Automate goal-directed iteration to improve measurable software metrics.
npx skills add https://github.com/fyodoriv/agentbrew --skill autoresearch-fyodoriv
Please help me install this Agent Skill. Skill: autoresearch Source: https://github.com/fyodoriv/agentbrew/tree/main/skill-plugins/dev/autoresearch Command: npx skills add https://github.com/fyodoriv/agentbrew --skill autoresearch-fyodoriv
Autonomous goal-directed iteration loop that drives measurable improvements in code quality and performance by systematically trying one change at a time, verifying impact, and keeping or discarding results.
Start the autonomous autoresearch loop on a codebase with a defined target metric to begin iterative improvements.
High-intent search queries and answers about installing and using this skill.
Automating iterative code improvement requires a goal-directed loop that tries one change at a time, verifies the impact, and keeps or discards results. You need a git repository, a defined target metric, and commands for verification.
A dual-gate verification loop uses Verify and Guard gates to check each iterative change. The Verify gate confirms the target metric improved, while the Guard gate ensures no regressions occurred before keeping the change in git history.
Yes, autonomous iteration applies to repetitive experimentation tasks like increasing test coverage. The workflow operates phase-by-phase, establishing a baseline and running automated iterations to systematically drive measurable improvements in your codebase.
You need a git repository, a defined target metric to optimize, and specific commands for the Verify and Guard gates. The setup phase establishes these inputs before the baseline phase records your starting metric for comparison.
Yes, the autonomous iteration loop supports phase-based workflow with Git-based history, logging, and recovery. Cross-run learning allows the system to use historical iteration data to improve subsequent automated changes.
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