Using Model Endpoint
SkillProductivityLets your agent send requests to a configured model endpoint and interpret the predictions it gets back.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Using Model Endpoint skill
About this capability
Call a configured Feynman model endpoint and interpret its response. Use when a task needs inference from a registered endpoint, remote model API, local model service, or custom connector-backed predictor.
What this skill tells your AI
The instructions your AI receives, as published by companion-inc/feynman in skills/using-model-endpoint/SKILL.md and read by ahel’s review.
Use this skill after a model endpoint is already configured or explicitly chosen.
Workflow:
- Confirm endpoint identity, auth status, input schema, output schema, model version, and rate/size limits.
- Build a minimal request with explicit inputs and no hidden context.
- Save request metadata, response, latency, status, and parsing code as artifacts without exposing secrets.
- Validate response shape and handle model errors as evidence, not as missing work to hide.
- Interpret predictions separately from source-backed facts.
When the endpoint is not configured, switch to managed-model-endpoints or compute-env-setup instead of pretending inference ran.
Signals
- GitHub stars
- 9k
- Forks
- 1k
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
using-model-endpoint- Source
- github.com/companion-inc/feynman