Token导航 LogoToken导航

Agent Skill · 研究检索

cloud-tagging-enforcer(云标记执行器)

cloud-tagging-enforcer 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

cloud-tagging-enforcer 是研究检索类 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,按安装量在研究检索类的 73,675 个 Skill 里排第 41,004。

总安装 1,224周安装 51GitHub Stars 公开资料未说明下载 408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:cloud-tagging-enforcer(云标记执行器)
来源仓库:https://github.com/charlie-morrison/cloud-tagging-enforcer
安装命令:
openclaw skills install cloud-tagging-enforcer
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 cloud-tagging-enforcer

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

云标记执行器强制实施资源标签策略,确保成本归属与合规追踪。适用于 OpenClaw 中治理多账号云环境的标记规范落地。自动修正缺失或错误的标签,生成违规告警日志。安装命令:openclaw skills install cloud-tagging-enforcer。操作具备修改权限,部署前务必测试策略表达式准确性。

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 cloud-tagging-enforcer;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

cloud-tagging-enforcer 怎么安装?

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

cloud-tagging-enforcer 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。

cloud-tagging-enforcer 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。

cloud-tagging-enforcer 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。

cloud-tagging-enforcer 用的人多吗?

公开数据是累计安装 1,224 次,近一周安装 51 次,下载 408 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。

cloud-tagging-enforcer 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。
  • 需要处理研究检索类任务、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。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
cloud-tagging-enforcer

云标记执行器 · 本页

1,224—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
cloud-tagging-enforcer
description
Enforce cloud resource tagging policies for cost allocation, compliance, and governance across AWS/GCP/Azure

Cloud Tagging Enforcer

Scan cloud infrastructure for missing, incorrect, or non-compliant resource tags. This skill teaches an AI agent to audit tagging across AWS, GCP, and Azure, define and validate tagging schemas, generate compliance reports, and produce remediation scripts that bring resources into policy compliance.

Use when: "check cloud tags", "untagged resources", "tagging policy", "cost allocation tags", "tag compliance", "enforce tags", "missing tags", "tag governance", "remediate tags"

Commands

1. scan -- Find untagged or non-compliant resources

Discover all resources that violate the tagging policy by having missing, empty, or incorrectly formatted tags.

Step 1: Define the expected tagging schema

Before scanning, establish what "compliant" means. Load the policy or define defaults.

# Check if a tagging policy file exists in the repo
rg -l "tagging.policy|tag.schema|required.tags" . -g '*.{json,yaml,yml,toml}'

# If no policy exists, use a sensible default for cost allocation and governance
cat <<'POLICY'
required_tags:
  - key: "Environment"
    allowed_values: ["production", "staging", "development", "sandbox"]
  - key: "Owner"
    pattern: "^[a-z]+@[a-z]+\\.[a-z]+$"
  - key: "Team"
    allowed_values: []  # any non-empty value
  - key: "CostCenter"
    pattern: "^CC-[0-9]{4}$"
  - key: "Project"
    allowed_values: []  # any non-empty value
  - key: "ManagedBy"
    allowed_values: ["terraform", "cloudformation", "pulumi", "manual"]
POLICY
Step 2: Scan AWS resources
# AWS: Find all resources missing required tags using Resource Groups Tagging API
# This is the most efficient API -- covers 50+ resource types in one call
aws resourcegroupstaggingapi get-resources \
  --no-paginate \
  --output json | python3 -c "
import sys, json

required_keys = ['Environment', 'Owner', 'Team', 'CostCenter', 'Project']
data = json.load(sys.stdin)
violations = []

for resource in data.get('ResourceTagMappingList', []):
    arn = resource['ResourceARN']
    tags = {t['Key']: t['Value'] for t in resource.get('Tags', [])}
    missing = [k for k in required_keys if k not in tags]
    empty = [k for k in required_keys if k in tags and not tags[k].strip()]

    if missing or empty:
        violations.append({
            'arn': arn,
            'missing_tags': missing,
            'empty_tags': empty,
            'existing_tags': tags
        })

print(f'Total resources scanned: {len(data.get(\"ResourceTagMappingList\", []))}')
print(f'Non-compliant resources: {len(violations)}')
print()
for v in violations[:20]:
    svc = v['arn'].split(':')[2]
    print(f'  {svc}: {v[\"arn\"]}')
    if v['missing_tags']:
        print(f'    MISSING: {v[\"missing_tags\"]}')
    if v['empty_tags']:
        print(f'    EMPTY:   {v[\"empty_tags\"]}')
    print()
if len(violations) > 20:
    print(f'  ... and {len(violations) - 20} more')
"

# AWS: Find EC2 instances specifically without the Environment tag
aws ec2 describe-instances \
  --filters "Name=instance-state-name,Values=running" \
  --query 'Reservations[].Instances[?!not_null(Tags[?Key==`Environment`].Value | [0])].{ID:InstanceId,Name:Tags[?Key==`Name`]|[0].Value,Type:InstanceType}' \
  --output table

# AWS: Find untagged S3 buckets
for bucket in $(aws s3api list-buckets --query 'Buckets[].Name' --output text); do
  tags=$(aws s3api get-bucket-tagging --bucket "$bucket" 2>/dev/null | python3 -c "
import sys, json
data = json.load(sys.stdin)
keys = [t['Key'] for t in data.get('TagSet', [])]
print(','.join(keys))
" 2>/dev/null)
  if [ -z "$tags" ]; then
    echo "UNTAGGED: s3://$bucket"
  fi
done
Step 3: Scan GCP resources
# GCP: Find instances without required labels
gcloud compute instances list --format=json | python3 -c "
import sys, json

required_labels = ['environment', 'owner', 'team', 'cost-center', 'project']
instances = json.load(sys.stdin)
for inst in instances:
    labels = inst.get('labels', {})
    missing = [l for l in required_labels if l not in labels]
    if missing:
        zone = inst['zone'].split('/')[-1]
        print(f'NON-COMPLIANT: {inst[\"name\"]} ({zone})')
        print(f'  Missing labels: {missing}')
"

# GCP: Scan all project resources using Cloud Asset Inventory
gcloud asset search-all-resources \
  --scope="projects/$GCP_PROJECT" \
  --query="NOT labels:environment" \
  --format="table(name.basename(), assetType, labels)"
Step 4: Scan Azure resources
# Azure: Find resources without required tags
az resource list --query "[?tags.Environment==null || tags.Owner==null].{Name:name,Type:type,RG:resourceGroup,Tags:tags}" --output table

# Azure: Get a compliance percentage by resource group
az resource list --output json | python3 -c "
import sys, json
from collections import defaultdict

required = ['Environment', 'Owner', 'Team', 'CostCenter']
resources = json.load(sys.stdin)
rg_stats = defaultdict(lambda: {'total': 0, 'compliant': 0})

for r in resources:
    rg = r.get('resourceGroup', 'unknown')
    tags = r.get('tags') or {}
    rg_stats[rg]['total'] += 1
    missing = [k for k in required if k not in tags]
    if not missing:
        rg_stats[rg]['compliant'] += 1

for rg, stats in sorted(rg_stats.items()):
    pct = (stats['compliant'] / stats['total'] * 100) if stats['total'] > 0 else 0
    status = 'PASS' if pct == 100 else 'FAIL'
    print(f'{rg:<30} {stats[\"total\"]:>6} {stats[\"compliant\"]:>10} {pct:>6.1f}% {status}')
"
Report template
## Cloud Tagging Compliance Report

**Date:** YYYY-MM-DD
**Cloud:** AWS / GCP / Azure
**Account/Project:** account-id / project-name
**Policy:** 5 required tags (Environment, Owner, Team, CostCenter, Project)

### Summary
- Total resources scanned: 847
- Fully compliant: 612 (72.3%)
- Non-compliant: 235 (27.7%)
- Completely untagged: 43 (5.1%)

### Non-compliance by service
- EC2 instances: 23 / 89 non-compliant (25.8%)
- S3 buckets: 8 / 34 non-compliant (23.5%)
- RDS instances: 2 / 12 non-compliant (16.7%)
- Lambda functions: 45 / 120 non-compliant (37.5%)

### Most common missing tags
1. CostCenter -- missing on 180 resources
2. Owner -- missing on 95 resources
3. Team -- missing on 67 resources

### Estimated unattributable monthly cost
- Resources without CostCenter: $12,400/mo
- Resources without Owner: $8,200/mo (overlap with above)

2. policy -- Define and validate a tagging schema

Create a formal tagging policy document and validate existing tags against it.

Step 1: Generate a tagging policy from current usage
# Discover all tag keys currently in use (AWS)
aws resourcegroupstaggingapi get-tag-keys --output json | python3 -c "
import sys, json
data = json.load(sys.stdin)
keys = sorted(data.get('TagKeys', []))
print(f'Found {len(keys)} unique tag keys:')
for k in keys:
    print(f'  - {k}')
"

# Discover tag value patterns for each key
for key in Environment Owner Team CostCenter; do
  echo "=== $key ==="
  aws resourcegroupstaggingapi get-tag-values --key "$key" --output text | sort | uniq -c | sort -rn | head -10
done
Step 2: Detect tag value drift and inconsistencies

Tag drift is when the same concept has multiple spellings: "prod" vs "production" vs "Production". This silently breaks cost allocation and IAM policies.

aws resourcegroupstaggingapi get-tag-values --key "Environment" --output json | python3 -c "
import sys, json

data = json.load(sys.stdin)
values = data.get('TagValues', [])
normalized = {}
for v in values:
    norm = v.lower().strip().replace('-', '').replace('_', '')
    if norm not in normalized:
        normalized[norm] = []
    normalized[norm].append(v)

print('Tag value inconsistencies for Environment:')
for norm, variants in normalized.items():
    if len(variants) > 1:
        print(f'  DRIFT: {variants} -- standardize to one value')
    else:
        print(f'  OK:    {variants[0]}')
"
Step 3: Generate preventive enforcement policies

Preventive controls stop non-compliant resources from being created. This is stronger than detective controls (scanning after the fact).

# Generate an AWS SCP that denies resource creation without required tags
import json

scp = {
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "DenyEC2WithoutRequiredTags",
            "Effect": "Deny",
            "Action": ["ec2:RunInstances"],
            "Resource": ["arn:aws:ec2:*:*:instance/*", "arn:aws:ec2:*:*:volume/*"],
            "Condition": {
                "Null": {
                    "aws:RequestTag/Environment": "true",
                    "aws:RequestTag/Owner": "true",
                    "aws:RequestTag/Team": "true"
                }
            }
        },
        {
            "Sid": "DenyRDSWithoutRequiredTags",
            "Effect": "Deny",
            "Action": ["rds:CreateDBInstance", "rds:CreateDBCluster"],
            "Resource": ["*"],
            "Condition": {
                "Null": {
                    "aws:RequestTag/Environment": "true",
                    "aws:RequestTag/Owner": "true"
                }
            }
        },
        {
            "Sid": "DenyS3WithoutRequiredTags",
            "Effect": "Deny",
            "Action": ["s3:CreateBucket"],
            "Resource": ["*"],
            "Condition": {
                "Null": {
                    "aws:RequestTag/Environment": "true",
                    "aws:RequestTag/Owner": "true"
                }
            }
        }
    ]
}
print(json.dumps(scp, indent=2))
# Generate a Terraform default_tags block for the AWS provider
cat <<'TF'
provider "aws" {
  region = var.region

  default_tags {
    tags = {
      Environment = var.environment
      Owner       = var.team_email
      Team        = var.team_name
      CostCenter  = var.cost_center
      Project     = var.project_name
      ManagedBy   = "terraform"
    }
  }
}
TF
Step 4: Validate tag values against the schema
import re, json

schema = {
    "Environment": {"allowed": ["production", "staging", "development", "sandbox"]},
    "Owner": {"pattern": r"^[a-z.]+@[a-z]+\.[a-z]+$"},
    "Team": {"required": True},  # any non-empty value
    "CostCenter": {"pattern": r"^CC-[0-9]{4}$"},
    "Project": {"required": True},
}

def validate_tags(tags: dict, schema: dict) -> list:
    """Return list of violations for a set of tags."""
    violations = []
    for key, rules in schema.items():
        value = tags.get(key, "")
        if not value:
            violations.append(f"MISSING: {key}")
            continue
        if "allowed" in rules and value not in rules["allowed"]:
            violations.append(f"INVALID VALUE: {key}={value} (allowed: {rules['allowed']})")
        if "pattern" in rules and not re.match(rules["pattern"], value):
            violations.append(f"BAD FORMAT: {key}={value} (expected: {rules['pattern']})")
    return violations

# Example usage
test_tags = {"Environment": "prod", "Owner": "jdoe", "CostCenter": "1234"}
for v in validate_tags(test_tags, schema):
    print(f"  {v}")
# Output:
#   INVALID VALUE: Environment=prod (allowed: ['production', ...])
#   BAD FORMAT: Owner=jdoe (expected: ^[a-z.]+@[a-z]+\.[a-z]+$)
#   BAD FORMAT: CostCenter=1234 (expected: ^CC-[0-9]{4}$)
#   MISSING: Team
#   MISSING: Project
Report template
## Tagging Policy Definition

### Required Tags (all resources)
| Tag Key | Allowed Values | Pattern | Purpose |
|---------|----------------|---------|---------|
| Environment | production, staging, development, sandbox | - | Cost allocation, access control |
| Owner | - | email format | Accountability |
| Team | any non-empty | - | Org mapping |
| CostCenter | - | CC-NNNN | Finance allocation |
| Project | any non-empty | - | Project tracking |

### Enforcement Mechanisms
- **Preventive:** SCP denies resource creation without required tags
- **Detective:** Daily scan via this skill, alerts to Slack/PagerDuty
- **Corrective:** Auto-remediation scripts apply placeholder values

### Tag Value Drift Detected
- Environment: "prod" vs "production" (12 resources affected)
- Team: "Platform" vs "platform-team" (8 resources affected)

3. enforce -- Generate remediation scripts

Produce executable scripts that add missing tags to non-compliant resources.

Step 1: Generate AWS bulk remediation
# Generate a remediation script for all non-compliant resources
aws resourcegroupstaggingapi get-resources --no-paginate --output json | python3 -c "
import sys, json

required = {
    'Environment': 'NEEDS-CLASSIFICATION',
    'Owner': 'NEEDS-OWNER',
    'Team': 'NEEDS-TEAM',
    'CostCenter': 'CC-0000',
    'Project': 'NEEDS-PROJECT'
}
data = json.load(sys.stdin)
commands = []

for resource in data.get('ResourceTagMappingList', []):
    arn = resource['ResourceARN']
    tags = {t['Key']: t['Value'] for t in resource.get('Tags', [])}
    missing = {k: v for k, v in required.items() if k not in tags}

    if missing:
        tags_json = json.dumps(missing)
        commands.append(f'aws resourcegroupstaggingapi tag-resources --resource-arn-list \"{arn}\" --tags \'{tags_json}\'')

print('#!/bin/bash')
print('# Auto-generated tagging remediation script')
print(f'# {len(commands)} resources to remediate')
print('# Review before running -- placeholder values (NEEDS-*) must be replaced by owners')
print('set -euo pipefail')
print()
for i, cmd in enumerate(commands):
    print(f'echo \"[{i+1}/{len(commands)}] Tagging resource...\"')
    print(cmd)
    print('sleep 0.2  # rate limiting')
    print()
"
Step 2: Generate Terraform tag fixes

For resources managed by Terraform, fix the source of truth rather than patching live.

# Find Terraform resource blocks missing tags
rg -l 'resource "aws_' . -g '*.tf' | while read tf_file; do
  python3 -c "
import re

with open('$tf_file') as f:
    content = f.read()

blocks = re.finditer(r'resource\s+\"(aws_\w+)\"\s+\"(\w+)\"\s*\{', content)
for match in blocks:
    rtype, rname = match.group(1), match.group(2)
    # Find the block end by counting braces
    start = match.end()
    depth = 1
    pos = start
    while depth > 0 and pos < len(content):
        if content[pos] == '{': depth += 1
        elif content[pos] == '}': depth -= 1
        pos += 1
    block = content[start:pos]
    if 'tags' not in block:
        print(f'MISSING tags in $tf_file: {rtype}.{rname}')
"
done
Step 3: Generate GCP label remediation
# GCP: Produce gcloud commands to set missing labels
gcloud compute instances list --format=json | python3 -c "
import sys, json

required = {'environment': 'needs-classification', 'owner': 'needs-owner', 'team': 'needs-team', 'cost-center': 'cc-0000'}
instances = json.load(sys.stdin)

print('#!/bin/bash')
print('# GCP label remediation script')
print('set -euo pipefail')
for inst in instances:
    labels = inst.get('labels', {})
    missing = {k: v for k, v in required.items() if k not in labels}
    if missing:
        zone = inst['zone'].split('/')[-1]
        label_str = ','.join(f'{k}={v}' for k, v in missing.items())
        print(f'gcloud compute instances update {inst[\"name\"]} --zone={zone} --update-labels={label_str}')
"
Step 4: Dry-run validation

Always validate before applying. Compare pre- and post-remediation compliance.

# Pre-remediation compliance count
echo "=== Pre-remediation compliance ==="
aws resourcegroupstaggingapi get-resources --no-paginate --output json | python3 -c "
import sys, json
required_keys = ['Environment', 'Owner', 'Team', 'CostCenter', 'Project']
data = json.load(sys.stdin)
total = len(data.get('ResourceTagMappingList', []))
compliant = sum(1 for r in data.get('ResourceTagMappingList', [])
                if all(k in {t['Key'] for t in r.get('Tags', [])} for k in required_keys))
print(f'Total: {total}, Compliant: {compliant} ({compliant/total*100:.1f}%)')
print(f'Non-compliant: {total - compliant} ({(total-compliant)/total*100:.1f}%)')
"

# After remediation, re-run the same check to confirm improvement
Report template
## Tagging Remediation Report

**Date:** YYYY-MM-DD
**Scope:** AWS account 123456789012
**Mode:** Dry run / Applied

### Remediation Summary
- Resources targeted: 235
- Tags added: 580 (across 5 required keys)
- Resources now compliant: 235 / 235

### Remediation breakdown
| Tag Key | Resources Fixed | Default Value Applied |
|---------|----------------|----------------------|
| Environment | 45 | NEEDS-CLASSIFICATION |
| Owner | 95 | NEEDS-OWNER |
| Team | 67 | NEEDS-TEAM |
| CostCenter | 180 | CC-0000 |
| Project | 193 | NEEDS-PROJECT |

### Next steps
1. Resource owners must replace placeholder values (NEEDS-*) within 7 days
2. SCP enforcement activates in 14 days -- non-compliant creates will be denied
3. Re-scan scheduled for YYYY-MM-DD to verify owner updates
4. Escalate any resources still carrying NEEDS-* after deadline

平台分布

OpenClaw

74.25%
按下载量换算303

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。

继续浏览同类 Skills