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chrisvoncsefalvay

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• 4 skills
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• 1 day ago last updated

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   Missouri · chrisvoncsefalvay bundle

   Use when the user has page annotations from the Missouri Chrome extension, or asks you to look at, highlight, mark up, or comment on elements in a web page. Provides guidance on using Playwright page.evaluate() to read annotations, create markers, focus elements, and interact with Missouri's page API.
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   Posttrainer · chrisvoncsefalvay bundle

   Hands-on fine-tuning workshop: guides beginners through training a real LLM on a real dataset using RunPod serverless GPUs for under $20. Use when users ask "how do I fine-tune a model", "I want to try post-training", "train a small model", "fine-tune on RunPod", "QLoRA tutorial", "hands-on fine-tuning", or want to actually train a model rather than just learn theory. Also triggers on "posttrainer", "post-trainer", "chrisvoncsefalvay/posttrainer", or "craft of post-training hands-on". Walks users through account setup, model/dataset selection, launching training, evaluating results and pushing to HuggingFace. For DOING, not reading. Companion to "The Craft of Post-Training" by Chris von Csefalvay (No Starch Press, 2026).
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   Autostar · chrisvoncsefalvay bundle

   Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles. Also use when the user wants to improve a system prompt, a data pipeline, a writing style, or any artifact where quality can be decomposed into measurable tracks. For inference optimisation tasks (model latency, throughput, quantization, GPU deployment), a* delegates the low-level tuning to AITune while maintaining quality tracking and learning.
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   Autostar Web · chrisvoncsefalvay bundle

   Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Web runtime package: uses memory in this order: connector-backed, project-pack, none. Never assumes subprocess access or unrestricted local files. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles.
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