/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
$ npx -y skills add lingzhi227/agent-research-skills --skill math-reasoning --agent claude-codeHow it fires
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/math-reasoning
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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
SKILL.md
math-reasoning.SKILL.mdname: math-reasoning
description: 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.
argument-hint: [task-or-context]
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](../research-planning/)
- Downstream: [algorithm-design](../algorithm-design/), [paper-writing-section](../paper-writing-section/)
- See also: [symbolic-equation](../symbolic-equation/), [data-analysis](../data-analysis/)
Read more
name: math-reasoning description: 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. argument-hint: [task-or-context]
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](../research-planning/)
- Downstream: [algorithm-design](../algorithm-design/), [paper-writing-section](../paper-writing-section/)
- See also: [symbolic-equation](../symbolic-equation/), [data-analysis](../data-analysis/)
31 skills for Claude Code covering the full academic research paper lifecycle — from literature search to slide generation — plus GitHub repository analysis for research topics. Extracted from 17 GitHub repos studying LLM-agent-driven research automation.
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