bychrisvoncsefalvay· 2 skills
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.
$npx -y skills add chrisvoncsefalvay/autostar --skill autostar-skillInstalls into the current project.
Run `npx skills use "https://github.com/chrisvoncsefalvay/autostar" --skill "chrisvoncsefalvay/autostar/autostar-skill"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/chrisvoncsefalvay/autostar" that are relevant to the current task. Run `npx skills add "https://github.com/chrisvoncsefalvay/autostar"` and select the relevant skills, then follow their instructions.