What problem does it solve?
Users often struggle to get consistent, high-quality outputs from coding agents due to unclear prompting, missing context, poor boundary setting, or ineffective correction patterns, leading to wasted tokens, rework, and failed tasks. This Skill eliminates guesswork by analyzing actual cross-session collaboration history to deliver evidence-based, personalized teaching on how to communicate more effectively with coding agents.
Core Features & Use Cases
- Cross-Session Collaboration Analysis: Reviews all available authorized coding agent interaction records across tasks, projects, and time periods to identify recurring communication patterns, not just isolated incidents.
- Evidence-Bound Teaching Reports: Generates structured, actionable reports with concrete replacement prompt patterns, correction habits, handoff structures, and durable guidance recommendations tailored to your specific workflow.
- Use Case: For regular coding agent users who frequently need to correct agent outputs, re-run failed tasks, or struggle to enforce project-specific rules across sessions, this Skill delivers personalized, history-backed improvements to reduce rework and boost agent output quality.
Quick Start
Ask the AI to use the feedback skill to review your cross-session coding agent collaboration history and deliver a personalized teaching report with actionable improvements for your prompts and workflows.