writing-skills
Use when creating or updating SKILL.md documentation - Explains how and why to create a skill.
alignment-level-QC
Calculates technical mapping statistics for any aligned BAM file (ChIP or ATAC). It assesses the performance of the aligner itself by generating metrics on read depth, mapping quality, error rates, and read group data using samtools and Picard.Use this skill to check "how well the reads mapped" or to validate BAM formatting/sorting before further processing. Do NOT use this skill for biological signal validation (like checking for peaks or open chromatin) or for filtering/removing reads.
chromatin-state-inference
This skill should be used when users need to infer chromatin states from histone modification ChIP-seq data using chromHMM. It provides workflows for chromatin state segmentation, model training, state annotation.
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. ChIPseeker mode is also supported according to requirements.
BAM-filtration
Performs data cleaning and removal operations. This skill takes a raw BAM and creates a new, "clean" BAM file by actively removing artifacts: mitochondrial reads, blacklisted regions, PCR duplicates, and unmapped reads. Use this skill to "clean," "filter," or "remove bad reads" from a dataset. This is a prerequisite step before peak calling. Do NOT use this skill if you only want to view statistics without modifying the file.
differential-tad-analysis
This skill performs differential topologically associating domain (TAD) analysis using HiCExplorer's hicDifferentialTAD tool. It compares Hi-C contact matrices between two conditions based on existing TAD definitions to identify significantly altered chromatin domains.
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. What's more, this skill can help user visualize the signal profiles around TSS or target regions. Use this skill when you have filtered and generated the clean BAM file (e.g. `*.filtered.bam`).
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.
UMR-LMR-PMD-detection
This pipeline performs genome-wide segmentation of CpG methylation profiles to identify Unmethylated Regions (UMRs), Low-Methylated Regions (LMRs), and Partially Methylated Domains (PMDs) using whole-genome bisulfite sequencing (WGBS) methylation calls. The pipeline provides high-resolution enhancer-like LMRs, promoter-associated UMRs, and large-scale PMDs characteristic of reprogramming, aging, or cancer methylomes, enabling integration with chromatin accessibility, TF binding, and genome architecture analyses.
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.
hic-matrix-qc
This skill performs standardized quality control (QC) on Hi-C contact matrices stored in .mcool or .cool format. It computes coverage and cis/trans ratios, distance-dependent contact decay (P(s) curves), coverage uniformity, and replicate correlation at a chosen resolution using cooler and cooltools. Use it to assess whether Hi-C data are of sufficient quality for downstream analyses such as TAD calling, loop detection, and compartment analysis.
functional-enrichment
Perform GO and KEGG functional enrichment using HOMER from genomic regions (BED/narrowPeak/broadPeak) or gene lists, and produce R-based barplot/dotplot visualizations. Use this skill when you want to perform GO and KEGG functional enrichment using HOMER from genomic regions or just want to link genomic region to genes.
De-novo-motif-discovery
This skill identifies novel transcription factor binding motifs in the promoter regions of genes, or directly from genomic regions of interest such as ChIP-seq peaks, ATAC-seq accessible sites, or differentially acessible regions. It employs HOMER (Hypergeometric Optimization of Motif Enrichment) to detect both known and previously uncharacterized sequence motifs enriched within the supplied genomic intervals. Use the skill when you need to uncover sequence motifs enriched or want to know which TFs might regulate the target regions.
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.
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.
hic-compartments-calling
This skill performs PCA-based A/B compartments calling on Hi-C .mcool datasets using pre-defined MCP tools from the cooler-tools, cooltools-tools, and plot-hic-tools servers.
hic-tad-calling
This skill should be used when users need to identify topologically associating domains (TADs) from Hi-C data in .mcools (or .cool) files or when users want to visualize the TAD in target genome loci. It provides workflows for TAD calling and visualization.
hic-loop-calling
This skill performs chromatin loop detection from Hi-C .mcool files using cooltools.
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.
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).
integrative-DMR-DEG
This skill performs correlation analysis between differential methylation and differential gene expression, identifying genes with coordinated epigenetic regulation. It provides preprocessing and integration workflows, using promoter-level methylation–expression relationships.
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.
methylation-variability-analysis
This skill provides a complete and streamlined workflow for performing methylation variability and epigenetic heterogeneity analysis from whole-genome bisulfite sequencing (WGBS) data. It is designed for researchers who want to quantify CpG-level variability across biological samples or conditions, identify highly variable CpGs (HVCs), and explore epigenetic heterogeneity.
correlation-methylation-epiFeatures
This skill provides a complete pipeline for integrating CpG methylation data with chromatin features such as ATAC-seq signal, H3K27ac, H3K4me3, or other histone marks/TF signals.
atac-footprinting
This skill performs transcription factor (TF) footprint analysis using TOBIAS on ATAC-seq data. It corrects Tn5 sequence bias, quantifies TF occupancy at motif sites, generates footprint scores, and optionally compares differential TF binding across conditions.
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.
peak-calling
Perform peak calling for ChIP-seq or ATAC-seq data using MACS3, with intelligent parameter detection from user feedback. Use it when you want to call peaks for ChIP-seq data or ATAC-seq data.
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.
hic-compartment-shift
This skill performs A/B compartment shift analysis between two Hi-C samples.
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.
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.
ChIPseq-QC
Performs ChIP-specific biological validation. It calculates metrics unique to protein-binding assays, such as Cross-correlation (NSC/RSC) and FRiP. Use this when you have filtered the BAM file and called peaks for ChIP-seq data. Do NOT use this skill for ATAC-seq data or general alignment statistics.
differential-region-analysis
The differential-region-analysis pipeline identifies genomic regions exhibiting significant differences in signal intensity between experimental conditions using a count-based framework and DESeq2. It supports detection of both differentially accessible regions (DARs) from open-chromatin assays (e.g., ATAC-seq, DNase-seq) and differential transcription factor (TF) binding regions from TF-centric assays (e.g., ChIP-seq, CUT&RUN, CUT&Tag). The pipeline can start from aligned BAM files or a precomputed count matrix and is suitable whenever genomic signal can be summarized as read counts per region.
TF-differential-binding
The TF-differential-binding pipeline performs differential transcription factor (TF) binding analysis from ChIP-seq datasets (TF peaks) using the DiffBind package in R. It identifies genomic regions where TF binding intensity significantly differs between experimental conditions (e.g., treatment vs. control, mutant vs. wild-type). Use the TF-differential-binding pipeline when you need to analyze the different function of the same TF across two or more biological conditions, cell types, or treatments using ChIP-seq data or TF binding peaks. This pipeline is ideal for studying regulatory mechanisms that underlie transcriptional differences or epigenetic responses to perturbations.
Generative Framework
Conversation-driven specification and execution of healthcare data generation at scale
healthsim-membersim
MemberSim generates realistic synthetic claims and payer data for testing claims processing systems, payment integrity, and benefits administration.
healthsim-networksim
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healthsim-patientsim
Generate realistic clinical patient data including demographics, encounters, diagnoses, medications, labs, and vitals. Use when user requests: (1) patient records or clinical data, (2) EMR test data, (3) specific clinical cohorts like diabetes or heart failure, (4) HL7v2 or FHIR patient resources.
healthsim-populationsim
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healthsim-trialsim
Generate realistic clinical trial synthetic data including study definitions, sites, subjects, visits, adverse events, efficacy assessments, and disposition. Use when user requests: clinical trial data, CDISC/SDTM/ADaM datasets, trial cohorts (Phase I/II/III/IV), FDA submission test data, or specific therapeutic areas like oncology or biologics/CGT.
healthsim-rxmembersim
RxMemberSim generates realistic synthetic pharmacy data for testing PBM systems, claims adjudication, and drug utilization review. Use when user requests: (1) pharmacy claims or prescription data, (2) DUR alerts or drug interactions, (3) formulary or tier cohorts, (4) pharmacy prior authorization, (5) NCPDP formatted output.
template-skill
Replace with description of the skill and when Claude should use it.
aesthetic
Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.
anthropic-architect
Determine the best Anthropic architecture for your project by analyzing requirements and recommending the optimal combination of Skills, Agents, Prompts, and SDK primitives.
anthropic-prompt-engineer
Master Anthropic's prompt engineering techniques to generate new prompts or improve existing ones using best practices for Claude AI models.
databases
Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.
frontend-dev-guidelines
Frontend development guidelines for React/TypeScript applications. Modern patterns including Suspense, lazy loading, useSuspenseQuery, file organization with features directory, MUI v7 styling, TanStack Router, performance optimization, and TypeScript best practices. Use when creating components, pages, features, fetching data, styling, routing, or working with frontend code.
notebooklm
Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.
ai-multimodal
Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.
better-auth
Implement authentication and authorization with Better Auth - a framework-agnostic TypeScript authentication framework. Features include email/password authentication with verification, OAuth providers (Google, GitHub, Discord, etc.), two-factor authentication (TOTP, SMS), passkeys/WebAuthn support, session management, role-based access control (RBAC), rate limiting, and database adapters. Use when adding authentication to applications, implementing OAuth flows, setting up 2FA/MFA, managing user sessions, configuring authorization rules, or building secure authentication systems for web applications.
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