scienceclaw-qa
Answer scientific questions across all disciplines with evidence-based responses and citations.
Skill by beita6969
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/beita6969-scienceclaw-qa
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Use cases: testing, research, legal
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/scienceclaw-qa
What's inside
- Skill name
scienceclaw-qa- Description
- 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).
- License
- MIT
- Instructions
- 207 lines in SKILL.md · view on GitHub
Files (2)
- SKILL.md
- discipline-taxonomy.md · references/
SKILL.md (first lines)
# ScienceCLAW QA Answer scientific questions with evidence-based responses, structured methodology, and proper citations across all disciplines. ## When to Use Use this skill when the user: - Asks factual questions about any scientific discipline (natural or social sciences) - Needs explanation of scientific concepts, theories, laws, or methods - Requests comparison of competing scientific hypotheses or models - Wants to understand experimental results or published findings - Needs multi-step scientific reasoning to arrive at an answer - Asks "how does X work" or "why does X happen" questions rooted in science - Requests definitions, classifications, or taxonomies of scientific terms - Needs help interpreting data, graphs, or scientific figures (without code execution) ## When NOT to Use Do not use this skill when: - The question is opinion-based or subjective (e.g., "Is physics better than biology?") - The user needs code execution for data analysis (use the code-execution skill) - The user needs a literature search or paper retrieval (use the scienceclaw-retrieval skill) - The question is about non-scientific topics (pop culture, personal advice, etc.) - The user wants summarization of a specific paper (use scienceclaw-summarization) - The user needs structured information extraction from text (use scienceclaw-ie) - The user needs formal proof construction or complex causal inference (use scienceclaw-reasoning) ## Methodology Follow this four-step process for every scientific QA task: ### Step 1: Identify Discipline and Scope Determine the primary discipline and any cross-disciplinary dimensions of the question. Consult the discipline taxonomy in `references/discipline-taxonomy.md` for classification guidance. - Map the question to one or more of the 17 supported disciplines - Identify the subdiscipline(s) most relevant to the question - Note if the question spans multiple disciplines (e.g., biophysics, neuroeconomics) - Determine the depth of answer expected (introductory, intermediate, expert) ### Step 2: Retrieve Evidence Gather supporting evidence before constructing the answer. - Use the **literature-search** skill to find relevant primary sources, review articles, and textbook references - Prioritize peer-reviewed sources and authoritative references - For rapidly evolving fields, check recency of evidence (prefer sources from the last 5 years unless historical context is needed) - For contested topics, retrieve evidence from multiple perspectives ### Step 3: Synthesize Answer Construct the answer by integrating evidence from multiple sources. - Begin with a direct, concise answer to the question - Provide supporting explanation with appropriate depth - Include relevant context (historical development, current consensus, open questions) - Use discipline-appropriate terminology with definitions for technical terms - Present numerical values with units and uncertainty where applicable - Acknowledge limitations, caveats, or areas of active debate ### Step 4: Cite Sources Provide citations for all factual claims. - Use inline citations with author-year format: (Author et al., Year) - Include a references section at the end of the answer - Prefer DOI links when available - Distinguish between primary research, review articles, and textbook references - For well-established facts, cite authoritative textbooks or review articles rather than original papers ## Discipline-Specific QA Patterns ### Natural Science Approach For physics, chemistry, biology, medicine, materials science, astronomy, earth science, mathematics, and computer science: - **Quantitative emphasis**: Include equations, numerical values, units, and error bounds - **Mechanistic explanation**: Describe underlying mechanisms and causal chains - **Experimental grounding**: Reference key experiments that established the finding - **Model-based reasoning**: Explain which theoretical models apply and their limitations - **Scale awareness**: Specify the spatial, temporal, or energy scales at which answers apply Example pattern for natural science questions: ``` 1. State the direct answer with key quantitative values 2. Explain the underlying mechanism or theory 3. Reference the foundational experiment(s) or derivation 4. Note boundary conditions, assumptions, and limitations 5. Cite sources (textbooks for established facts, papers for recent findings) ``` ### Social Science Approach For economics, sociology, psychology, political science, linguistics, history, law, philosophy, and education: - **Context sensitivity**: Specify cultural, temporal, and geographic scope of findings - **Effect sizes and significance**: Report statistical effect sizes, not just p-values - **Methodological transparency**: Note the study designs behind cited evidence (RCT, observational, meta-analysis) - **Theoretical pluralism**: Present competing theoretical frameworks where relevant - **Normative vs. positive distinction**: Clearly separate empirical findings from value judgments Example pattern for social science questions: ``` 1. State the current consensus or dominant finding 2. Describe the evidence base (study types, sample sizes, replication status) 3. Present alternative theoretical interpretations 4. Note methodological limitations and external validity concerns 5. Cite meta-analyses and systematic reviews where available ``` ## Citation Format Requirements ### Inline Citations Use author-year format within the text:
From beita6969/ScienceClaw, MIT.
More from beita6969/ScienceClaw
- 0.608score
scienceclaw-benchmark-for51
beita6969
Use when a task asks to predict a phonon property of inorganic crystals from their structure, for example the frequency of the last peak of the phonon density…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for44
beita6969
Use when a task gives a covariate table plus, for each of many simulated observational datasets, a binary treatment vector and an outcome vector, and asks for…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for43
beita6969
Use when a task gives noisy OCR text of historical newspapers, periodicals or books (several languages, with metadata and a set of labelled OCR/ground-truth…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for34
beita6969
Use when predicting a binary molecular property such as HIV replication inhibition (OGB ogbg-molhiv, MoleculeNet-style activity) from SMILES strings and…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for42
beita6969
Use when a task gives hourly ICU records (vitals, labs, demographics, one stay per patient) with sepsis labels and asks for causal hour-by-hour sepsis alarms…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for33
beita6969
Use when forecasting day-ahead hourly electricity load of individual buildings (residential and commercial smart-meter data, BuildingsBench-style) from the…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for32
beita6969
Use when segmenting the hippocampus into anterior and posterior parts in small 3-D T1-weighted brain MRI crops (Medical Segmentation Decathlon Task04) from a…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for35
beita6969
Use when forecasting monthly tourism, visitor or other seasonal business and service series (Monash Tourism Monthly, 24-month horizon, period 12) from each…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark
beita6969
Index of the 23 discipline skills (FoR30-FoR52) that mirror the disciplines of the companion ScienceClaw-Eval benchmark: find which discipline skill fits a…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for30
beita6969
Use when segmenting top-down field or UAV RGB images of crops (sugar beet, PhenoBench-style) into soil / crop / weed semantics plus crop-plant and crop-leaf…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for31
beita6969
Use when ranking or predicting the fitness effect of single amino-acid substitutions in a protein (deep mutational scanning, ProteinGym-style assays) from…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for46
beita6969
Use when a task gives Python programming problems (a function stub with docstring or a task description, an entry-point name, a few public assert tests) and…
SkillAFreshness: A★ 1KClaude Code +9
Signals
- Stars
- 1,019
- Forks
- 109
- Open issues
- 1
- Last commit (this folder)
- Mar 12, 2026 (7 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/beita6969/ScienceClaw
- Website
- scienceclaw.science
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from beita6969
- 0.608score
scienceclaw-benchmark-for33
beita6969
Use when forecasting day-ahead hourly electricity load of individual buildings (residential and commercial smart-meter data, BuildingsBench-style) from the…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for34
beita6969
Use when predicting a binary molecular property such as HIV replication inhibition (OGB ogbg-molhiv, MoleculeNet-style activity) from SMILES strings and…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for30
beita6969
Use when segmenting top-down field or UAV RGB images of crops (sugar beet, PhenoBench-style) into soil / crop / weed semantics plus crop-plant and crop-leaf…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for32
beita6969
Use when segmenting the hippocampus into anterior and posterior parts in small 3-D T1-weighted brain MRI crops (Medical Segmentation Decathlon Task04) from a…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark
beita6969
Index of the 23 discipline skills (FoR30-FoR52) that mirror the disciplines of the companion ScienceClaw-Eval benchmark: find which discipline skill fits a…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for35
beita6969
Use when forecasting monthly tourism, visitor or other seasonal business and service series (Monash Tourism Monthly, 24-month horizon, period 12) from each…
SkillAFreshness: A★ 1KClaude Code +9
Cite this page
scienceclaw-qa skill. Agent Mart. https://agentmart.dev/skills/beita6969-scienceclaw-qa (last updated 2026-10-24)