What problem does it solve? When users correct an AI agent's implementation or design choices, that feedback is often lost or dumped into ever-growing instruction files. This Skill ensures each correction is classified and stored once in the smallest authoritative artifact, preventing duplicated, contradictory, or bloated guidance. ## Core Features & Use Cases - Feedback Classification: Routes each correction to the right destination—regression test, design document, instruction file, learning inbox, or no persistence at all. - Learning Inbox Management: Maintains scoped .github/learnings/<area>.md files with strict limits (ten topics, 8 KB) and a defined entry format. - Compaction and Garbage Collection: Promotes mature guidance into specifications, encodes behavior in tests, and removes obsolete or contradicted entries. - Use Case: A user rejects an error-handling pattern in the agent host. The Skill checks existing instructions, generalizes the principle, and records it once in the relevant design document instead of scattering copies across files. ## Quick Start Ask the agent to record the correction you just made using the feedback-learning workflow so it is stored in the right place.
