What problem does it solve?
Autoresearch reduces skill rot by automatically improving an existing skill through targeted research, generating multiple improved versions, and selecting the best option without manual trial-and-error.
Core Features & Use Cases
- Skill evolution via evidence: Reads the target skill’s purpose, data sources, output format, and dependencies, then searches for better approaches and common failure modes.
- Multi-variant improvement loop: Produces four distinct SKILL.md variations (better inputs, sharper output, more robust handling, and a fundamentally different rethinking).
- Rubric-based selection and safe updating: Scores variations with explicit criteria and only applies the winner if it beats the baseline on improvement.
- Operational integration: Logs progress to memory/logs and sends a structured notification via ./notify, including the scoring summary and PR details.
Quick Start
Run autoresearch with var set to the skill you want improved, for example: "Evolve the token-movers skill by running autoresearch with var=token-movers and returning the PR for the best variation."