Simulates natural clicking patterns using Gaussian distribution for realistic CPS variation. Configurable weapon filters and block-breaking protection keep the automation subtle and context-aware.
Instead of using simple random delays between fixed bounds, this clicker employs the Box-Muller transform to generate normally distributed timing intervals. This means most clicks cluster around your target CPS with natural variation, while extreme outliers occur rarely—exactly how human clicking patterns behave statistically.
Fine-grained control over which held items trigger the auto-clicker. You can independently enable or disable clicking for swords, axes, fists, sticks, or any other item type. This prevents unwanted clicking while holding blocks, food, or tools you don't want to spam.
An optional safeguard that disables auto-clicking when your crosshair is aimed at a block. This prevents accidentally breaking your own structures, beds, or important blocks during combat while still maintaining full clicking speed against entities.
Basic random number generators produce flat distributions where every delay between min and max is equally likely. This creates unnatural clicking patterns with too many extreme values and no central tendency—a clear signature of automation.
Flat distribution — robotic pattern
The Box-Muller transform converts uniform random numbers into a normal distribution. Most clicks fall near your target CPS, with natural-looking variation that tapers off symmetrically. This mirrors how real players click.
Bell curve — natural human pattern
Step 1: Generate two independent uniform random numbers U₁ and U₂ between 0 and 1.
Step 2: Apply the transform: Z = √(-2·ln(U₁)) · cos(2π·U₂)
Step 3: Scale Z to your CPS range using μ (mean) and σ (standard deviation).
The result is a normally distributed value where ~68% of clicks fall within ±1σ of your target CPS, and ~95% within ±2σ.
Human reaction time and clicking speed follow normal distributions due to the central limit theorem—many small independent factors (fatigue, focus, hand position) combine to create bell-curve patterns.
Flat random distributions are a red flag for anti-cheat systems. The Gaussian approach produces timing that's statistically indistinguishable from legitimate players who click consistently but with natural micro-variations.
→ Consistency: Your CPS stays reliably within your configured range without wild spikes.
→ Natural outliers: Occasionally you'll click slightly faster or slower—just like a real person having a good or tired moment.
→ Heuristic evasion: Statistical tests for automation look for uniform distributions. Gaussian timing passes these checks effortlessly.
The relationship between CPS and click delay is inverse:
delay(ms) = 1000 / CPS
For a CPS range of 10-14:
• Max delay (min CPS): 1000/10 = 100ms between clicks
• Min delay (max CPS): 1000/14 ≈ 71ms between clicks
The Gaussian distribution centers around 12 CPS (~83ms delay) with natural spread.
Legit / Closet: 8-12 CPS — Matches average player clicking speed, very safe.
Balanced: 12-16 CPS — Above average but still humanly achievable with practice.
Aggressive: 16-20 CPS — High click speed, use with caution as it approaches butterfly/drag-clicking territory.
Maximum: 20-25 CPS — Only plausible if you're known for specialized clicking techniques.