What problem does it solve? Long-running or unattended AI work leaves reviewers unable to reconstruct what was decided, why, and on what evidence without rereading entire transcripts. This Skill keeps a single canonical decision trail so a human can audit the run after the fact. ## Core Features & Use Cases - Structured TSV decision log: One row per decision with timestamp, phase, decision, rationale, evidence pointer, and result, rendered as a sortable table on GitHub or in a terminal. - Safe logging helper script: scripts/log.sh appends well-formed rows, writes the header on first use, strips stray tabs and newlines, and neutralizes spreadsheet formula injection from untrusted cell content. - End-of-run audit and cross-model review: Verifies every log row against the actual transcript and spawns a subagent on a different model family to flag weak evidence, skipped verification, or risky choices. - Use Case: During a multi-week migration, log each fork, revert, and verification result to decisions.tsv, then commit it with the PR so reviewers can follow the trail and trust the outcome. ## Quick Start Ask the agent to start a show-me-your-work decision log for this task and record each major decision with its evidence as the work proceeds.