What problem does it solve? After a coding session with an AI agent, it is hard to know why the agent struggled, made mistakes, or wasted tokens. This Skill reviews session logs and proposes concrete improvements to the agent's environment so future runs go better. ## Core Features & Use Cases - Session Analysis: Reads primary sources and session logs for a specified or current coding session to find friction points. - Categorized Improvement Candidates: Identifies issues across navigation, automated checks, coding standards, AGENTS.md bloat, tool economy, no-op instructions, and information access. - Prioritized Recommendations: Presents improvement candidates to the user ordered by severity. - Use Case: After an agent repeatedly failed to find the right config file during a long session, run a retrospective to add a navigation pointer to AGENTS.md and a lint check that would have caught the agent's error. ## Quick Start Ask the agent to run a retrospective on the current coding session and suggest improvements to its environment.