What problem does it solve? Context degradation in long AI sessions is silent and gradual: models stop attending to early instructions, compaction summaries drop standing instructions, and nothing announces the failure. This Skill installs a trivially checkable per-turn signal (a canary) so the moment context rot, drift, or compaction occurs, it becomes visible instead of silently corrupting your work. ## Core Features & Use Cases - Canary contract installation: Sets up a byte-stable first line on every response containing the user's name, a turn counter, and an honest self-check status (ctx ok / aging / thin), each field probing a different failure mode. - Trip detection and calibration: Distinguishes a single missed canary (warning) from a confirmed trip (two consecutive misses, counter discontinuity, or agent self-declaration) to avoid false alarms. - Recovery protocol: On a confirmed trip, checkpoints durable state to a file, re-anchors against project instructions, recommends a fresh session, and re-installs the canary with a new generation counter. - Use Case: At the start of a long refactoring session, ask the agent to install a context canary. Eighty turns later, after an auto-compaction event, the turn counter resets — you immediately know the summary dropped state and can checkpoint before continuing. ## Quick Start Ask the agent to install a context canary using your name so every reply starts with a name, turn counter, and context status line.