Start with your coding agent

Read the Surface Area guides installed in .claude/skills (run `gateway skill list` if you can't find them) and check `gateway auth status`. Then look at this repository and tell me, in a short list:
1. which external systems and APIs my agent calls,
2. which of them Surface Area ships a world template for (`gateway worlds schema templates`),
3. the smallest first world and scenario you would build to test my agent, and why.
Don't create anything yet.

Using the worlds-getting-started guide, create a world for <vendor, e.g. Slack> from the shipped template, open a session on it, call one of its routes, grade the session, and close it. Show me each command you ran and the world's link in the dashboard.

Using the worlds-getting-started guide, build a world from our API spec at <path or URL to openapi.yaml> with `gateway worlds schema init <dir> --from-openapi <spec>`. Keep every entity and route the spec declares, add a few realistic rows of invented data, run `gateway worlds schema check` until it passes, then publish the first version and open a session to prove the routes answer.

Using the world-data-ingestion guide, load <file, export, or "the tool calls in our recent traces"> into the world <world slug>. Run `gateway worlds describe` first so the rows match the world's entities and keys, report anything that was refused and why, and do not invent fields that aren't in the source.

Using the worlds-getting-started and benchmarks-getting-started guides, write <number> scenarios for the world <world slug> that cover what my agent in <path to agent code> is supposed to do. Each scenario needs a clear instruction and a grader that checks the world's end state. Then wire my agent up as an agent module, run every scenario with `gateway worlds run`, and summarise the pass rate and the failures.

Using the benchmarks-getting-started guide, turn <tasks, a harbor task tree, or a description of the job> into a benchmark. Pick the right kind (a world with tasks, or a container benchmark), validate it, do a dry run, and push it. Tell me how to run it against two models and read the per-task results.

Instrument the agent in <path> with the Surface Area SDK so every run is traced to my project. Use the tracing docs at https://docs.surfacearea.ai/tracing/setup, read credentials from GATEWAY_HOST, GATEWAY_PUBLIC_KEY and GATEWAY_SECRET_KEY, run it once, and confirm the trace appears with `gateway traces list`.

Add a CI job that runs my agent against the world <world slug> on every pull request and fails when the score drops below <threshold>, using `gateway worlds ci` (see `gateway worlds ci --help` and https://docs.surfacearea.ai/use-cases/evals-in-ci). Keep the agent module and world checked into the repository, keep the keys in CI secrets, and show me the workflow file before committing it.

Using the hooks-getting-started guide (`gateway skill show hooks-getting-started`), write a gateway-hooks.toml for the agent in <path> that <deny / record / redact what you need>. Validate it with `gateway hooks check gateway-hooks.toml` and wire it into the agent.