploaded file(stored in internal storage)
JSONJSON objectdefaults: '{"key": "value"}'
ARRAYList of itemsitemType: STRING required
SELECTDropdownvalues: [a, b, c]

Common Task Types

Logging & Debug

- id: log_message
  type: io.kestra.plugin.core.log.Log
  message: "Hello {{ inputs.name }}!"
  level: INFO  # DEBUG, INFO, WARN, ERROR

HTTP Requests

- id: api_call
  type: io.kestra.plugin.core.http.Request
  uri: "https://api.example.com/data"
  method: GET
  headers:
    Authorization: "Bearer {{ secret('API_TOKEN') }}"

Python Scripts

- id: python_task
  type: io.kestra.plugin.scripts.python.Script
  containerImage: python:3.11-slim
  beforeCommands:
    - pip install pandas requests
  script: |
    import pandas as pd
    from kestra import Kestra

    data = {"result": "success"}
    Kestra.outputs(data)  # Pass outputs to next tasks
  outputFiles:
    - "*.csv"  # Files to capture

Shell Commands

- id: shell_task
  type: io.kestra.plugin.scripts.shell.Commands
  commands:
    - echo "Processing {{ inputs.filename }}"
    - cat {{ outputs.download.uri }}

Detailed References

Core Workflow Components

Plugins

Architecture & Patterns

Additional Resources

  • Ask Kestra AI API: See references/ask-kestra-ai.md - Query Kestra's documentation AI when you need more specific answers or the latest documentation
Tip: If a Kestra MCP server is configured, use it to validate flows, list executions, or trigger runs directly. This skill focuses on generating correct YAML; the MCP server handles runtime interaction.
Tip: If you can't find an answer in the static references, use the Ask Kestra AI API to query their documentation assistant. It's especially useful for: - Edge cases not covered in common patterns - Latest plugin features or changes - Complex output structures (e.g., ForEachItem merging) - Troubleshooting specific error messages

Quick Debugging Checklist

  • Indentation: Exactly 2 spaces (no tabs)
  • Task IDs: Unique within flow, use lowercase with hyphens
  • Plugin types: Fully qualified (io.kestra.plugin.core.log.Log)
  • Expressions: Use {{}} for Pebble templating
  • Secrets: Use {{secret('SECRET_NAME')}}
  • Outputs: Reference as {{outputs.taskId.property}}
  • Inputs: Reference as {{inputs.inputId}}
  • Strings with colons: Quote them (message: "Time: 10:00")
  • Multi-line scripts: Use | for literal block scalar

Common Errors & Fixes

ErrorCauseFix
"Task type not found"Plugin not available or typoVerify exact plugin type string
"Invalid YAML syntax"Bad indentation or special charsUse 2-space indent, quote strings with :
"Variable not found"Wrong expression syntaxCheck {{outputs.taskId.prop}} format
"Required property missing"Missing required task fieldCheck plugin docs for required fields
"Cannot coerce"Type mismatch in expressionVerify input/output types match
Broken JSON body / 400 errorsUser data contains " quotesUse `{{value \json}}` filter (no surrounding quotes)
Fix deployed but restart still failsrestart uses original revisionUse replay with latest_revision: true instead

适合场景

01

用户想查找某类 Agent Skill 时

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

37.94%
按下载量换算37

Claude

28.83%
按下载量换算28

Cursor

18.64%
按下载量换算18

Gemini CLI

8.58%
按下载量换算8

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/lemig/kestra-template --skill kestra 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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