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Explain Wisp's current managed-model endpoint boundary and plan a safe integration.
Passed 20 of 22 test cases with the skill, and 7 of 22 without it.
and 13 more in the full report
13 fixed7 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 13 test cases the skill fixed. Same model, same prompt, with and without the skill.
I need to run an inference task against an already deployed Triton inference server in my local WSL context using existing environment auth. What capability should be loaded to perform this?
| Checked | Without skill | With skill |
|---|---|---|
| Recommend loading using-model-endpoint to execute the bounded inference client. | Missed | Passed |
The instructions your model reads when the skill loads.
Wisp does not currently expose an endpoint registry or a service-lifecycle backend. The Agent cannot allocate ports, configure tunnels, read secrets, register health checks, or start and stop a persistent inference service through Python.
Do not model service startup as a normal run_in_context command: a Run tracks
one process lifecycle, while a managed endpoint also needs a durable endpoint
identity, health, routing, authentication, restart policy, and ownership.
If the user already operates an endpoint outside Wisp and the selected local,
WSL, or SSH context can reach it using credentials already configured in that
execution environment, load using-model-endpoint to run a bounded inference
client. Never request or print secret values merely to make the call.
Otherwise explain that endpoint registration and service management are not available in this Wisp build. A future implementation should add a typed service or execution-context backend with keyring-backed secret binding, health checks, start/stop/recovery semantics, and auditable invocation Runs.
test cases passed, vs 7 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.