A new open-source tool that streamlines interactions with multiple AI coding agents by capturing and normalizing session data.
Switching between different AI coding agents often requires re-explaining tasks and workflows, leading to inefficiencies and frustration. Existing solutions do not adequately address this issue, prompting the need for a better approach.
The tool hooks into various AI agents to collect session data including prompts and tool calls.
All captured data is formatted uniformly to facilitate easier analysis and pattern recognition.
The system distills the normalized data into repeatable workflows and common mistake patterns.
Before applying learned patterns, a human review step ensures accuracy and relevance.
The accumulated knowledge is made available for any future AI agents, enhancing their efficiency.
Plan, execute and review a cross-cutting refactor in small batches, with Claude Code making the edits and GitHub Copilot reviewing each PR.
Claude Code reads your coverage report, picks the riskiest untested modules and writes unit tests that pass, one small PR at a time.
Copilot and Claude review each PR for bugs, missing tests and risky changes before a human looks, so reviewers can focus on design.