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    Cloud Cost-to-Code Optimization Agent ⭐

    1

    Traces cloud infrastructure costs back to application code and architecture to identify high-impact savings.

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    Cloud Cost-to-Code Optimization Agent ⭐

    Cloud Cost-to-Code Optimization Agent ⭐

    Example session with this skill installed

    Our AWS RDS bill increased by 40% last month despite steady user growth. Here is our Prisma schema and a sample of our slow query logs. Can you find the root cause and recommend a fix?

    • Read your context and instructions
    • Compiled the cloud cost-to-code optimization

    I have analyzed your Prisma schema and slow query logs against the reported RDS cost spike. The 40% increase is primarily driven by high IOPS and CPU utilization on your primary db.m5.large instance.

    Cost-to-Code Analysis: RDS Optimization

    The primary cost driver is a specific N+1 pattern in the Order retrieval logic, which triggers individual Profile and Address lookups for every item in a list view.

    OpportunitySavings PotentialEffortRiskConfidencePriority
    Batch Order RelationsHigh (~$350/mo)LowLowHighP0
    Add composite index on deleted_atMedium (~$120/mo)LowLowHighP1
    Enable RDS Proxy for poolingMediumMediumMediumMediumP2

    Implementation Plan: Batch Order Relations

    • Problem: High DB compute caused by 100+ sequential queries per API request.
    • Evidence: Logs show SELECT * FROM "Profile" WHERE "userId" = $1 executing 50 times in 200ms.
    • Root Cause: The getOrders service lacks an include statement for related entities.
    • Recommendation: Use Prisma's include or fluent API to fetch relations in a single join.
    • Steps:
      1. Update order.service.ts to include user: true and address: true.
      2. Deploy to staging and monitor db.cpu.utilization.
      3. Run load tests to verify latency reduction.
    • Validation: Verify queries_per_request drops from ~105 to <5.

    Next steps

    1. Apply the suggested Prisma include fix to the getOrders method.
    2. Check if Query Insights on AWS console shows a reduction in Top SQL load.
    3. Review the deleted_at filtering logic to ensure it hits the new index.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Trace RDS or DynamoDB cost spikes back to specific N+1 or unindexed queries.Right-size K8s nodes by analyzing container resource limits vs actual usage.Identify high-cost serverless patterns like chatty functions or long durations.Provide a prioritized FinOps roadmap based on engineering effort and risk.

    About this skill

    The problem

    Infrastructure bills grow decoupled from application logic, making it impossible to see which line of code is burning your budget. Developers often treat cost as a DevOps problem, missing massive savings hidden in inefficient code patterns like N+1 queries or chatty microservices.

    What it does

    • Maps cloud billing exports and usage metrics directly to specific application services and architecture components.
    • Identifies cost-heavy code patterns including hot loops, repeated API calls, and inefficient serialization.
    • Classes optimization opportunities by impact, effort, and risk to provide a prioritized engineering roadmap.
    • Generates detailed implementation plans with validation metrics and rollback steps to ensure production safety.
    • Analyzes trade-offs between cost, latency, and reliability to prevent regressions.

    Frameworks & tools

    Works with AWS, Azure, and GCP billing exports. Supports Kubernetes, Docker, serverless runtimes, and major managed database providers. Analyzes Infrastructure-as-Code (Terraform, Pulumi) and application source code.

    Why this beats prompting it yourself

    Generic LLMs suggest turning off idle instances. This skill traces the "Cost-to-Code" path, connecting a $1,000 AWS line item to a specific middleware function or database query. It enforces a rigorous MAP-MEASURE-TRACE workflow that prevents hallucinated savings and ensures technical feasibility.

    Use cases

    • Audit a ballooning RDS bill to find the specific N+1 queries driving CPU utilization.
    • Optimize Lambda execution costs by identifying long-running functions and chatty service chains.
    • Right-size Kubernetes clusters based on actual application workload behavior and scaling thresholds.
    • Review new feature code to estimate its impact on monthly cloud egress and storage costs.

    Known limitations

    Cannot execute cloud provider API calls to modify resources directly. Does not invent pricing data if billing exports or usage metrics are not provided.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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