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.
github.com/2233admin/unlazyIndexed at 1667149Up to date with upstream
35 files and 383 symbols in 2 modules, written in JavaScript.
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Code health
This codebase scores 5.5 out of 10 for code health, which we rate fair. It also scores maintainability 5.7 and static performance 8.8 out of 10. The three are scored separately and never blended into one number. Risk is concentrated, as it usually is: 4 of 35 files are git hotspots, and they average 3.3 — which is where the fixes pay off most.
1 thing worth doing in the week to Sep 3, the last indexed commit, 1 of them now.
Tidy 3 functions you changed in scripts/lib/gates.mjs this week
2 commits in the last 7 days added or worsened 5 critical or high findings here, still open. Fixing now costs less than after the next change.
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[](https://repowise.dev/repo/2233admin/unlazy?src=badge_card)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. This page is a map of the 2233admin/unlazy repository, rebuilt from the source each time it is indexed. Repowise parses every symbol, computes a dependency graph, scores per-file code health from complexity, duplication, test coverage and churn, mines git history for hotspots and ownership, and lifts the architectural decisions into documentation you can read here or query through MCP.
The codebase has 35 files and 383 symbols in 2 modules, led by JavaScript. Code health is 5.5 out of 10, rated fair.
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