self-improvement-ci

Automated CI self-improvement captures recurring failure patterns by analyzing pull request check results in CI environments. It deduplicates recurring issues using a stable pattern_key and emits structured learning suggestions for agent context and system prompts.

Aggregating quality signals from CI pipelines without manual intervention requires a CI-only self-improvement approach. This method runs in headless environments, identifies recurring issues, and proposes durable prevention rules to be added to system prompts.

Yes, analyzing failed pull request checks and capturing recurring failure patterns requires GitHub CLI authentication. You also need the gh-aw extension for authoring and validation of the structured learning candidates.

Pattern aggregation for recurring CI failures works by identifying recurring issues and deduplicating them using a stable pattern_key. This process emits structured learning candidates that can be used to improve agent context and system prompts continuously.

Yes, you can run continuous learning capture in headless CI environments. The CI-only self-improvement mechanism operates without interactive chat loops, making it ideal for capturing recurring failure patterns automatically during pull request checks.

Automated learning capture in CI is limited by its dependency on GitHub CLI authentication and the gh-aw extension for authoring and validation. It is designed for headless environments and does not support interactive chat loops for real-time learning.