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+---
+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.
diff --git a/docs/README.agents.md b/docs/README.agents.md
index b2a094825..473206e3e 100644
--- a/docs/README.agents.md
+++ b/docs/README.agents.md
@@ -30,6 +30,7 @@ See [CONTRIBUTING.md](../CONTRIBUTING.md#adding-agents) for guidelines on how to
| [ADR Generator](../agents/adr-generator.agent.md)
[](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)
[](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)
[](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)
[](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)
[](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)
[](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)
[](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)
[](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
[](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)
[](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)
[](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)
[](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)
[](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)
[](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)
[](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)
[](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)
[](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. | |