scientific-computing
3
research-patterns
Research methodology and best practices for finding existing patterns
akaszubski
research
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
scientific-computing
3
track-generation
This skill generates normalized BigWig (.bw) tracks (and/or fold-change tracks) from BAM files for ATAC-seq and ChIP-seq visualization. It handles normalization (RPM or fold-change) and Tn5 offset correction automatically. Use this skill when you have filtered and generated the clean BAM file (e.g. `*.filtered.bam`).
BIsnake2001
research
scientific-computing
3
atacseq-qc
Performs ATAC-specific biological validation. It calculates metrics unique to chromatin accessibility assays, such as TSS enrichment scores and fragment size distributions (nucleosome banding patterns). Use this skill when you have filtered BAM file and have called peak for the file. Do NOT use this skill for ChIP-seq data or general alignment statistics.
BIsnake2001
research
scientific-computing
3
nested-tad-detection
This skill detects hierarchical (nested) TAD structures from Hi-C contact maps (in .cool or mcool format) using OnTAD, starting from multi-resolution .mcool files. It extracts a user-specified chromosome and resolution, converts the data to a dense matrix, runs OnTAD, and organizes TAD calls and logs for downstream 3D genome analysis.
BIsnake2001
research
scientific-computing
3
known-motif-enrichment
This skill should be used when users need to perform known motif enrichment analysis on ChIP-seq, ATAC-seq, or other genomic peak files using HOMER (Hypergeometric Optimization of Motif EnRichment). It identifies enrichment of known transcription factor binding motifs from established databases in genomic regions.
BIsnake2001
research
scientific-computing
3
hic-normalization
Automatically detect and normalize Hi-C data. Only .cool or .mcool file is supported. All .mcool files are then checked for existing normalization (supports bins/weight only) and balanced if none of the normalizations exist.
BIsnake2001
research
scientific-computing
3
peak-calling
Perform peak calling for ChIP-seq or ATAC-seq data using MACS2, with intelligent parameter detection from user feedback. Use it when you want to call peaks for ChIP-seq data or ATAC-seq data.
BIsnake2001
research
scientific-computing
3
loop-annotation
This skill annotates chromatin loops, including enhancer/promoter assignments, CTCF-peak overlap. It automatically constructs enhancer and promoter sets when missing and outputs standardized loop categories.
BIsnake2001
research
scientific-computing
3
genomic-feature-annotation
This skill is used to perform genomic feature annotation and visualization for any file containing genomic region information using Homer (Hypergeometric Optimization of Motif EnRichment). It annotates regions such as promoters, exons, introns, intergenic regions, and TSS proximity, and generates visual summaries of feature distributions.
BIsnake2001
research
scientific-computing
3
differential-methylation
This skill performs differential DNA methylation analysis (DMRs and DMCs) between experimental conditions using WGBS methylation tracks (BED/BedGraph). It standardizes input files into per-sample four-column Metilene tables, constructs a merged methylation matrix, runs Metilene for DMR detection, filters the results, and generates quick visualizations.
BIsnake2001
research
scientific-computing
3
local-methylation-profile
This skill analyzes the local DNA methylation profiles around target genomic regions provide by user. Use this skill when you want to vasulize the average methylation profile around target regions (e.g. TSS, CTCF peak or other target regions).
BIsnake2001
research
scientific-computing
3
replicates-incorporation
This skill manages experimental reproducibility, pooling, and consensus strategies. This skill operates in two distinct modes based on the input state. (1) Pre-Peak Calling (BAM Mode): It merges all BAMs, generate the merge BAM file to prepare for track generation and (if provided with >3 biological replicates) splits them into 2 balanced "pseudo-replicates" to prepare for peak calling. (2) Post-Peak Calling (Peak Mode): If provided with peak files (only support two replicates, derived from either 2 true replicates or 2 pseudo-replicates), it performs IDR (Irreproducible Discovery Rate) analysis, filters non-reproducible peaks, and generates a final "conservative" or "optimal" consensus peak set. Trigger this skill when you need to handle more than two replicates (creating pseudo-reps) OR when you need to merge peak lists.
BIsnake2001
research
scientific-computing
3
regulatory-community-analysis-chia-pet
This skill performs protein-mediated regulatory community analysis from ChIA-PET datasets and provide a way for visualizing the communities. Use this skill when you have a annotated peak file (in BED format) from ChIA-PET experiment and you want to identify the protein-mediated regulatory community according to the BED and BEDPE file from ChIA-PET.
BIsnake2001
research
scientific-computing
3
global-methylation-profile
This skill performs genome-wide DNA methylation profiling. It supports single-sample and multi-sample workflows to compute methylation density distributions, genomic feature distribution of the methylation profile, and sample-level clustering/PCA. Use it when you want to systematically characterize global methylation patterns from WGBS or similar per-CpG methylation call files.
BIsnake2001
research
scientific-computing
3
hic-loop-calling
This skill performs chromatin loop detection from Hi-C .mcool files using cooltools.
BIsnake2001
research
scientific-computing
3
motif-scanning
This skill identifies the locations of known transcription factor (TF) binding motifs within genomic regions such as ChIP-seq or ATAC-seq peaks. It utilizes HOMER to search for specific sequence motifs defined by position-specific scoring matrices (PSSMs) from known motif databases. Use this skill when you need to detect the presence and precise genomic coordinates of known TF binding motifs within experimentally defined regions such as ChIP-seq or ATAC-seq peaks.
BIsnake2001
research
scientific-computing
2
exploratory-data-analysis
Perform comprehensive exploratory data analysis on research data. Automatically analyze data structure, quality, distributions, and generate insights. Use when the user provides a dataset, asks to "explore data", "analyze this file", or needs to understand their data before formal analysis.
braselog
research
scientific-computing
2
research-synthesis
Synthesize research findings for OAK planning using the oak.plan-research workflow. Use when consolidating findings from codebase exploration, comparing approaches, or creating research/*.md documents.
sirkirby
research
scientific-computing
2
memgraph-aco-queries
Query and analyze ACO pheromone graphs in Memgraph. Use for prime candidate queries, pheromone analysis, path validation, and convergence monitoring.
tonyoconnell
research
scientific-computing
2
experiment-design-checklist
Generates a rigorous experiment design given a hypothesis. Use when asked to design experiments, plan experiments, create an experimental setup, or figure out how to test a research hypothesis. Covers controls, baselines, ablations, metrics, statistical tests, and compute estimates.
GhostScientist
research
scientific-computing
2
statistical-analysis
Comprehensive statistical analysis toolkit for research. Conduct hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, Bayesian stats, power analysis, assumption checks, and APA reporting. Use when the user asks about statistics, needs help analyzing data, or when writing methods sections that include statistical approaches.
braselog
research
scientific-computing
2
designing-experiments
Interactive guidance for building Sun lab experiment configurations using MCP tools. Covers cue and segment design, trial structure configuration, and experiment state definition. Use when creating new experiments, modifying experiment configurations, setting up trial parameters, or when the user asks about experiment design, templates, or MCP configuration tools.
Sun-Lab-NBB
research
scientific-computing
2
exploratory-analysis
Systematic exploratory data analysis process - discover patterns in unfamiliar data, identify meaningful insights, formulate specific questions for deeper investigation
tilmon-engineering
research
scientific-computing
2
design-synthesis
Synthesizes research findings into design decisions via codebase investigation. Use when (1) translating research into implementation approaches, (2) selecting between design alternatives, (3) executing after /research or deep-research, or (4) preparing input for /plan phase.
bsamiee
research