Validate autoimprove.yaml configuration and dry-run gates and benchmarks.
npx skills add https://github.com/tokyo-megacorp/autoimprove --skill diagnose-tokyo-megacorp
Please help me install this Agent Skill. Skill: diagnose Source: https://github.com/tokyo-megacorp/autoimprove/tree/main/skills/diagnose Command: npx skills add https://github.com/tokyo-megacorp/autoimprove --skill diagnose-tokyo-megacorp
This Skill helps users verify the correctness of their autoimprove.yaml configuration, diagnose failures in gates and benchmark metrics, and troubleshoot extraction issues, preventing costly mistakes during automated optimization processes.
Use the diagnose skill to validate your autoimprove.yaml configuration and test your gate commands and metric extraction patterns before starting autoimprove experiments.
High-intent search queries and answers about installing and using this skill.
You can validate your autoimprove.yaml configuration by checking its structural integrity and required fields, ensuring reliable automated codebase improvements and preventing costly mistakes during optimization processes.
Missing benchmark metrics during automated codebase improvements usually stem from mismatches in regex or jq extraction patterns tested against live benchmark outputs, or from failing gate commands that prevent benchmarks from completing successfully.
The best way to debug failing gates and metric extraction patterns is to execute dry-test runs for gates and benchmarks, then probe regex or jq extraction patterns against live outputs to identify data parsing issues.
Yes, you can execute dry-test runs for gates and benchmarks to ensure commands complete successfully and produce expected outputs without starting a full automated optimization experiment.
Probing metric extraction patterns works by testing your regex or jq patterns against live benchmark outputs to identify data parsing issues and ensure automated codebase improvements capture the correct metrics reliably.
arbisoftDefine a regex-first hybrid parsing pipeline with LLM fallback for edge cases.
sencersoyluPlan and execute computational experiments for algorithm benchmarking and ablation studies.
japurcellIdentify and fix performance bottlenecks across frontend and backend workloads.
Xpotato1024Coordinate diff inspection, block extraction, and patch verification workflows.
