Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Identify research gaps, synthesize cross-disciplinary insights, and generate novel hypotheses. Use when: user asks about unexplored areas, cross-field connections, or new research directions. NOT for: routine literature review or data analysis.
Answer scientific questions across all disciplines with evidence-based responses and citations. Use when: (1) user asks factual science questions, (2) needs explanation of concepts/theories/methods, (3) multi-step scientific reasoning needed. Covers natural sciences (physics, chemistry, biology, medicine, materials, astronomy, earth science, math, CS) and social sciences (economics, sociology, psychology, political science, linguistics, history, law, philosophy, education). NOT for: opinion-based questions, non-scientific queries, or when code execution is needed (use code-execution skill).
Perform multi-step scientific reasoning, proof construction, causal inference, and logical argumentation. Use when: (1) deriving conclusions from premises, (2) causal analysis, (3) mathematical proofs, (4) hypothesis evaluation, (5) counterfactual reasoning. NOT for: simple factual questions (use scienceclaw-qa), data analysis (use code-execution), or literature search (use scienceclaw-retrieval).
Retrieve scientific information from databases, literature, and knowledge bases. Use when: (1) finding relevant papers, (2) querying scientific databases, (3) cross-referencing findings, (4) building bibliographies, (5) systematic literature search. NOT for: answering questions (use scienceclaw-qa), summarizing (use scienceclaw-summarization), or data analysis (use code-execution skill).
Summarize scientific papers, datasets, experimental results, and literature reviews. Use when: (1) condensing research papers, (2) creating literature reviews, (3) summarizing experimental findings, (4) meta-analysis synthesis, (5) creating executive summaries of research. NOT for: information extraction (use scienceclaw-ie), full paper retrieval (use scienceclaw-retrieval), or writing new content (use scienceclaw-generation).
Verify scientific claims, check calculations, validate experimental designs, and fact-check citations. Use when: (1) checking a claim against evidence, (2) validating statistical analyses, (3) verifying experimental reproducibility claims, (4) fact-checking references, (5) adversarial review of research. NOT for: generating new content (use scienceclaw-generation), simple QA (use scienceclaw-qa).
Classify scientific objects, detect patterns, and categorize data across astronomy, biology, and social sciences
Summarize and simplify scientific literature, educational content, and research papers
Scientific computing and statistical analysis with SciPy, NumPy, and pandas. Use when: (1) statistical hypothesis testing, (2) optimization problems, (3) signal processing, (4) numerical integration, (5) data manipulation and analysis. NOT for: symbolic math (use sympy-math), machine learning (use sklearn directly), or visualization (use matplotlib-viz).
Search Semantic Scholar for academic papers across all disciplines with citation graph analysis. Use when: (1) finding papers across disciplines, (2) citation/reference graph traversal, (3) author search, (4) paper recommendations, (5) large-scale bibliometric analysis. NOT for: specific database access (use pubmed for biomedical, arxiv for preprints).