Individual skill
cerebras-guide
Blazing inference on Cerebras wafer-scale hardware — ultra-fast open-model APIs for speed-critical workloads.
skills/cerebras-guide/SKILL.md
Source commit faf37c0859ebf627bfcb5225e8057d088691e0ae
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- Yes
- Observed behavior
- Not checked yet
- Last checked
- Nov 8, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 54
- Repository last updated
- Oct 10, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- ai-agents Repository topic
- ai-agents-automation Repository topic
- ai-skills Repository topic
- awesome Repository topic
- awesome-list Repository topic
- chatgpt Repository topic
- claude-skills Repository topic
- meta-ai Repository topic
- muse-ai Repository topic
- engineering Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 5,122 bytes
Read excerpt
## Overview
Cerebras builds wafer-scale AI chips — entire processors the size of a silicon
wafer — and offers inference APIs running open models on that hardware at
extreme speed. Like Groq, the proposition is hardware-level velocity: very high
tokens-per-second and low latency for supported models, delivered via a simple
API.
Cerebras competes in the same "speed specialist" category as Groq, with its own
hardware architecture and model set. The practical approach is to evaluate both
(and other speed-optimized providers) on your workload: supported models,
measured latency on your prompts, pricing, and rate limits. The winner is
workload-specific, not universal.