Best use case
floating-point-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Rigorous floating-point error analysis
Teams using floating-point-analysis 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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/floating-point-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How floating-point-analysis Compares
| Feature / Agent | floating-point-analysis | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Rigorous floating-point error analysis
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
# Floating-Point Analysis ## Purpose Provides rigorous floating-point error analysis capabilities for numerical algorithm verification and accuracy assessment. ## Capabilities - IEEE 754 arithmetic modeling - Roundoff error accumulation tracking - Interval arithmetic computation - Arbitrary precision arithmetic - Numerical condition number computation - Error bound derivation ## Usage Guidelines 1. **Error Modeling**: Model floating-point operations precisely 2. **Interval Arithmetic**: Use interval bounds for guaranteed accuracy 3. **High Precision**: Employ arbitrary precision for validation 4. **Error Bounds**: Derive forward and backward error bounds ## Tools/Libraries - MPFR - Arb - Herbie - FPBench
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