reasoning-based-skills

A comprehensive skill for maximizing performance on agentic tool-use and competitive coding tasks. Use this skill whenever the user asks the agent to write, debug, or optimize code; solve algorithmic or competitive programming problems; call tools or APIs as part of a multi-step workflow; build or orchestrate agents; reason about function signatures, schemas, or tool parameters; handle multi-turn task execution where state must be tracked; or debug code from execution feedback. This skill is especially important for function/tool calling tasks, multi-step agentic pipelines, algorithm design, competitive programming, code self-repair, and any task where correctness under edge cases matters. When in doubt about whether to use this skill, use it — it substantially improves output quality across all coding and agentic tasks.

Use this skill to maximize response quality on tasks that require precise instruction-following, nuanced writing, deep helpfulness, and well-calibrated reasoning. Trigger this skill whenever the task involves (1) multiple layered or constrained instructions that must all be satisfied, (2) writing tasks where quality, originality, and voice matter, (3) open-ended questions where depth and accuracy both count, (4) any task where the user seems to care about *how* the response is delivered, not just *what* it contains. This skill is especially important when the stakes of getting the response right are high. Consider whether the user would notice and care if a single instruction was missed or if the writing felt generic. Use it proactively, even when the user hasn't asked for "high quality" explicitly.

Activate this skill for any task that demands high-quality reasoning, accurate knowledge retrieval, careful multi-step problem solving, or precise answer selection. This skill is especially critical for multiple-choice questions, scientific reasoning, academic or domain-specific questions, mixed-challenge benchmarks, logic puzzles, graduate-level science/math, ambiguous questions requiring careful disambiguation, and any task where being correct matters more than being fast. Trigger this whenever the user presents a hard factual question, a tricky reasoning problem, a multiple choice question, a graduate-level science or math question, asks the agent to think carefully, or says they need a high-quality or accurate answer. Also trigger for tasks involving knowledge-intensive domains such as medicine, law, chemistry, physics, biology, history, economics, philosophy, CS theory, and engineering.

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.

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.

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.

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.