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Agent Skill · 云服务

amg-check-azure-spend(AMG check Azure spend 部署)

用于辅助云资源、部署、容器、基础设施和运维自动化任务。它适合让 Agent 检查配置、整理部署步骤、分析资源状态、生成排障思路或辅助云服务接入。使用时需要明确目标环境、账号权限、区域和资源组,区分本地测试与生产操作;涉及删除资源、重启服务、修改网络或权限配置时,应先确认影响范围。

amg-check-azure-spend 是云服务类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为调用外部服务;许可证为 MIT-0,第三方扫描有提醒项,按安装量在云服务类的 479 个 Skill 里排第 136。

总安装 1,689周安装 69GitHub Stars 公开资料未说明下载 546核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

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:amg-check-azure-spend(AMG check Azure spend 部署)
来源仓库:https://github.com/1w2w3y/amg-check-azure-spend
安装命令:
openclaw skills install amg-check-azure-spend
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install amg-check-azure-spend

这个 Skill 是做什么的

云服务类 Skill 对接各家云平台的接口,用来查询资源、估算成本和执行变更。

amg-check-azure-spend 用于辅助云资源、部署、容器、基础设施和运维自动化任务。适合检查 Azure 订阅成本分析,列出可访问订阅并引导用户选择。通过 clawhub 安装并使用 openclaw skills install amg-check-azure-spend 命令调用。

使用时需明确目标环境、账号权限和资源组,区分测试与生产操作。涉及删除资源或修改配置时应先确认影响范围,避免误操作。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install amg-check-azure-spend;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

amg-check-azure-spend 怎么安装?

在终端执行 openclaw skills install amg-check-azure-spend。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

amg-check-azure-spend 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

amg-check-azure-spend 需要什么权限?

权限级别是调用外部服务。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。

amg-check-azure-spend 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

amg-check-azure-spend 用的人多吗?

公开数据是累计安装 1,689 次,近一周安装 69 次,下载 546 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

amg-check-azure-spend 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理云服务类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

云服务类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
azure-deploy

Azure 部署 · Codex、Claude

6,641,856764调用外部服务未明确通过
azure-diagnostics

Azure diagnostics 问题管理 · Codex、Claude

6,575,520764调用外部服务未明确通过
azure-aigateway

Azure aigateway 部署 · Codex、Claude

6,553,272764调用外部服务未明确有提醒
azure-ai

Azure AI 搜索 · Codex、Claude

6,514,128764调用外部服务未明确通过
azure-cloud-migrate

Azure Cloud migrate 部署 · Codex、Claude

6,506,784764调用外部服务未明确有提醒
amg-check-azure-spend

AMG check Azure spend 部署 · 本页

1,689—调用外部服务MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
amg-check-azure-spend
description
Run only when the user explicitly asks for a monthly Azure subscription cost analysis — lists all accessible subscriptions, lets the user choose which to analyze, then queries last billing month's cost breakdown by resource type, region, and service category for each subscription. Queries subscriptions sequentially with 1-minute waits between calls to avoid billing API rate limits (429). On first run, auto-discovers datasource UID.
argument-hint
[subscription-id-1,subscription-id-2,...] (optional, comma-separated)
disable-model-invocation
true
effort
max
allowed-tools
mcp__amg__amgmcp_cost_analysis mcp__amg__amgmcp_query_azure_subscriptions mcp__amg__amgmcp_datasource_list Bash(sleep *) Glob Read Write Edit

<!-- Auto-generated for OpenClaw by pack-openclaw. Notes for OpenClaw users: - Claude Code dynamic expressions (!...) in this file are NOT evaluated by OpenClaw and appear as literal text. Run them manually at the start of the workflow. - Invoke this skill only via slash command (e.g. /amg-check-azure-spend). Auto-invocation is disabled on Claude Code but not on OpenClaw. -->

OpenClaw Setup (one-time)

This skill calls MCP tools prefixed with mcp__amg__*, so OpenClaw must have an MCP server registered under the exact name amg. Run this once per workspace before invoking the skill:

openclaw mcp set amg '{"url":"https://<your-grafana-instance>/api/azure-mcp","transport":"streamable-http","headers":{"Authorization":"Bearer <your-token>"}}'

Replace <your-grafana-instance> with your Azure Managed Grafana endpoint and <your-token> with a valid Grafana service-account token (starts with glsa_). The server name must be amg — the skill's allowed-tools reference mcp__amg__* and will not find tools under any other name.

Verify the server is registered:

openclaw mcp list
Official skill source: https://github.com/Azure/amg-skills

Runtime Context

  • Current UTC time: !date -u +%Y-%m-%dT%H:%M:%SZ
  • Config: !cat memory/amg-check-azure-spend/config.md 2>/dev/null || echo "NOT_CONFIGURED"
  • Arguments: subscription-ids=$ARGUMENTS

Azure Subscription Cost Analysis

Analyze Azure subscription costs for the last full billing month using amgmcp_cost_analysis. Queries each subscription individually with mandatory waits between calls to respect the billing API rate limit.

Critical Constraints

  • No subagents for MCP. The Agent tool cannot access MCP tools — all MCP calls must be made from the main context.
  • Sequential queries only. Query one subscription at a time. Never query multiple subscriptions in parallel.
  • Mandatory 1-minute wait. After each amgmcp_cost_analysis call, run sleep 60 via Bash before the next call. This is non-negotiable — the billing API has a tight rate limit.
  • Recommend 5 subscriptions per run. The billing API has tight throttling — each subscription takes 1-3 minutes (wait time + potential 429 retries). Warn the user if they select more than 5 that the run will be slow, but allow it if they confirm.
  • 429 backoff. If you receive a 429 (Too Many Requests), wait 2 minutes (sleep 120), then retry. If 429 persists, wait 5 minutes (sleep 300) and retry once more. If still failing, skip that subscription and note it in the report.

Progress Tracking

Update checkboxes as you complete each phase:

  • [ ] Phase 1a: Datasource validated
  • [ ] Phase 1b: Subscriptions listed
  • [ ] Phase 1c: Subscriptions selected
  • [ ] Phase 2: Cost queries completed (0/N)
  • [ ] Report presented
  • [ ] Report saved to memory/amg-check-azure-spend/report.md

Configuration

If Config shows NOT_CONFIGURED: Run First-Run Setup at the bottom of this file, then return here.

If Config is populated: Extract the datasource UID from the pre-loaded Runtime Context above.

  • Datasource UID: from ## Azure Monitor Datasource > UID

Time Range

Always use the last full billing month boundaries. Compute them from the "Current UTC time" in the Runtime Context above:

  • startTime: first day of the *previous* month at 00:00:00.000Z (e.g., if current UTC time is 2026-04-16T..., startTime = 2026-03-01T00:00:00.000Z)
  • endTime: first day of the *current* month at 00:00:00.000Z (e.g., if current UTC time is 2026-04-16T..., endTime = 2026-04-01T00:00:00.000Z)

Do NOT use relative expressions like now-30d — billing months have variable lengths.


Workflow

Phase 1a: Validate Datasource

Call amgmcp_datasource_list (no parameters). Find entry with type == "grafana-azure-monitor-datasource".

  • Matches configured UID -> proceed.
  • Different UID -> update memory/amg-check-azure-spend/config.md, warn user, use new UID.
  • Not found -> abort with error.

Phase 1b: List All Subscriptions

Call amgmcp_query_azure_subscriptions with the validated datasource UID.

Present the full list as a table:

#Subscription NameSubscription ID
1......

Phase 1c: Select Subscriptions

If $ARGUMENTS provides subscription IDs (comma-separated): Use those directly. Validate they appear in the Phase 1b list.

If 5 or fewer subscriptions exist: Use all of them.

If more than 5 subscriptions exist: Present the list to the user and ask them to choose. Suggest a default selection of ~5 based on name (e.g., production subscriptions first). Example prompt:

*"Found N subscriptions. I recommend selecting around 5 — the billing API has tight rate limits, so each subscription takes 1-3 minutes (mandatory waits + potential 429 retries). You can select more, but the run will be slow. Which subscription numbers would you like to analyze? (e.g., 1,3,5) Or press Enter to use my suggested selection: [list]."*

If the user selects more than 5: Warn them about the expected duration (roughly 2-3 minutes per subscription due to mandatory waits and 429 backoffs), then proceed if they confirm.

Phase 2: Query Cost for Each Subscription

For each selected subscription, one at a time:

  1. Call amgmcp_cost_analysis with:
   azureMonitorDatasourceUid: {DATASOURCE_UID}
   subscriptionId: {SUBSCRIPTION_ID}
   startTime: {BILLING_MONTH_START}
   endTime: {BILLING_MONTH_END}
  1. Record the cost breakdown from the response:

- Total cost for the subscription - Top cost drivers by resource type (MeterCategory) - Cost by region - Any notable cost spikes or anomalies

  1. Wait 1 minute before the next query:
   sleep 60
  1. If you receive a 429 (Too Many Requests):

- Wait 2 minutes: sleep 120 - Retry the same subscription - If 429 again, wait 5 minutes: sleep 300 and retry once more - If still 429, skip this subscription and note "Rate-limited — skipped" in the report

Repeat for each subscription. Do NOT proceed to the next subscription without the 1-minute wait.


Output Format

Present a cost report with these sections:

1. Billing Period

State the exact billing month analyzed (e.g., "March 2026: 2026-03-01 to 2026-04-01").

2. Cost Summary Table

SubscriptionTotal CostTop Resource TypeTop RegionStatus
sub-name-1$X,XXX.XXVirtual MachinesEast USOK
sub-name-2$X,XXX.XXStorageWest US 2OK

3. Per-Subscription Breakdown

For each subscription:

  • Total cost for the billing month
  • Top 10 cost drivers by MeterCategory (resource type) with amounts and percentages
  • Cost by region — table showing spend per Azure region
  • Notable observations — any unusually high costs, unexpected resource types, or potential optimization opportunities

4. Cross-Subscription Comparison

  • Which subscription has the highest spend
  • Common cost drivers across subscriptions
  • Regional distribution of spend across all queried subscriptions

5. Cost Optimization Suggestions

Based on the data, flag:

  • Subscriptions with disproportionately high costs in a single category
  • Resource types that dominate spend (potential right-sizing candidates)
  • Regions with unexpectedly high costs

Save Report

After presenting findings, save the report to memory/amg-check-azure-spend/report.md:

  1. Read the current file (if it exists).
  2. Overwrite with the full cost report from this run, including:

- Billing period and generation date - Cost summary table across all queried subscriptions - Per-subscription breakdowns (top services, regions, resource types) - Cross-subscription comparison - Cost optimization suggestions - Any skipped subscriptions (rate-limited) noted with reason

  1. Update the "Generated" date header to today's date.

This file provides a baseline for future runs to compare month-over-month trends.


Error Handling

See ${CLAUDE_SKILL_DIR}/reference/error-handling.md for the full recovery table.


First-Run Setup

Run only when Config shows NOT_CONFIGURED. After completing, return to the Workflow above.

1. Discover Datasource UID: Call amgmcp_datasource_list. Filter type == "grafana-azure-monitor-datasource". Prefer uid == "azure-monitor-oob" if multiple match. Abort if zero match.

2. Write config: Write memory/amg-check-azure-spend/config.md:

# amg-check-azure-spend Configuration

User-specific values for the Azure spend analysis skill.
This file is auto-generated on first run and can be edited manually.

## Azure Monitor Datasource
- **UID**: {discovered_uid}
- **Name**: {discovered_name}

3. Confirm: Show the resolved config and ask for confirmation before proceeding.

适合场景

01

Azure 资源规划

02

云服务升级

03

基础设施检查

04

企业云环境自动化

能力概览

能力 1

整理 Azure 服务操作流程

能力 2

提示 CLI/MCP 前置条件

能力 3

辅助云资源检查和规划

能力 4

保留官方服务来源线索

平台分布

OpenClaw

75.6%
按下载量换算413

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

调用外部服务

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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