probabilistic-analysis-toolkit

Analyze randomized algorithms with probability theory tools and concentration inequalities

509 stars

Best use case

probabilistic-analysis-toolkit is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Analyze randomized algorithms with probability theory tools and concentration inequalities

Teams using probabilistic-analysis-toolkit should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/probabilistic-analysis-toolkit/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/computer-science/skills/probabilistic-analysis-toolkit/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/probabilistic-analysis-toolkit/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How probabilistic-analysis-toolkit Compares

Feature / Agentprobabilistic-analysis-toolkitStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Analyze randomized algorithms with probability theory tools and concentration inequalities

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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

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