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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.

    $19.99

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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.

    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.

    Cannot modify cloud resources directly. Requires billing exports or usage metrics to provide dollar-value estimates. Does not support niche, proprietary cloud providers outside the big three.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Listed1 month ago

    What's inside

    Frequently Asked Questions

    The agent maps cloud billing data to your application's architecture and source code, allowing it to pinpoint specific code patterns—like inefficient database queries or hot loops—that are driving up infrastructure costs.

    This skill requires access to cloud provider billing exports (AWS, GCP, or Azure) and your Infrastructure-as-Code (Terraform or Pulumi) files to accurately correlate costs with your service architecture.

    No, the agent is designed for analysis and planning only; it generates detailed implementation roadmaps and validation steps but will never modify your live infrastructure or code.

    Unlike general LLMs that suggest basic resource right-sizing, this skill uses a specialized 'MAP-MEASURE-TRACE' workflow to identify deep-seated logic inefficiencies that common DevOps tools often miss.

    You receive the complete skill configuration and prompt architecture, compatible with leading AI agent frameworks, along with documentation on how to connect your billing exports and repositories.

    It is highly effective for microservices, serverless functions (Lambda/Cloud Functions), and containerized applications running on Kubernetes where cost-to-code attribution is typically difficult.

    New in DevOps & Cloud