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Agent Skill · 研究检索

finops-expert(菲诺普斯专家)

finops-expert 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。

finops-expert 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 31,907。

总安装 2,618周安装 108GitHub Stars 19下载 855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

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:finops-expert(菲诺普斯专家)
来源仓库:https://github.com/personamanagmentlayer/pcl
仓库路径:skills/finops-expert
安装命令:
npx skills add https://github.com/personamanagmentlayer/pcl --skill finops-expert
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/personamanagmentlayer/pcl --skill finops-expert

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。

用于查找、检索和筛选相关信息,适合快速定位候选结果。可根据关键词、任务场景或来源线索进行信息聚合。安装方式:通过 npx 从 GitHub 仓库添加,支持 Codex、Claude 等宿主环境。使用前建议确认权限范围和维护状态,避免触发不必要的联网或命令执行。注意维护状态,部分技能可能依赖外部服务或 API。

来源仓库内路径:skills/finops-expert

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/personamanagmentlayer/pcl --skill finops-exper…;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

    它需要需要联网,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

finops-expert 怎么安装?

在终端执行 npx skills add https://github.com/personamanagmentlayer/pcl --skill finops-expert。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

finops-expert 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

finops-expert 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

finops-expert 是免费的吗?

来源标注的许可证是 MIT。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。

finops-expert 用的人多吗?

公开数据是累计安装 2,618 次,近一周安装 108 次,下载 855 次,GitHub 19 星。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。

finops-expert 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 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 Codex,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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

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
finops-expert

菲诺普斯专家 · 本页

2,61819需要联网MIT通过

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

FinOps Expert

Expert guidance for cloud financial operations, cost optimization, resource management, and cloud economics.

Core Concepts

FinOps Fundamentals

  • Cloud cost visibility
  • Usage optimization
  • Rate optimization
  • Architecture optimization
  • Cloud unit economics
  • Showback and chargeback

Cost Management

  • Reserved Instances (RIs)
  • Savings Plans
  • Spot instances
  • Right-sizing resources
  • Idle resource cleanup
  • Storage lifecycle policies

FinOps Practices

  • Tagging strategies
  • Budgets and alerts
  • Cost allocation
  • Forecasting and planning
  • Cross-team collaboration
  • Continuous optimization

AWS Cost Analysis

import boto3
from datetime import datetime, timedelta
from typing import Dict, List
import pandas as pd

class AWSCostAnalyzer:
    """Analyze AWS costs using Cost Explorer API"""

    def __init__(self):
        self.ce_client = boto3.client('ce')

    def get_cost_and_usage(self, start_date: str, end_date: str,
                          granularity: str = 'DAILY',
                          metrics: List[str] = None) -> Dict:
        """Get cost and usage data"""
        if metrics is None:
            metrics = ['UnblendedCost', 'UsageQuantity']

        response = self.ce_client.get_cost_and_usage(
            TimePeriod={
                'Start': start_date,
                'End': end_date
            },
            Granularity=granularity,
            Metrics=metrics,
            GroupBy=[
                {'Type': 'DIMENSION', 'Key': 'SERVICE'}
            ]
        )

        return response['ResultsByTime']

    def get_top_services_by_cost(self, days: int = 30, top_n: int = 10) -> pd.DataFrame:
        """Get top services by cost"""
        end_date = datetime.now().strftime('%Y-%m-%d')
        start_date = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')

        results = self.get_cost_and_usage(start_date, end_date, 'MONTHLY')

        service_costs = {}
        for result in results:
            for group in result['Groups']:
                service = group['Keys'][0]
                cost = float(group['Metrics']['UnblendedCost']['Amount'])

                if service in service_costs:
                    service_costs[service] += cost
                else:
                    service_costs[service] = cost

        df = pd.DataFrame(list(service_costs.items()),
                         columns=['Service', 'Cost'])
        return df.nlargest(top_n, 'Cost')

    def get_cost_forecast(self, days_ahead: int = 30) -> Dict:
        """Get cost forecast"""
        start_date = datetime.now().strftime('%Y-%m-%d')
        end_date = (datetime.now() + timedelta(days=days_ahead)).strftime('%Y-%m-%d')

        response = self.ce_client.get_cost_forecast(
            TimePeriod={
                'Start': start_date,
                'End': end_date
            },
            Metric='UNBLENDED_COST',
            Granularity='MONTHLY'
        )

        return {
            'forecasted_cost': float(response['Total']['Amount']),
            'mean_value': float(response['ForecastResultsByTime'][0]['MeanValue'])
        }

    def get_rightsizing_recommendations(self) -> List[Dict]:
        """Get EC2 rightsizing recommendations"""
        response = self.ce_client.get_rightsizing_recommendation(
            Service='AmazonEC2'
        )

        recommendations = []
        for rec in response['RightsizingRecommendations']:
            recommendations.append({
                'instance_id': rec['CurrentInstance']['ResourceId'],
                'current_type': rec['CurrentInstance']['InstanceType'],
                'recommended_type': rec['ModifyRecommendationDetail']['TargetInstances'][0]['InstanceType']
                    if rec.get('ModifyRecommendationDetail') else None,
                'estimated_savings': float(rec['EstimatedMonthlySavings']['Value'])
                    if rec.get('EstimatedMonthlySavings') else 0
            })

        return recommendations

class CostOptimizer:
    """Optimize cloud costs"""

    def __init__(self):
        self.ec2_client = boto3.client('ec2')
        self.rds_client = boto3.client('rds')
        self.s3_client = boto3.client('s3')

    def find_idle_resources(self) -> Dict[str, List]:
        """Find idle/unused resources"""
        idle_resources = {
            'ec2_instances': [],
            'ebs_volumes': [],
            'elastic_ips': [],
            'load_balancers': []
        }

        # Idle EC2 instances (stopped for > 7 days)
        instances = self.ec2_client.describe_instances(
            Filters=[{'Name': 'instance-state-name', 'Values': ['stopped']}]
        )

        for reservation in instances['Reservations']:
            for instance in reservation['Instances']:
                idle_resources['ec2_instances'].append({
                    'id': instance['InstanceId'],
                    'type': instance['InstanceType'],
                    'state': instance['State']['Name']
                })

        # Unattached EBS volumes
        volumes = self.ec2_client.describe_volumes(
            Filters=[{'Name': 'status', 'Values': ['available']}]
        )

        for volume in volumes['Volumes']:
            idle_resources['ebs_volumes'].append({
                'id': volume['VolumeId'],
                'size': volume['Size'],
                'type': volume['VolumeType']
            })

        # Unattached Elastic IPs
        addresses = self.ec2_client.describe_addresses()

        for address in addresses['Addresses']:
            if 'InstanceId' not in address:
                idle_resources['elastic_ips'].append({
                    'allocation_id': address['AllocationId'],
                    'public_ip': address['PublicIp']
                })

        return idle_resources

    def calculate_reserved_instance_savings(self,
                                           instance_type: str,
                                           count: int,
                                           term: int = 1) -> Dict:
        """Calculate RI savings"""
        # Simplified calculation (would use actual pricing API)
        on_demand_hourly = self._get_on_demand_price(instance_type)
        ri_hourly = on_demand_hourly * 0.65  # ~35% discount

        hours_per_year = 24 * 365
        annual_on_demand = on_demand_hourly * hours_per_year * count
        annual_ri = ri_hourly * hours_per_year * count

        return {
            'instance_type': instance_type,
            'count': count,
            'annual_on_demand_cost': annual_on_demand,
            'annual_ri_cost': annual_ri,
            'annual_savings': annual_on_demand - annual_ri,
            'savings_percentage': ((annual_on_demand - annual_ri) / annual_on_demand) * 100
        }

    def _get_on_demand_price(self, instance_type: str) -> float:
        """Get on-demand hourly price (simplified)"""
        # In production, use AWS Pricing API
        prices = {
            't3.micro': 0.0104,
            't3.small': 0.0208,
            't3.medium': 0.0416,
            'm5.large': 0.096,
            'm5.xlarge': 0.192
        }
        return prices.get(instance_type, 0.10)

Cost Allocation and Tagging

class CostAllocation:
    """Manage cost allocation with tags"""

    def __init__(self):
        self.ec2_client = boto3.client('ec2')
        self.ce_client = boto3.client('ce')

    def define_tagging_strategy(self) -> Dict[str, List[str]]:
        """Define mandatory tags"""
        return {
            'environment': ['prod', 'staging', 'dev'],
            'team': ['engineering', 'data', 'product'],
            'cost_center': ['CC001', 'CC002', 'CC003'],
            'project': ['project-a', 'project-b'],
            'owner': ['email addresses']
        }

    def audit_resource_tags(self, resource_type: str = 'instance') -> List[Dict]:
        """Audit resources for missing tags"""
        mandatory_tags = ['environment', 'team', 'cost_center']
        untagged_resources = []

        if resource_type == 'instance':
            instances = self.ec2_client.describe_instances()

            for reservation in instances['Reservations']:
                for instance in reservation['Instances']:
                    tags = {tag['Key']: tag['Value']
                           for tag in instance.get('Tags', [])}

                    missing_tags = [tag for tag in mandatory_tags
                                  if tag not in tags]

                    if missing_tags:
                        untagged_resources.append({
                            'resource_id': instance['InstanceId'],
                            'missing_tags': missing_tags
                        })

        return untagged_resources

    def get_cost_by_tag(self, tag_key: str, start_date: str,
                       end_date: str) -> pd.DataFrame:
        """Get costs grouped by tag"""
        response = self.ce_client.get_cost_and_usage(
            TimePeriod={
                'Start': start_date,
                'End': end_date
            },
            Granularity='MONTHLY',
            Metrics=['UnblendedCost'],
            GroupBy=[
                {'Type': 'TAG', 'Key': tag_key}
            ]
        )

        costs = []
        for result in response['ResultsByTime']:
            for group in result['Groups']:
                costs.append({
                    'tag_value': group['Keys'][0].split('$')[1]
                        if '$' in group['Keys'][0] else 'Untagged',
                    'cost': float(group['Metrics']['UnblendedCost']['Amount'])
                })

        return pd.DataFrame(costs)

Budget Management

class BudgetManager:
    """Manage AWS budgets and alerts"""

    def __init__(self):
        self.budgets_client = boto3.client('budgets')
        self.account_id = boto3.client('sts').get_caller_identity()['Account']

    def create_monthly_budget(self, name: str, amount: float,
                             email: str) -> Dict:
        """Create monthly cost budget with alerts"""
        budget = {
            'BudgetName': name,
            'BudgetLimit': {
                'Amount': str(amount),
                'Unit': 'USD'
            },
            'TimeUnit': 'MONTHLY',
            'BudgetType': 'COST'
        }

        # Alert at 80% and 100%
        notifications = [
            {
                'Notification': {
                    'NotificationType': 'ACTUAL',
                    'ComparisonOperator': 'GREATER_THAN',
                    'Threshold': 80,
                    'ThresholdType': 'PERCENTAGE'
                },
                'Subscribers': [{
                    'SubscriptionType': 'EMAIL',
                    'Address': email
                }]
            },
            {
                'Notification': {
                    'NotificationType': 'ACTUAL',
                    'ComparisonOperator': 'GREATER_THAN',
                    'Threshold': 100,
                    'ThresholdType': 'PERCENTAGE'
                },
                'Subscribers': [{
                    'SubscriptionType': 'EMAIL',
                    'Address': email
                }]
            }
        ]

        response = self.budgets_client.create_budget(
            AccountId=self.account_id,
            Budget=budget,
            NotificationsWithSubscribers=notifications
        )

        return response

Best Practices

Cost Visibility

  • Implement comprehensive tagging
  • Enable Cost Explorer
  • Set up cost allocation tags
  • Create custom cost reports
  • Use dashboards for visualization
  • Monitor costs daily

Optimization

  • Right-size resources regularly
  • Use Reserved Instances/Savings Plans
  • Leverage Spot instances for flexible workloads
  • Implement auto-scaling
  • Clean up idle resources
  • Use storage lifecycle policies

Governance

  • Set budgets and alerts
  • Implement approval workflows
  • Regular cost reviews
  • Cross-team accountability
  • Document cost optimization wins
  • Automate cost controls

Anti-Patterns

❌ No tagging strategy ❌ Ignoring rightsizing recommendations ❌ Not using Reserved Instances ❌ No budget alerts ❌ Keeping idle resources ❌ Manual cost tracking ❌ Siloed cost responsibility

Resources

平台分布

Claude Code

30.49%
按下载量换算261

OpenCode

20.85%
按下载量换算178

Antigravity

16%
按下载量换算137

Codex

12.62%
按下载量换算108

Gemini CLI

7.25%
按下载量换算62

windsurf

3.68%
按下载量换算31

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。

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