Best use case
Science Communication — Making Research Accessible is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
## Overview
Teams using Science Communication — Making Research Accessible 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 — Making Research Accessible Compares
| Feature / Agent | Science Communication — Making Research Accessible | 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?
## Overview
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
tooluniverse-target-research
Gather comprehensive biological target intelligence from 9 parallel research paths covering protein info, structure, interactions, pathways, expression, variants, drug interactions, and literature. Features collision-aware searches, evidence grading (T1-T4), explicit Open Targets coverage, and mandatory completeness auditing. Use when users ask about drug targets, proteins, genes, or need target validation, druggability assessment, or comprehensive target profiling.
tooluniverse-drug-research
Generates comprehensive drug research reports with compound disambiguation, evidence grading, and mandatory completeness sections. Covers identity, chemistry, pharmacology, targets, clinical trials, safety, pharmacogenomics, and ADMET properties. Use when users ask about drugs, medications, therapeutics, or need drug profiling, safety assessment, or clinical development research.
tooluniverse-disease-research
Generate comprehensive disease research reports using 100+ ToolUniverse tools. Creates a detailed markdown report file and progressively updates it with findings from 10 research dimensions. All information includes source references. Use when users ask about diseases, syndromes, or need systematic disease analysis.
Clinical Research
## Overview
tooluniverse-literature-deep-research
Conduct comprehensive literature research with target disambiguation, evidence grading, and structured theme extraction. Creates a detailed report with mandatory completeness checklist, biological model synthesis, and testable hypotheses. For biological targets, resolves official IDs (Ensembl/UniProt), synonyms, naming collisions, and gathers expression/pathway context before literature search. Default deliverable is a report file; for single factoid questions, uses a fast verification mode and may include an inline answer. Use when users need thorough literature reviews, target profiles, or to verify specific claims from the literature.
research-lookup
Look up current research information using Perplexity Sonar Pro Search or Sonar Reasoning Pro models through OpenRouter. Automatically selects the best model based on query complexity. Search academic papers, recent studies, technical documentation, and general research information with citations.
research-grants
Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC. Agency-specific formatting, review criteria, budget preparation, broader impacts, significance statements, innovation narratives, and compliance with submission requirements.
Reproducibility Checklist — Open Science Best Practices
## Overview
Patent Drafting — Intellectual Property Protection for Research
## Overview
FAIR Data Principles — Findable, Accessible, Interoperable, Reusable
## Overview
hot3d
HOT3D (Hand-Object 3D Dataset) by Meta Facebook - multi-view egocentric hand and object 3D tracking for Aria/Quest smart glasses. State-of-the-art multi-view 3D hand pose, object pose, and hand-object interaction tracking. Supports visualization with 3D joint projections, meshes, and skeletal overlays on video frames.
handtracking
Real-time hand detection in egocentric videos using victordibia/handtracking. Outputs bounding boxes for hands, specifically trained on EgoHands dataset. Supports video input/output with labeled hand boxes. Lightweight and fast for egocentric view applications.