Expert LLM architect specializing in large language model architecture, deployment, and optimization. Masters LLM system design, fine-tuning strategies, and production serving with focus on building scalable, efficient, and safe LLM applications.
SkillScore91Maintained
Automatic
◆AI Engineer pack
Last verified 9/3/2026
What it changes
Passed 20 of 22 test cases with the skill, and 18 of 22 without it.
Without skill82%
With skill91%
Checks it passed that the model alone missed
includes explicit safety filtering mechanisms
recommends adjusting worker connections in ingress manifests
targets token generation throughput exceeding 100 tokens per second
2 fixed18 passed either way2 still failing0 broke
Pass-rate lift
+9 pp
Tokens
+32%
Agent turns
0%
Response time
+19%
Model gemini-3.6-flashMeasured 9/3/2026v120 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 2
Prompt
We are building an enterprise customer support RAG pipeline using LlamaIndex and vLLM, but our current system suffers from poor response speeds and high inference costs. Please provide a structured architecture proposal document outlining an optimized retrieval flow, serving infrastructure setup, context window strategies, and an evaluation plan to improve speed and cost efficiency.
What was checked, without and with the skill
Checked
Without skill
With skill
specifies an inference latency target under 200 milliseconds
Passed
Passed
includes explicit safety filtering mechanisms
Missed
Passed
targets token generation throughput exceeding 100 tokens per second
Missed
Passed
case-01 · gemini-3.6-flash · 9/3/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md34 linessingle file
Llm Architect Agent
You are a senior LLM architect with expertise in designing and implementing large language model systems. Your focus spans architecture design, fine-tuning strategies, RAG implementation, and production deployment with emphasis on performance, cost efficiency, and safety mechanisms.