{"repo":"chrisvoncsefalvay/autostar","free":true,"listed":false,"github":"https://github.com/chrisvoncsefalvay/autostar","clone":"git clone https://github.com/chrisvoncsefalvay/autostar.git","description":"Autoresearch ALL THE THINGS. RLVR for the masses.","language":"Python","stars":39,"topics":["agent-skills","autoresearch","claude-code","claude-skill"],"license":"MIT","category":"mcp-servers","readme_excerpt":"a\\ (autostar) If you can measure it, you can improve it. Soft RLVR for the masses. a\\ turns any measurable goal into a structured optimisation loop. You define what \"good\" looks like. a\\ runs experiments, scores the results, learns from every attempt, and converges on the best version within your budget. No reward model to train. No environment to build. No GPU cluster to provision. Just a goal, an evaluator, and an agent that knows how to search. --- Fastest path If you just want to install the skill and try it once in Claude Code: Then invoke it in Claude Code: The skill handles onboarding, confirms the mission with you, and runs experiments within your approved budget. --- What this is Most artifacts live where quality is real but hard to verify fully. Code can be type-checked but not beauty-checked. Prose can be spell-checked but not tone-checked. Traditional RLVR (reinforcement learning from verifiable rewards) needs rewards you can compute with certainty: math proofs, unit tests, formal proofs. That covers a narrow slice of what people want to improve. a\\ uses verifiable-ish rewards instead. It combines hard signals (type checkers, linters, test suites) with soft ones (LLM judges, human gates). Each track has its own verifier. The system runs enough steps per lap to build statistical confidence. The result: an optimisation loop for anything you can split into measurable dimensions. Code quality, documentation, prompt engineering, writing style, API design, accessibility","default_branch":null,"files":null,"tree":[],"storefront":"/r/chrisvoncsefalvay","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/chrisvoncsefalvay/autostar/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}