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claude-codescience

Babysitter probabilistic-analysis-toolkit

Analyze randomized algorithms with probability theory tools and concentration inequalities

install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/science/computer-science/skills/probabilistic-analysis-toolkit" ~/.claude/skills/a5c-ai-babysitter-probabilistic-analysis-toolkit && rm -rf "$T"
manifest: library/specializations/domains/science/computer-science/skills/probabilistic-analysis-toolkit/SKILL.md
tags
#probability-theory#concentration-inequalities#algorithm-analysis#randomized-algorithms#statistical-analysis
source content

Probabilistic Analysis Toolkit

Purpose

Provides expert guidance on analyzing randomized algorithms using probability theory and concentration inequalities.

Capabilities

  • Expected value calculations
  • Chernoff and Hoeffding bound applications
  • Markov and Chebyshev inequality analysis
  • Moment generating function analysis
  • Concentration inequality selection
  • Las Vegas and Monte Carlo analysis

Usage Guidelines

  1. Random Variable Identification: Define relevant random variables
  2. Expectation Computation: Calculate expected values
  3. Concentration Selection: Choose appropriate bounds
  4. Bound Application: Apply concentration inequalities
  5. Result Interpretation: Interpret probabilistic guarantees

Tools/Libraries

  • Symbolic probability
  • Statistical libraries
  • SymPy