メニュー
You MUST use this before any creative work (features, products, content, strategy, systems, or behavior changes). Start by classifying what we’re brainstorming, then run thorough one-question-at-a-time discovery, propose 2–3 approaches, and converge on a validated plan/spec.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
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.
Strategic scientific problem selection, project ideation, and troubleshooting based on the Fischbach & Walsh framework.
Deep learning for single-cell analysis using scvi-tools and scverse ecosystem. 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, (7) RNA velocity with veloVI, or (8) QC analysis of single-cell RNA-seq data. Triggers include scVI, scANVI, totalVI, QC, quality control, batch correction, integration, multi-modal.
A conversational framework for systematic scientific problem selection, project ideation, troubleshooting, and strategic decision making.
| name | brainstorming |
| description | You MUST use this before any creative work (features, products, content, strategy, systems, or behavior changes). Start by classifying what we’re brainstorming, then run thorough one-question-at-a-time discovery, propose 2–3 approaches, and converge on a validated plan/spec. |
| version | 2.1.0 |
| tags | ["brainstorming","discovery","requirements","design","planning","ideation"] |
Turn vague ideas into a validated plan or spec through natural, collaborative dialogue.
This is domain-generic: it can be used for product features, websites, backend systems, marketing campaigns, business ideas, content, workflows, or anything else.
Core rules:
Start every brainstorming session by asking a single multiple-choice question:
What are we brainstorming today?
Then ask one follow-up question that is specific to the chosen category.
Rule: until classification is answered, do not assume domain.
Ask one question per message. Prefer multiple choice when possible. Use this ladder in order; skip only if already answered.
Use MoSCoW:
Unknowns Register rule:
Summarize in 150–250 words:
Present 2–3 options with trade-offs and a recommendation:
Each option includes:
Then ask:
Once an option is chosen, produce the output in small sections (200–300 words) and ask after each:
Choose the output type based on the classification:
Always ask whether the user wants the final deliverable saved to a file. If yes, write a Markdown file and confirm the filename. Default filename: brainstorming-plan.md
