Agent skill

approximation-ratio-calculator

Analyze and prove approximation ratios for optimization algorithms

a5c-ai1,642★ · 1 repos on radarProfile →
claude-codecodexcan modify filesMIT
Install
npx skills add a5c-ai/babysitter --skill approximation-ratio-calculator --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 1 KB
Bundled scripts: none
Allowed tools: -Bash-Read-Write-Edit-Glob-Grep
Path: library/specializations/domains/science/computer-science/skills/approximation-ratio-calculator/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 1,642
Language: JavaScript

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Approximation Ratio Calculator ## Purpose Provides expert guidance on analyzing approximation algorithms and proving approximation guarantees. ## Capabilities - LP relaxation analysis - Integrality gap computation - Randomized rounding analysis - Approximation factor derivation - PTAS/FPTAS feasibility assessment - Inapproximability results analysis ## Usage Guidelines 1. **Problem Setup**: Formalize the optimization problem 2. **LP Relaxation**: Construct and analyze LP relaxation 3. **Rounding Design**: Design rounding scheme 4. **Ratio Proof**: Prove approximation ratio 5. **Gap Analysis**: Analyze integrality gap ## Tools/Libraries - LP/ILP solvers - Symbolic computation - Proof assistants

What's inside
Steps it walks through
  1. Purpose
  2. Capabilities
  3. Usage Guidelines
  4. Tools/Libraries
More from babysitter
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About this skill
What does the approximation-ratio-calculator skill do?

Analyze and prove approximation ratios for optimization algorithms

How do I install it?

Run `npx skills add a5c-ai/babysitter --skill approximation-ratio-calculator --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.

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