multilingual-reasoning/SKILL.md
Apply this skill whenever the user writes in a non-English language, asks questions about regional/cultural knowledge tied to a specific country or language community, poses math or logic problems in any language, or needs to follow multi-step instructions given in a non-English language. Also use when the user explicitly asks the agent to respond in a specific language, when a task requires cross-lingual reasoning or comparison, or when the user is testing the agent's multilingual ability. This skill dramatically improves performance on multilingual instruction-following, regional knowledge, mathematical reasoning, and logic tasks in any language. Use it proactively — don't wait for the user to ask about "multilingual" explicitly.
npx skillsauth add ahoynodnarb/reasoning-based-skills multilingual-reasoningInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill helps the agent perform at its highest level across all languages — not just translation, but genuine reasoning, instruction-following, and knowledge retrieval in the target language.
Do not "think in English, translate out."
This is the single biggest source of degradation in multilingual tasks. When a user writes in
Arabic, solve the problem in Arabic. When they write in Japanese, reason in Japanese. Treat the
input language as the language of thought, not just the language of presentation.
The goal is native-quality reasoning in the user's language, not high-quality English reasoning with a translation wrapper.
When you receive input, immediately identify:
If the language is ambiguous (e.g. a math problem with only symbols), default to the language of any surrounding instructions.
See
references/language-calibration.mdfor script/dialect disambiguation guides.
Multilingual instruction-following degrades primarily because constraint-tracking weakens in non-English. Combat this explicitly:
Common failure modes to avoid:
For questions that touch on local knowledge (laws, customs, geography, history, institutions, cuisine, social norms), apply these principles:
See
references/regional-knowledge-anchors.mdfor region-specific knowledge flags.
Math and logic problems in non-English languages are harder not because the math changes, but because:
Protocol for math/logic problems:
Number system notes:
Before writing your final response:
Checklist:
Translated English is the hardest failure to self-detect. Signs include:
When in doubt, ask yourself: Would a native speaker of this language write this?
E.g. "Here is a French text. Summarize it in English."
Read these when needed — don't load all at once:
| File | When to read |
|------|-------------|
| references/language-calibration.md | Dialect/script disambiguation, register notes for 20+ languages |
| references/regional-knowledge-anchors.md | Region-specific knowledge flags: law, currency, institutions, cultural norms |
| references/math-language-conventions.md | Mathematical notation and narration conventions by language |
| references/logic-connectives.md | Cross-linguistic logical connective mappings and ambiguity flags |
development
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
testing
Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
development
Activate this skill for any problem requiring rigorous mathematical reasoning, formal logical deduction, or structured constraint solving. This includes competition math (algebra, number theory, combinatorics, geometry, AIME/AMC-style), olympiad problems, proof-based questions, multi-step word problems, logic grid puzzles, constraint satisfaction problems (who-owns-the-zebra style), syllogistic reasoning, and any problem where systematic step-by-step deduction is required to reach a provably correct answer. Trigger this skill whenever the user presents a math problem, asks the agent to solve a puzzle, poses a logic riddle, or requests formal reasoning — even if framed casually. When in doubt, use this skill. Precision and correctness matter more than speed.
development
Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.