# 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 9 to 21 API Credits: 4 for the narrowing counts, 12 more if you compare cities, and 5 to read a shortlist of 25. It stops and asks before 30.

```text
Help me find machine learning engineers in San Francisco who work with PyTorch, using the Metix AI Platform. 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 read the profile fields current_title, location.country, location.city and skills, and the operators each allows. Check the balance with GET /auth/key/status (free) at the start and at the end, and stop and ask before the run passes 30 API Credits.

2. Population. Profiles whose current_title matches "machine learning engineer". Then add, one at a time and in this order: location.country eq "United States", location.city eq "San Francisco", skills match "pytorch". All conditions sit in one all node.

3. Count each step. Send each of the four filters with size 1 and record the total. Four counts, four API Credits. Show me the four totals as a table before going further.

4. Budget. Read records only after I have seen the counts. If the last step has fewer than 10 people, say so and suggest how to widen it instead of reading.

5. Compare cities (optional). Ask me whether to repeat the title count and the title plus skill count in New York, Seattle, Boston, Austin and Chicago. That is 10 more counts.

6. Read the shortlist. Search the last filter with size 25 and read those 25 profiles with only current_title, location.city, skills, experience and education (5 API Credits).

7. Audit. Tell me how many of the 25 are really machine learning engineers who used PyTorch in a recent role, and how many only list it. If more than a third are off target, propose a tighter filter.

8. Outputs. A table of the 25 with current title, current company, city, years in the current role and the evidence for the skill. Keep the records to our conversation; do not publish them.

9. Ranking. Order the table by how recently each person worked with the skill, then by time in role. Say what you ranked on.

10. Limits. Tell me what the search misses: people who never list skills, other titles for the same work, and people outside the city limits.
```

## Adapt it

| To change | Edit | For example |
| --- | --- | --- |
| The role | Step 2, the title | "data engineer", "backend engineer" |
| The place | Step 2, the country and city | "London" with "United Kingdom" |
| The skill | Step 2, the skill | "kubernetes", "rust" |
| The shortlist size | Step 6 | 50 profiles cost 10 API Credits |

## What to ask before running it

1. Which title, and are there other titles for the same work?
2. Which city, and does a nearby city count?
3. Which skill is a must-have, and which is only nice to have?