Initialize autoresearch loops for prepared benchmark projects.
npx skills add https://github.com/Elijah-J/InfoScraper --skill ar-start
Please help me install this Agent Skill. Skill: ar-start Source: https://github.com/Elijah-J/InfoScraper/tree/main/.agents/skills/ar-start Command: npx skills add https://github.com/Elijah-J/InfoScraper --skill ar-start
Autoresearch workflows for new benchmark projects require coordinated setup, branch management, and initial evaluations. ar-start automates starting the loop from a prepared scaffold, reducing manual setup time and ensuring repeatable baselines.
Start the autoresearch workflow for a prepared project directory by running ar-start <project-dir> to initialize and launch the loop.
High-intent search queries and answers about installing and using this skill.
To bootstrap an autoresearch loop, run ar-start on your prepared project directory to switch to the autoresearch/<slug> branch, execute bootstrap, and generate baseline evaluation results.
Starting an autoresearch workflow requires locating autoresearch.toml, program.md, and eval.py in your prepared project directory to ensure proper workflow orchestration and initial evaluation execution.
Baseline generation in benchmark optimization creates or updates a baseline results row, executes the first evaluation, and reports baseline scores alongside guardrail status to establish repeatable performance metrics.
Yes, automated workflow orchestration manages git branches by switching to the expected autoresearch/<slug> branch before running bootstrap and executing initial evaluations within a dedicated workspace.
The best way to automate initial evaluation setup is using a bootstrap-and-run workflow that enforces guardrails, executes the first eval, and reports baseline scores to ensure repeatable results.
Autoresearch workflows require guardrails during baseline generation to enforce workflow requirements, verify guardrail findings, and update project status accurately after executing the initial evaluation.
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