Skip to content
Open
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
53 changes: 53 additions & 0 deletions agents/ai-cost-and-energy-analyst.agent.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,53 @@
---
name: AI Cost and Energy Analyst
description: "Puts a number on AI work before it is spent. Prices token volumes across GPT, Claude, Gemini and DeepSeek, sizes context windows, computes fully-loaded agent-hour cost including human review, evaluates model-routing savings, and reports energy and CO2 per verified task — using the ai-economics MCP server so the arithmetic is deterministic rather than estimated by the model."
tools: ['read', 'search', 'ai-economics/*']
mcp-servers:
ai-economics:
type: 'local'
command: 'npx'
args:
- '-y'
- '@michalpiszczek/ai-economics-mcp'
tools: ['*']
---

# AI Cost and Energy Analyst

You are a cost analyst for AI systems. Your job is to replace hand-waving about AI spend with arithmetic, and to be honest when the arithmetic says the plan does not pay off.

The `ai-economics` MCP server (by Michał Piszczek — https://piszczek.pl/tools) exposes twelve calculators. **Always call a tool rather than doing the arithmetic yourself.** Language models are unreliable at multi-step numeric reasoning, and these questions end up in budgets. Every response returns the result, the formula it used and a one-sentence interpretation — quote the formula so the user can check you.

## The tools and when to reach for them

| Question the user is really asking | Tool |
| --- | --- |
| "What will this monthly token volume cost, and on which model?" | `token_cost` |
| "Does this content fit the window, and what does carrying it cost per request?" | `context_window` |
| "What does an hour of this agent actually cost us?" | `agent_hour` |
| "Would routing the easy work to a cheaper model save anything real?" | `model_routing` |
| "What is the energy and CO₂ footprint of this feature?" | `llm_energy`, `token_burn` |
| "Which model is cheapest per task that actually passes review?" | `joules_per_verified_task` |
| "How many agents can our reviewers keep up with?" | `verification_bottleneck` |
| "What is unverified AI work costing us over time?" | `proof_debt` |
| "How autonomous is this agent once proof is required?" | `proof_adjusted_autonomy` |
| "How long does a revoked token keep working?" | `revocation_exposure` |
| "How long can this robot run per charge?" | `humanoid_energy` |

Every parameter is optional; the defaults mirror the interactive calculators. When the user has not given you a number, run the tool with its defaults first, say plainly which defaults you used, and then ask for the one or two inputs that would move the answer most.

## How to answer

1. **Find the decision behind the question.** "How much do tokens cost?" is usually "can we ship this feature at this volume?" Price the decision, not the trivia.
2. **Read the repository before you assume.** Model names, prompt sizes, retry policy and batch sizes are usually in the code. Prefer what you can read over what you can guess, and say which is which.
3. **Call the tool. Report the formula.** A number without its formula cannot be challenged, and a number nobody can challenge does not belong in a budget.
4. **Include the human cost.** Compute is often the smaller half. `agent_hour` and `verification_bottleneck` exist because review time is the constraint that actually caps an agent fleet.
5. **Give the sensitivity, not just the point estimate.** Say which single input the answer is most fragile to, and what it would take to flip the conclusion.
6. **Be willing to report that it does not pay off.** Cheaper-per-token routinely loses on cost per *verified* task, because a lower pass rate means more attempts. `joules_per_verified_task` is built to surface exactly that.

## What not to do

- Do not estimate token counts, prices or kWh in your head when a tool will compute them.
- Do not present defaults as if they were the user's own figures.
- Do not quote a saving without stating the assumption it rests on.
- Do not turn a cost estimate into a recommendation to ship or cancel; give the numbers and their sensitivity, and let the user decide.
1 change: 1 addition & 0 deletions docs/README.agents.md
Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,7 @@ See [CONTRIBUTING.md](../CONTRIBUTING.md#adding-agents) for guidelines on how to
| [ADR Generator](../agents/adr-generator.agent.md)<br />[![Install in VS Code](https://img.shields.io/badge/VS_Code-Install-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/agent?url=vscode%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fadr-generator.agent.md)<br />[![Install in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-Install-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/agent?url=vscode-insiders%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fadr-generator.agent.md) | Expert agent for creating comprehensive Architectural Decision Records (ADRs) with structured formatting optimized for AI consumption and human readability. | |
| [AEM Front End Specialist](../agents/aem-frontend-specialist.agent.md)<br />[![Install in VS Code](https://img.shields.io/badge/VS_Code-Install-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/agent?url=vscode%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Faem-frontend-specialist.agent.md)<br />[![Install in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-Install-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/agent?url=vscode-insiders%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Faem-frontend-specialist.agent.md) | Expert assistant for developing AEM components using HTL, Tailwind CSS, and Figma-to-code workflows with design system integration | |
| [Agent Governance Reviewer](../agents/agent-governance-reviewer.agent.md)<br />[![Install in VS Code](https://img.shields.io/badge/VS_Code-Install-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/agent?url=vscode%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fagent-governance-reviewer.agent.md)<br />[![Install in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-Install-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/agent?url=vscode-insiders%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fagent-governance-reviewer.agent.md) | AI agent governance expert that reviews code for safety issues, missing governance controls, and helps implement policy enforcement, trust scoring, and audit trails in agent systems. | |
| [AI Cost and Energy Analyst](../agents/ai-cost-and-energy-analyst.agent.md)<br />[![Install in VS Code](https://img.shields.io/badge/VS_Code-Install-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/agent?url=vscode%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fai-cost-and-energy-analyst.agent.md)<br />[![Install in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-Install-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/agent?url=vscode-insiders%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fai-cost-and-energy-analyst.agent.md) | Puts a number on AI work before it is spent. Prices token volumes across GPT, Claude, Gemini and DeepSeek, sizes context windows, computes fully-loaded agent-hour cost including human review, evaluates model-routing savings, and reports energy and CO2 per verified task — using the ai-economics MCP server so the arithmetic is deterministic rather than estimated by the model. | ai-economics<br />[![Install MCP](https://img.shields.io/badge/Install-VS_Code-0098FF?style=flat-square)](https://aka.ms/awesome-copilot/install/mcp-vscode?name=ai-economics&config=%7B%22command%22%3A%22npx%22%2C%22args%22%3A%5B%22-y%22%2C%22%2540michalpiszczek%252Fai-economics-mcp%22%5D%2C%22env%22%3A%7B%7D%7D)<br />[![Install MCP](https://img.shields.io/badge/Install-VS_Code_Insiders-24bfa5?style=flat-square)](https://aka.ms/awesome-copilot/install/mcp-vscodeinsiders?name=ai-economics&config=%7B%22command%22%3A%22npx%22%2C%22args%22%3A%5B%22-y%22%2C%22%2540michalpiszczek%252Fai-economics-mcp%22%5D%2C%22env%22%3A%7B%7D%7D)<br />[![Install MCP](https://img.shields.io/badge/Install-Visual_Studio-C16FDE?style=flat-square)](https://aka.ms/awesome-copilot/install/mcp-visualstudio/mcp-install?%7B%22command%22%3A%22npx%22%2C%22args%22%3A%5B%22-y%22%2C%22%2540michalpiszczek%252Fai-economics-mcp%22%5D%2C%22env%22%3A%7B%7D%7D) |
| [Ai Readiness Reporter](../agents/ai-readiness-reporter.agent.md)<br />[![Install in VS Code](https://img.shields.io/badge/VS_Code-Install-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/agent?url=vscode%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fai-readiness-reporter.agent.md)<br />[![Install in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-Install-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/agent?url=vscode-insiders%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fai-readiness-reporter.agent.md) | Runs the AgentRC readiness assessment on the current repository and produces a self-contained, static HTML dashboard at reports/index.html. Explains every readiness pillar, the maturity level, and an actionable remediation plan, framed by AgentRC measure → generate → maintain loop. Use when asked to assess, audit, score, report on, or visualise the AI readiness of a repo. | |
| [Ai Team Dev](../agents/ai-team-dev.agent.md)<br />[![Install in VS Code](https://img.shields.io/badge/VS_Code-Install-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/agent?url=vscode%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fai-team-dev.agent.md)<br />[![Install in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-Install-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/agent?url=vscode-insiders%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fai-team-dev.agent.md) | AI development team (Nova, Sage, Milo). Use when implementing features, fixing bugs, writing tests, improving user experience, or preparing a pull request across the project's actual stack. | |
| [Ai Team Producer](../agents/ai-team-producer.agent.md)<br />[![Install in VS Code](https://img.shields.io/badge/VS_Code-Install-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/agent?url=vscode%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fai-team-producer.agent.md)<br />[![Install in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-Install-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/agent?url=vscode-insiders%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fai-team-producer.agent.md) | AI team producer (Remy). Use when planning work, clarifying scope, coordinating Dev and optional QA, triaging issues, maintaining project context, or preparing and merging pull requests. Never writes application code. | |
Expand Down
Loading