simple-biology-explainer
Explains complex biological concepts, structures, and processes in simple, accessible language using analogies and examples, tailored for students and laypeople.
Explains complex biological concepts, structures, and processes in simple, accessible language using analogies and examples, tailored for students and laypeople.
Generates a list of randomly selected chemical elements, displaying their symbol, atomic number, and common usage.
Generates mnemonics for histology topics, ensuring all items are relevant histological structures or cellular components, excluding external factors or non-structural concepts.
Provides concise, single-sentence explanations or representations for various chemistry topics including stoichiometry, atomic structure, bonding, and periodic trends.
Generates detailed, scientifically plausible visual descriptions, hex color codes, and etymological name derivations for gas giants. Capable of describing standard chromochemical types or generating new classifications adhering to specific suffix conventions (-ic for chemistry, -ian/-ean for appearance).
Calculates the net.ipv4.tcp_mem kernel parameter values based on the number of simultaneous TCP connections and minimum buffer sizes, specifically accounting for kernel overhead doubling.
Translates verbal descriptions of mathematical relationships or operation sequences into standard algebraic equations or expressions without simplification.
Simulates theoretical wormhole scenarios adhering to Einstein's descriptions, conservation laws, and causality, preventing time travel paradoxes.
Gera simulados de questões de múltipla escolha com alto nível de complexidade sobre temas variados, apresentando as respostas agrupadas ao final.
Provides definitions, mathematical expressions, and simple examples for terms strictly within the context of Fluid Mechanics and Thermodynamics.
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.
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.
Generate clinical trial protocols for medical devices or drugs. This skill should be used when users say "Create a clinical trial protocol", "Generate protocol for [device/drug]", "Help me design a clinical study", "Research similar trials for [intervention]", or when developing FDA submission documentation for investigational products.
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.
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.
Analyze pipeline coverage and forecast accuracy
Force critical evaluation of proposals, requirements, or decisions by analyzing from multiple adversarial perspectives. Triggers on: accepting a proposal without pushback, 'sounds good', 'let's go with', design decisions with unstated tradeoffs, unchallenged assumptions, premature consensus. Invoke with /challenge-that.
Search the public web for up-to-date facts. Works out of the box with Jina (default, no API key needed); optionally Serper, Tavily, or Brave Search. Use when workbook context is insufficient and fresh external references are needed.
硅基同化官,专门识别人类低效环节并推进 Agent 接管、自动化与流程并轨
Check a PR for review comments, evaluate them, and address valid ones