What problem does it solve? After a coding session, it is hard to know why an AI agent struggled, made mistakes, or wasted tokens. This Skill analyzes session logs and turns those friction points into concrete improvements for the agent's environment, such as better navigation pointers, automated checks, and coding standards. ## Core Features & Use Cases - Session Log Analysis: Reads primary sources from a specified or current coding session to find improvement candidates. - Categorized Findings: Evaluates navigation, automated checks, coding standards, AGENTS.md bloat, tool economy, no-op instructions, and information access, then ranks findings by severity. - Guardrail-First Recommendations: Classifies violations as mechanical (fixed with deterministic checks like linters, pre-commit hooks, or CI jobs) versus judgement calls (documented in CODING_STANDARDS.md). - Use Case: After an agent repeatedly failed to find a config file and shipped un-linted code, run a retrospective to add a navigation pointer to AGENTS.md and wire the repo's existing lint script into CI. ## Quick Start Ask the agent to run a retrospective on the current coding session and suggest environment improvements.