Best use case
science-communication is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Teams using science-communication should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/science-communication/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How science-communication Compares
| Feature / Agent | science-communication | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
This skill provides specific capabilities for your AI agent. See the About section for full details.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Science Communication — Making Research Accessible ## Overview Translate complex scientific findings into engaging content for non-specialist audiences. ## Output Types ### Press Release - **Headline**: Active voice, no jargon, emphasize significance - **Lead paragraph**: Who, what, when, where, why — in plain language - **Quote**: From the lead researcher, expressing significance - **Background**: 1-2 paragraphs of accessible context - **Implications**: What this means for patients/society/technology - **Contact info**: PI, institution press office ### Plain-Language Summary - Write at 8th-grade reading level - Replace jargon: "gene expression" → "how active a gene is" - Use analogies: "DNA methylation is like a dimmer switch for genes" - One key finding per paragraph - End with "Why it matters" ### Social Media (Twitter/X Thread) - Hook tweet: surprising finding in <280 chars - 3-5 thread tweets explaining the story - Include: figure, emoji for visual breaks, relevant hashtags - Tag relevant accounts (journal, institution, collaborators) ### Blog Post / Explainer - Catchy title (question or surprising fact) - Opening hook: a story, analogy, or question - Background: what was known before - The breakthrough: what's new - Implications: what happens next - Further reading: links to paper and resources ## Principles - **Accuracy**: Never oversimplify to the point of being wrong - **Engagement**: Tell a story, not a lecture - **Honesty**: Include limitations and caveats - **Accessibility**: No acronyms without expansion, no undefined jargon
Related Skills
social-science-research
Orchestrates a social science research workflow from literature review through data collection, text analysis, statistical modeling, and report generation. Use when conducting empirical social science research, policy analysis, or mixed-methods studies. NOT for pure natural science analysis or clinical trial data.
social-science-analysis
Social science research methods including survey design, qualitative analysis, content analysis, network analysis, psychometrics, and mixed methods. Covers sociology, psychology, political science, education, and communication studies. Use when user designs surveys, analyzes qualitative data, does content analysis, builds scales, or uses mixed methods. Triggers on "survey design", "qualitative analysis", "content analysis", "Likert scale", "thematic analysis", "grounded theory", "factor analysis", "SEM", "structural equation", "psychometrics", "interview coding".
scienceclaw-verification
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).
scienceclaw-summarization
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).
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).
scienceclaw-reasoning
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).
scienceclaw-qa
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).
scienceclaw-prediction
Predict scientific properties, trends, and outcomes. Use when: user asks for property prediction, trend forecasting, or model-based estimation. NOT for: historical data lookup or real-time monitoring.
scienceclaw-ie
Extract structured information from scientific texts: entities, relations, data tables, methods, results. Use when: (1) parsing papers for key data, (2) extracting experimental parameters, (3) building knowledge graphs from literature, (4) NER on scientific documents, (5) extracting methods/results sections. NOT for: summarization (use scienceclaw-summarization), full text retrieval (use scienceclaw-retrieval).
scienceclaw-generation
Generate scientific hypotheses, experimental designs, and paper drafts. Use when: user asks to propose hypotheses, design experiments, or write scientific content. NOT for: data analysis or literature search.
scienceclaw-discovery
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.
scienceclaw-classification
Classify scientific content by discipline, methodology, topic, and quality. Use when: user asks to categorize papers, methods, or research outputs. NOT for: simple keyword tagging or non-scientific content.