research-synthesis
research-synthesis transforms raw user research data such as interview transcripts, survey responses, usability test notes, and support tickets into structured themes, user segments, and actionable recommendations. Use this skill when consolidating qualitative feedback into patterns that inform product decisions, identify user needs, and prioritize development or design work.
git clone --depth 1 https://github.com/openyak/openyak /tmp/research-synthesis && cp -r /tmp/research-synthesis/backend/app/data/plugins/design/skills/research-synthesis ~/.claude/skills/research-synthesisSKILL.md
# /research-synthesis > If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md). Synthesize user research data into actionable insights. See the **user-research** skill for research methods, interview guides, and analysis frameworks. ## Usage ``` /research-synthesis $ARGUMENTS ``` ## What I Accept - Interview transcripts or notes - Survey results (CSV, pasted data) - Usability test recordings or notes - Support tickets or feedback - NPS/CSAT responses - App store reviews ## Output ```markdown ## Research Synthesis: [Study Name] **Method:** [Interviews / Survey / Usability Test] | **Participants:** [X] **Date:** [Date range] | **Researcher:** [Name] ### Executive Summary [3-4 sentence overview of key findings] ### Key Themes #### Theme 1: [Name] **Prevalence:** [X of Y participants] **Summary:** [What this theme is about] **Supporting Evidence:** - "[Quote]" — P[X] - "[Quote]" — P[X] **Implication:** [What this means for the product] #### Theme 2: [Name] [Same format] ### Insights → Opportunities | Insight | Opportunity | Impact | Effort | |---------|-------------|--------|--------| | [What we learned] | [What we could do] | High/Med/Low | High/Med/Low | ### User Segments Identified | Segment | Characteristics | Needs | Size | |---------|----------------|-------|------| | [Name] | [Description] | [Key needs] | [Rough %] | ### Recommendations 1. **[High priority]** — [Why, based on which findings] 2. **[Medium priority]** — [Why] 3. **[Lower priority]** — [Why] ### Questions for Further Research - [What we still don't know] ### Methodology Notes [How the research was conducted, any limitations or biases to note] ``` ## If Connectors Available If **~~user feedback** is connected: - Pull support tickets, feature requests, and NPS responses to supplement research data - Cross-reference themes with real user complaints and requests If **~~product analytics** is connected: - Validate qualitative findings with usage data and behavioral metrics - Quantify the impact of identified pain points If **~~knowledge base** is connected: - Search for prior research studies and findings to compare against - Publish the synthesis to your research repository ## Tips 1. **Include raw quotes** — Direct participant quotes make insights credible and memorable. 2. **Separate observations from interpretations** — "5 of 8 users clicked the wrong button" is an observation. "The button placement is confusing" is an interpretation. 3. **Quantify where possible** — "Most users" is vague. "7 of 10 users" is specific.
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.
This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
Set up your bio-research environment and explore available tools. Use when first getting oriented with the plugin, checking which literature, drug-discovery, or visualization MCP servers are connected, or surveying available analysis skills before starting a new project.
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