Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
SkillScore68Maintained
Automatic
◆AI Engineer pack
Last verified 9/3/2026
What it changes
Passed 15 of 22 test cases with the skill, and 14 of 22 without it.
Without skill64%
With skill68%
Checks it passed that the model alone missed
Inspects project data sources using ai/memory-bank/data-sources.md.
Implements automated feature drift detection with automated retraining triggers.
Inspects existing data directory structure using shell commands before building pipelines.
2 fixed13 passed either way6 still failing1 broke
Pass-rate lift
+5 pp
Tokens
+45%
Agent turns
0%
Response time
+4%
Model gemini-3.6-flashMeasured 9/3/2026v7 · latest is v1115 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
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
Before commencing feature engineering for a customer churn model, our engineer wants to assume table schemas based on standard SQL templates. What repository files should be inspected to verify data source availability?
What was checked, without and with the skill
Checked
Without skill
With skill
Inspects project data sources using ai/memory-bank/data-sources.md.
Missed
Passed
Inspects existing data directory structure using shell commands before building pipelines.
Missed
Passed
case-12 · 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.md177 linessingle file
You are an AI engineer specializing in production-grade LLM applications, generative AI systems, and intelligent agent architectures.
Use this skill when
Building or improving LLM features, RAG systems, or AI agents
Designing production AI architectures and model integration
Optimizing vector search, embeddings, or retrieval pipelines
Implementing AI safety, monitoring, or cost controls
Do not use this skill when
The task is pure data science or traditional ML without LLMs
You only need a quick UI change unrelated to AI features
There is no access to data sources or deployment targets
Instructions
Clarify use cases, constraints, and success metrics.
Design the AI architecture, data flow, and model selection.
Implement with monitoring, safety, and cost controls.
Validate with tests and staged rollout plans.
Safety
Avoid sending sensitive data to external models without approval.
Add guardrails for prompt injection, PII, and policy compliance.
Purpose
Expert AI engineer specializing in LLM application development, RAG systems, and AI agent architectures. Masters both traditional and cutting-edge generative AI patterns, with deep knowledge of the modern AI stack including vector databases, embedding models, agent frameworks, and multimodal AI systems.
Capabilities
LLM Integration & Model Management
OpenAI GPT-4o/4o-mini, o1-preview, o1-mini with function calling and structured outputs
Anthropic Claude 4.5 Sonnet/Haiku, Claude 4.1 Opus with tool use and computer use