# Bootstrap prompt

Paste the prompt below into an agent that can reach the Metix AI Platform: one with the [metix-skills](https://github.com/MetixAI-Official/metix-skills) installed, the MCP server connected, or plain REST access with `METIX_KEY` set. Followed end to end, it costs about 80 to 95 API Credits: about 68 for the counts and the rest for three small reads that check the matches. It stops and asks before 110.

```text
Answer one question with the Metix AI Platform: where in the US, and by whom, are companies hiring to build and run data centers? Work only through the public Platform (REST at https://mira-api.metix.ai, the MCP server, or the metix-skills) with the key in METIX_KEY, and never print the key.

1. Read before querying. Call GET /contract (free) and use only querySpecByEntity.job fields. A count with size 1 costs 1 API Credit; reading records costs 1 API Credit per 5. Check the balance with GET /auth/key/status (free) at the start and at the end, and stop and ask before the run passes 110 API Credits.

2. Population. Open US postings whose title matches "data center". Read 25 titles to confirm they are data center jobs.

3. Count by state. One count per state for the 30 largest, then one count for every other state (not in the list). The national total is their sum; do not rely on a single total, which can be banded.

4. Count by employer. Match company.name exactly as the records spell it; read a few records to learn the spellings (Amazon posts under two names). Report each employer's share of the national total, and Amazon's count in the two largest states.

5. Count by role. One count per word in the title: technician, manager, engineer, construction, operations, mechanical, security. A title can hold more than one word; say that the rows overlap.

6. The trades. Count US electricians (title "electrician") and construction leads (title "construction manager", "superintendent" or "project engineer"), then the same with a description that names data centers ("data center", "datacenter" or "data centre"). Read 30 of each matched set to check that the mention is about data center work, not a list of sectors, and report how many were.

7. Clean. Reposts and multi-city listings are not collapsed; say that the numbers measure hiring activity, not seats.

8. Outputs. Write data/states.json, employers.json, roles.json and trades.json with "unit": "jobs", the snapshot date and each row's count and share. Report the balance before and after as the cost.

9. Charts. States as bars scaled to the national total, the largest highlighted and every other state last. Employers and roles as shares of the national total. The trades as the share of each population that names data centers.

10. Limits. Titles only for the main population. One day, not a trend: open postings skew recent, so posting dates say nothing about growth.
```

## Adapt it

| To change | Edit | For example |
| --- | --- | --- |
| The industry | Step 2 | "semiconductor", "battery", "wind" |
| The trades | Step 6 | "HVAC", "pipefitter", "lineman" |
| The breakdown | Step 3 | Cities within one state, or metro areas |

## What to ask before running it

1. Which title, and does it mean anything else?
2. States, cities or employers?