Use when the user wants to learn, be taught, be quizzed, or understand an idea across any domain — science, math, philosophy, code, law, politics, psychology, medicine, arts, or history — with active scaffolding.
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Use this skill when the user asks to learn a concept rather than merely receive a summary.
.keating/state/learner.json for prior coverage.question interactions for retrieval and reconstruction, not just exposition. Stop and wait after each interaction..keating/outputs/verifications/ has no checklist for the topic, run /verify <topic> first./plan <topic> or /map <topic> only when a persisted filesystem artifact is explicitly useful; do not use them merely to render a card./animate <topic> yourself when a persisted animation would help — do not ask the user to do it./verify <topic> yourself before teaching factual claims — do not ask the user to do it./bench [topic] and /evolve [topic] yourself when improvement is needed — do not ask the user to do it./feedback <up|down|confused> [topic] yourself after sessions to record outcomes.No reviews yet. Tell others what you thought.
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This skill helps with using the @ax-llm/ax TypeScript library for building LLM applications. Use when the user asks about ax(), ai(), f(), s(), agent(), flow(), AxGen, AxAgent, AxFlow, signatures, streaming, or mentions @ax-llm/ax.
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This skill helps an LLM generate correct AxAgent code using @ax-llm/ax. Use when the user asks about agent(), child agents, namespaced functions, discovery mode, shared fields, llmQuery(...), RLM code execution, recursionOptions, or agent runtime behavior. For tuning and eval with agent.optimize(...), use ax-agent-optimize.
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This skill helps an LLM generate correct AxAgent tuning and evaluation code using @ax-llm/ax. Use when the user asks about agent.optimize(...), judgeOptions, eval datasets, optimization targets, saved optimizedProgram artifacts, or recursive optimization guidance.
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This skill helps an LLM generate correct AI provider setup and configuration code using @ax-llm/ax. Use when the user asks about ai(), providers, models, presets, embeddings, extended thinking, context caching, or mentions OpenAI/Anthropic/Google/Azure/Groq/DeepSeek/Mistral/Cohere/Together/Ollama/HuggingFace/Reka/OpenRouter with @ax-llm/ax.
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This skill helps an LLM generate correct AxFlow workflow code using @ax-llm/ax. Use when the user asks about flow(), AxFlow, workflow orchestration, parallel execution, DAG workflows, conditional routing, map/reduce patterns, or multi-node AI pipelines.
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This skill helps an LLM generate correct AxGen code using @ax-llm/ax. Use when the user asks about ax(), AxGen, generators, forward(), streamingForward(), assertions, field processors, step hooks, self-tuning, or structured outputs.
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