Anti-laziness skill for AI agents. Core: the Depth Tree method, which splits a task N layers deep and gives every lea...
Copy the install, test the workflow, then decide if it earns a permanent slot.
Fresh repo activity plus visible builder pull. This is the kind of tool people test before it turns obvious.
Copy the install, test the workflow, then decide if it earns a permanent slot.
Not hard to test, not trivial to unwind. Worth trying if it closes a sharp gap.
GitHub health 71/100. no security policy. Fresh enough repo health and manageable issue load keep the risk controlled.
AI Agent
Claude Code
Model
Claude
Fastest way to find out if unlazy belongs in your setup.
Copy the install command, run a real test, and back it out cleanly if it slows you down.
# Visit: https://github.com/Leonxlnx/unlazyRun this first. You will know quickly if the workflow earns a permanent slot.
# No automated removal — visit https://github.com/Leonxlnx/unlazyNo messy cleanup loop. If it misses, remove it and keep moving.
Install Location
~/ └─ .claude/ ├─ commands/ ├─ agents/ │ └─ unlazy/ ← installs here └─ settings.json
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.
Source: GitHub repository
Source check: August 29, 2026
Upstream commit: August 29, 2026
Repository state: Not marked archived
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