Hire an AI cloud and AI bills, waste and spend limits

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• Each line against last month, with the sums
• The one to three lines that explain the change, and the cause if it can be found
• Spend caps and loop limits for AI calls, before any other saving
• A saving list to approve, each with its size, effort and risk

How it does the job well

• Read the existing code, its conventions and its tests before changing anything. Match the project's patterns.
• Make the smallest change that fully solves the request. Keep unrelated refactors out.
• Reproduce a bug before fixing it, and add a test that fails without the fix.
• Treat input from users, files and networks as untrusted. Validate it at the boundary.

Mistakes it avoids

• Calling a bill high without last month or a budget beside it.
• Cutting a resource because it looks idle. Check what depends on it, and ask first.
• Counting a one-off launch cost as waste.
• Buying long commitments to save a few percent when usage is still changing.
• Claiming code works without running it or its tests.
• Inventing APIs, package versions or settings instead of checking the installed ones.

What a good result looks like

An example, not your task: You pay for cloud, hosting or AI services and nobody reads the bills closely.

• This month. The total against last month, the change in money and percent, and the budget if you gave one.
• What moved. The one to three lines that explain most of the change, with the cause or a note that it is not known.
• Caps to put in place. Spend caps and loop limits for AI calls, with the file or setting each one goes in.
• Savings to approve. Up to 10 fixes, biggest first, each with its monthly saving, the time to do it and the risk.
• Last month's fixes. Each fix you approved before, with its line before and now, so you see if it worked.
• Watch next month. The one to three lines most likely to move next.

How the work is checked

• Every total and change is shown with its sums.
• Spend caps come before savings when AI spend is climbing.
• No saving counts a cost twice, and none is claimed without a line it comes off.
• Each fix says what could break if it is done wrong.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

Capabilities

• Total each line. Sums every service and provider and shows the sums.
• Compare with last month. Gives the change in money and percent for each line.
• Find the mover. Names the lines that explain most of the change and sets them against what you said changed.
• Choose the path. If AI spend is up sharply, it checks for missing caps first. Then it runs the AWS or Vercel check that fits your setup, and the AI cost check when AI is a top line.
• Test each saving. Writes the monthly saving and the time to do it, and drops a fix that is not worth the effort.
• Hand back and stop. Gives one list to approve, saves it, and checks next month whether the lines fell.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id monthly-cloud-and-ai-bill-keeper only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/monthly-cloud-and-ai-bill-keeper/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• This month's bills as a spreadsheet or export, one per provider
• Last month's bills, or let the agent save them from the first run
• The code, if you want caps added to AI calls (optional)
• What changed this month, such as a launch or a new feature (optional)
• Read-only access to one named account, only if you want it to run checks itself (optional; exports work without it)
• A monthly budget (optional)

Helpful context (optional)

• Your usage numbers for AI calls, by feature
• A list of what each server or service is for

Tools and access

• Claude Code: It can read the code, keep the bills folder and run the checks on a schedule.
• AWS Cost Explorer: Export cost by service and by day, which shows when a jump started.
• Claude Code: It can run the AWS command line for you and show each command before it runs.
• Any good chat AI: Works if you run the commands yourself and paste the results, or paste a Cost Explorer CSV.

Functions and deliverables

• Cloud and AI bills, waste and spend limits: Compare cloud and AI bills. Find the largest changes, check spending limits and rank possible savings.
  • Each line against last month, with the sums
  • The one to three lines that explain the change, and the cause if it can be found
  • Spend caps and loop limits for AI calls, before any other saving
  • A saving list to approve, each with its size, effort and risk
• AWS cost waste finder: Find idle servers, loose disks and other AWS waste, with a ranked list of fixes and what each could save you every month.
  • Spend by service, region and tag, with the months that jumped
  • A table of idle and unused resources, sorted by monthly saving
  • A reason and a confidence level for every row
  • Right-sizing and savings plan options
  • A list of what it could not check
  Source: sickn33.
• Vercel cost and speed auditor: Find what makes your Vercel app cost too much or load slowly, using real production numbers. Get a ranked list of issues with a fix for each.
  • A file of production numbers pulled from your Vercel account
  • A ranked list of cost and speed issues
  • A fix for each issue, tied to the exact route or setting
  • Links to docs that match your framework version
  • A short report you can share with your team
  Source: sickn33.
• AI cost cutter: Share your AI app’s usage, costs and quality limits. Get a cost audit and changes to test, with estimated savings and trade-offs.
  • A map of where cost comes from
  • A ranked list of savings
  • A model routing plan
  • A caching plan with target hit rates
  • A way to track cost per request
  Source: Alireza Rezvani.
• AI spend limit checker: Check code that calls paid AI services for spend limits. Get a cost cap on every call, a hard count on every loop and retry, and a list of caps to set.
  • A cost line for each call site
  • Hard limits on loops and retries
  • Repeat-safe keys where calls can double up
  • A list of provider caps to set
  Source: sickn33.
• Cloud monitoring planner: Share your app and cloud setup. Get a plan, dashboards and alert settings to track service health.
  • Service health targets
  • Metric, log and trace setup
  • Dashboards by service
  • Alert rules with fewer false alarms
  Source: Burhan Sebin.
• AI project context organiser: Share your project and next task. Get a short rules file, a focused file list and a clear handoff for later sessions.
  • A ready rules file for your project
  • A list of which files to load for each task
  • Rules for when to reset or trim the session
  • A checklist for spotting drifting answers
  Source: Addy Osmani.

Tasks you can use it for

• You pay for cloud, hosting or AI services and nobody reads the bills closely.
• Your AI bill jumped and you do not know why.
• You want to know whether last month's savings really showed up.

Limits to check before starting

• You want a one-off review of a single bill with no earlier month to compare: use AWS cost waste finder for an AWS bill, or ask a person who knows your setup.
• You want waste fixed without your sign-off: this agent only hands back a list, so make the changes yourself or approve each one.
• You only want to check one AWS account for waste: run cloud-bill-waste-finder alone, with no monthly history needed.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Start a chat with the agent. In Claude Code, add it as a skill or save it in .claude/agents, then ask it to run this month's check.
• Put the final bills in one folder, or attach them in the chat.
• Point it at the code if you want caps added. Exports from your billing pages need no credentials. If you let it run commands, give it a read-only role or user for one named account, never paste keys into the chat, and delete the role or key in the provider's console when the check is done.
• Read the list. Say yes to the fixes you want, or make them yourself.
• Run it next month. It checks whether each fix brought its line down.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/monthly-cloud-and-ai-bill-keeper for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.