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 skills add lalitnayyar/finally --skill cerebrasgit clone --depth 1 https://github.com/lalitnayyar/finallyWrote 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/lalitnayyar/finally/cerebras)<a href="https://agentmods.dev/skills/lalitnayyar/finally/cerebras"><img src="https://agentmods.dev/badge/skills/lalitnayyar/finally/cerebras/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lalitnayyar/finally/cerebras"><img src="https://agentmods.dev/badge/skills/lalitnayyar/finally/cerebras.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00029 | $0.00314 |
| Opus 5.5 | $0.00012 | $0.00126 |
| Sonnet 5.5 | $0.00006 | $0.00063 |
| Haiku 4.5 | $0.00003 | $0.00031 |
Grade A, and why
cerebras-inference 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 13d 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
Calling an LLM via Cerebras
These instructions allow you write code to call an LLM with Cerebras specified as the inference provider.
This method uses LiteLLM and OpenRouter.
Setup
The OPENROUTER_API_KEY must be set in the .env file and loaded in as an environment variable.
The uv project must include litellm and pydantic.
uv add litellm pydantic
Code snippets
Use code like these examples in order to use Cerebras.
Imports and constants
from litellm import completion
MODEL = "openrouter/openai/gpt-oss-120b"
EXTRA_BODY = {"provider": {"order": ["cerebras"]}}
Code to call via Cerebras for a text response
response = completion(model=MODEL, messages=messages, reasoning_effort="low", extra_body=EXTRA_BODY)
result = response.choices[0].message.content
Code to call via Cerebras for a Structured Outputs response
response = completion(model=MODEL, messages=messages, response_format=MyBaseModelSubclass, reasoning_effort="low", extra_body=EXTRA_BODY)
result = response.choices[0].message.content
result_as_object = MyBaseModelSubclass.model_validate_json(result)
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.
- 13d ago First seen · 43 lines · 29 tokens per session scan A 801005f724ec
cerebras-inference is a skill published in the GitHub repository lalitnayyar/finally (0 stars, last pushed 6mo ago), licensed MIT. It adds 29 tokens to every session and 314 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-03.
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