pydantic-evals
Test and evaluate AI agents and LLM outputs using code-first evaluation framework with strong typing. Use when the user wants to: (1) Create evaluation datasets with test cases for AI agents, (2) Define evaluators (deterministic, LLM-as-Judge, custom, or span-based), (3) Run evaluations and generate reports, (4) Compare model performance across experiments, (5) Integrate evaluations with Pydantic AI agents, (6) Set up observability with Logfire, (7) Generate test datasets using LLMs, (8) Implement regression testing for AI systems.
graflow-workflow
Create Python workflow pipelines using Graflow with a structured plan-implement-review process. Use when building task graphs, parallel pipelines, LLM workflows, or any Graflow-based automation. Triggers on requests for "workflow", "pipeline", "task graph", "Graflow", or when user wants to build an automated data/AI pipeline.
magpie
Performs GPU kernel correctness and performance evaluation and LLM inference benchmarking with Magpie. Analyzes single or multiple kernels (HIP/CUDA/PyTorch), compares kernel implementations, runs vLLM/SGLang benchmarks with profiling and TraceLens, and runs gap analysis on torch traces. Creates kernel config YAMLs, discovers kernels in a project, and queries GPU specs. Use when the user mentions Magpie, kernel analyze or compare, HIP/CUDA kernel evaluation, vLLM/SGLang benchmark, gap analysis, TraceLens, creating kernel configs, or discovering GPU kernels.
deep-plan
Creates detailed, sectionized, TDD-oriented implementation plans through research, stakeholder interviews, and multi-LLM review. Use when planning features that need thorough pre-implementation analysis.
finding-duplicate-functions
Use when auditing a codebase for semantic duplication - functions that do the same thing but have different names or implementations. Especially useful for LLM-generated codebases where new functions are often created rather than reusing existing ones.
eino
Eino LLM/AI application development framework assistant (Golang). Use when the user needs to: (1) Build AI agents, (2) Create LLM applications, (3) Implement tool calling, (4) Build multi-agent systems, (5) Create workflows with Graph/Compose, (6) Implement streaming, (7) Human-in-the-loop patterns, or any other Eino framework development tasks. Triggers on phrases like "Eino å¼å", "å建 Agent", "LLM åºç¨", "AI Agent", "Eino æ¡æ¶", "æ建æºè½ä½".
allium
An LLM-native language for sharpening intent alongside implementation. Velocity through clarity.
ai-ml-development
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
ai-seo-optimization
Optimize content and websites for AI search engines (ChatGPT, Perplexity, Google AI Overviews) using GEO principles, content chunking, structured data, and brand visibility strategies. Use when working on SEO, AI visibility, content optimization, GEO, getting brand mentioned in AI, or implementing technical SEO for LLM search.