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infrahub-analyzing-datainfrahub 分析数据

Agent Skill

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

总安装

306

周安装

13

GitHub Stars

19

下载量

107
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:infrahub-analyzing-data(infrahub 分析数据)
来源仓库:https://github.com/opsmill/infrahub-skills
仓库路径:skills/infrahub-analyzing-data
安装命令:
npx skills add https://github.com/opsmill/infrahub-skills --skill infrahub-analyzing-data
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/opsmill/infrahub-skills --skill infrahub-analyzing-data

简介

用于辅助数据整理与表格分析,适合在 CSV/Excel 处理中清洗字段与生成统计口径。

  • 可汇总指标、发现异常并支持图表准备,提升数据可读性与洞察力。
  • 通过 npx skills add 命令从 GitHub 仓库安装,需确认数据来源与字段含义。
  • 涉及敏感数据导出时应先确认权限与脱敏边界,避免批量写回风险。
  • infrahub-analyzing-data 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Overview

Expert guidance for interactive data analysis against a live Infrahub instance. This skill uses the Infrahub MCP server to query, correlate, and reason over infrastructure data on demand — answering operational questions that span multiple node types and relationships.

Use this skill for any question of the form *"what does Infrahub currently know about X, and how does it relate to Y?"*

Typical question patterns:

  • Compliance — "Are all devices following the naming convention?"
  • Service impact — "Which services are hosted on devices in this rack?"
  • Maintenance windows — "Which devices are currently in a maintenance window, and what depends on them?"
  • Drift detection — "Which realized devices differ from their topology design?"
  • Capacity — "Which racks are over 80% full?"
  • Change impact — "What BGP sessions, services, and IPs depend on this prefix?"
  • Inventory gaps — "Which devices have no platform or OS version recorded?"

For automated, pipeline-enforced checks that block proposed changes, see ../infrahub-managing-checks/SKILL.md. For repeatable scheduled reports exported as artifacts, see ../infrahub-managing-transforms/SKILL.md.

Project Context

If invoked with arguments (e.g., /infrahub:analyzing-data Which devices have no platform assigned?), treat the arguments as the question to answer.

When to Use

  • Answering operational questions interactively via natural language
  • Cross-referencing two or more node types to find relationships or gaps
  • Investigating the blast radius of a change before executing it
  • Auditing data quality across the inventory
  • Producing one-time or on-demand reports for stakeholders
  • Exploring schema structure and data before writing a generator or check

How It Works

The Infrahub MCP server exposes tools that let Claude query Infrahub data directly. The typical workflow:

  1. Query — use MCP tools to fetch current state from Infrahub
  2. Correlate — join, diff, or filter the data against a policy or second dataset
  3. Reason — identify gaps, anomalies, or relationships
  4. Report — surface findings with context and remediation hints

Rule Categories

PriorityCategoryPrefixDescription
CRITICALMCP Toolsmcp-Available Infrahub MCP tools, invocation patterns, response structure
CRITICALQuery Patternsquery-GraphQL structures for fetching, filtering, and traversing relationships
HIGHCorrelationcorrelation-Joining, diffing, and reasoning over data from multiple queries
HIGHReporting Outputreporting-Presenting findings: summaries, tables, per-object detail, remediation hints
MEDIUMApproach Selectionapproach-When to use MCP analysis vs InfrahubCheck vs Transform

MCP Server Basics

When the Infrahub MCP server is connected, Claude can call tools such as:

  • mcp__infrahub__infrahub_query — Execute a GraphQL query (primary tool)
  • mcp__infrahub__infrahub_list_schema — List available node kinds
  • mcp__infrahub__infrahub_get — Retrieve a specific object by ID or filters
  • mcp__infrahub__infrahub_create — Create an object (remediation, on a branch)
  • mcp__infrahub__infrahub_update — Update an object (remediation, on a branch)
# Example: find all devices in an active
# maintenance window
query MaintenanceDevices {
  MaintenanceWindow(status__value: "active") {
    edges {
      node {
        name { value }
        start_time { value }
        end_time { value }
        devices {
          edges {
            node {
              name { value }
              role { value }
              site {
                node { name { value } }
              }
            }
          }
        }
      }
    }
  }
}

Typical Analysis Workflow

1. Understand the question
   → "Which services depend on devices currently
      in a maintenance window?"

2. Identify the node types involved
   → MaintenanceWindow, DcimDevice, Service
     (or equivalent in your schema)

3. Query current state
   → mcp__infrahub__infrahub_query — one query
     per node type, or combined

4. Correlate the data
   → Join across node types, filter, count, diff

5. Report findings
   → Summarize with counts, list affected objects,
     suggest next steps

Supporting References

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

34.34%
按下载量换算37

Claude

30.01%
按下载量换算32

Cursor

20.47%
按下载量换算22

Gemini CLI

8.95%
按下载量换算10

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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