What problem does it solve? Validating whether an AI coding agent can actually plan, edit code, and verify its work in a real repository is hard to measure; this Skill runs a repeatable multi-stage integration test that executes a real agentic loop and scores the results. ## Core Features & Use Cases - End-to-End Agentic Loop: Executes an approved change prompt through the Codex CLI against the real repo root, producing real filesystem edits and git diffs. - Deterministic and LLM Evaluation: Combines rule-based checks (planning before edits, verification after edits, repo-root-only changes) with LLM evaluators for agentic loop success and change quality. - Evidence and Scoring Reports: Collects evidence.json, execution summaries, and logs, then writes scored reports and a human-readable summary per run. - Use Case: A platform team wants to benchmark Codex CLI behavior on their repository; they run this test with an auto-generated task, review the scored report, and detect regressions like missing verification steps or sandbox-blocked tool access. ## Quick Start Ask the agent to run the Codex Readiness integration test and choose whether to provide a custom task or auto-generate one.