probabilistic-analysis-toolkit
Analyze randomized algorithms with probability theory tools and concentration inequalities
Profile →npx skills add a5c-ai/babysitter --skill probabilistic-analysis-toolkit --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Purpose
- Capabilities
- Usage Guidelines
- Tools/Libraries
What does the probabilistic-analysis-toolkit skill do?
Analyze randomized algorithms with probability theory tools and concentration inequalities
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill probabilistic-analysis-toolkit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.