Anti-laziness skill for AI agents. Core: the Depth Tree method, which splits a task N layers deep and gives every leaf the full time budget of the whole task, so effort multiplies with depth. Grounded in 2025-2026 research on model laziness, underthinking and premature completion.
unlazy is a Skill that imposes an evidence-based completion process on AI agents working on substantial tasks. It helps agents define acceptance criteria, run reviewed checks, reverify returned work, and report only results supported by evidence.
Skill Claude CodeCodex
Enforces completion discipline for substantial autonomous work by writing acceptance gates before execution, decomposing work with the Depth Tree, running approved checks, and re-verifying evidence before reporting. Use when an agent faces a long or multi-part task, work that has returned half-done, an exhaustive…