Feynman is an open-source AI research agent that helps users investigate topics with language models. It supports local model providers and hosted model authentication through its setup process. The catalogue contains skills, agents, and instructions that extend Feynman’s workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/companion-inc/feynman/using-model-endpointnpx skills add companion-inc/feynman --skill using-model-endpointgit clone --depth 1 https://github.com/companion-inc/feynmanWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/companion-inc/feynman/using-model-endpoint)<a href="https://agentmods.dev/skills/companion-inc/feynman/using-model-endpoint"><img src="https://agentmods.dev/badge/skills/companion-inc/feynman/using-model-endpoint.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.00186 |
| Opus 5 | $0.00022 | $0.00093 |
| Sonnet 5 | $0.00009 | $0.00037 |
| Haiku 4.5 | $0.00004 | $0.00019 |
Grade A, and why
using-model-endpoint scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Using Model Endpoint
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 19 lines · 43 tokens per session scan A e8fccc1e7aa4
using-model-endpoint is a skill published in the GitHub repository companion-inc/feynman (8,857 stars, last pushed 10d ago), licensed MIT. It adds 43 tokens to every session and 186 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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