Automates end-to-end optimization loops for measurable metrics.
npx skills add https://github.com/Volinad/v-skills-public --skill autoresearch-volinad
Please help me install this Agent Skill. Skill: autoresearch Source: https://github.com/Volinad/v-skills-public/tree/main/autoresearch Command: npx skills add https://github.com/Volinad/v-skills-public --skill autoresearch-volinad
This skill automates end-to-end autonomous optimization loops for any measurable metric, enabling teams to systematically improve performance without manual trial-and-error.
Start an autoresearch session to optimize a measurable metric by proposing hypotheses, running experiments, and reviewing the generated report.
High-intent search queries and answers about installing and using this skill.
You can automate optimization loops by running hypothesize-experiment-analyze cycles that systematically improve a target metric. It supports parameter mode for numeric tuning and code mode for file edits with Git integration.
A structured optimization journal records the results of automated hypothesize-experiment-analyze cycles. It logs parameter and code mode experiments across domains like trading strategies and code performance, concluding with a generated final report for review.
Running autonomous optimization experiments overnight requires Python 3.10+, Git, a valid eval command, and a defined parameter space or program.md. These prerequisites enable long-running automated cycles for any measurable metric.
Yes, autonomous optimization works across domains including trading strategies, code performance, prompts, and configurations. It supports both numeric parameter tuning and code modification modes to improve any measurable metric.
The best way to tune parameters without manual trial-and-error is using parameter mode within an autonomous optimization loop. It automates the hypothesize-experiment-analyze cycle, systematically testing numeric values against your eval command to improve the target metric.
You should avoid automated optimization loops if your project lacks a valid eval command, a defined parameter space, or a program.md file. Without these prerequisites and a measurable metric, the autonomous hypothesize-experiment-analyze cycle cannot function properly.
JamesPrialCreate Go benchmarks with b.N loops, sub-benchmarks, and timing control.
ChuaHanChongCompile ML experiment results into a markdown report with comparison tables.
4lermanRun autonomous experiment loops to optimize code by a measurable metric.
yoonhwanAutomate iterative code optimization loops with structured JSON experiment results.
