math-reasoning
Formal mathematical reasoning for research papers — derive equations, write proofs, formalize problem settings, select statistical tests, and generate LaTeX math notation. Use when the user needs mathematical derivations, theorem proofs, notation tables, or statistical analysis formalization.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/lingzhi227/agent-research-skills --skill math-reasoning- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads math-reasoning automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Mathematical Reasoning
Perform rigorous mathematical reasoning and produce publication-quality LaTeX output.
Input
$0— Task type:derive,prove,formalize,stats,notation,verify$1— Context: equation, theorem statement, problem description, or data description
Tasks
derive — Step-by-step equation derivation
Show every intermediate step. Justify each with the rule applied. Box final result with \boxed{}. Number important equations with \label{eq:name}.
prove — Formal theorem proof
Use appropriate technique: direct, contradiction, induction, construction, or cases. See references/proof-templates.md for LaTeX templates.
formalize — Problem setting formalization
Convert informal description into formal mathematical framework with: variable definitions, domain/range specifications, assumptions, objective function.
stats — Statistical test selection
Use the decision tree in references/notation-guide.md to select appropriate tests. Report p-values, effect sizes, confidence intervals.
notation — Generate notation table
Create a \begin{table} with all symbols used in the paper. Use standard ML notation from references/notation-guide.md.
verify — Check mathematical correctness
Verify: dimensional consistency, boundary cases, gradient computations, notation consistency across sections.
References
- Standard ML notation + statistical tests:
~/.claude/skills/math-reasoning/references/notation-guide.md - Proof templates and theorem environments:
~/.claude/skills/math-reasoning/references/proof-templates.md
Rules
- Define ALL symbols before first use: "Let $\mathcal{X}$ denote..."
- Use consistent notation throughout the paper
- Number equations that are referenced later
- Use
\tag{reason}for key derivation steps - State assumptions explicitly
- Cite lemmas and prior results used in proofs
Related Skills
- Upstream: research-planning
- Downstream: algorithm-design, paper-writing-section
- See also: symbolic-equation, data-analysis
FAQ
What does the math-reasoning skill do?
Formal mathematical reasoning for research papers — derive equations, write proofs, formalize problem settings, select statistical tests, and generate LaTeX math notation. Use when the user needs mathematical derivations, theorem proofs, notation tables, or statistical analysis formalization.
How do I install math-reasoning?
Run npx skills add https://github.com/lingzhi227/agent-research-skills --skill math-reasoning in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/math-reasoning/ (personal) or .claude/skills/math-reasoning/ (project).
Which AI agents can use math-reasoning?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is math-reasoning safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains math-reasoning?
It is published by lingzhi227 in lingzhi227/agent-research-skills. It has 2,167 recorded installs.
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