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AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
Passed 18 of 23 test cases with the skill, and 7 of 23 without it.
and 17 more in the full report
12 fixed6 passed either way4 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
We plan to build a multimodal document extraction service using Google's native Gemini models. Which skill handle in Phase 2 should be invoked specifically for working with Gemini APIs?
| Checked | Without skill | With skill |
|---|---|---|
| Invokes @gemini-api-dev for Gemini API development. | Missed | Passed |
The instructions your model reads when the skill loads.
Comprehensive AI/ML workflow for building LLM applications, implementing RAG systems, creating AI agents, and developing machine learning pipelines. This bundle orchestrates skills for production AI development.
Use this workflow when:
ai-product - AI product developmentai-engineer - AI engineeringai-agents-architect - Agent architecturellm-app-patterns - LLM patternsUse @ai-product to design AI-powered features
Use @ai-agents-architect to design multi-agent system
test cases passed, vs 7 / 23 without
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