nodejs-backend-patterns
Build production-ready Node.js backend services with Express/Fastify, implementing middleware patterns, error handling, authentication, database integration, and API design best practices. Use when creating Node.js servers, REST APIs, GraphQL backends, or microservices architectures.
multi-cloud-architecture
Design multi-cloud architectures using a decision framework to select and integrate services across AWS, Azure, and GCP. Use when building multi-cloud systems, avoiding vendor lock-in, or leveraging best-of-breed services from multiple providers.
modern-javascript-patterns
Master ES6+ features including async/await, destructuring, spread operators, arrow functions, promises, modules, iterators, generators, and functional programming patterns for writing clean, efficient JavaScript code. Use when refactoring legacy code, implementing modern patterns, or optimizing JavaScript applications.
nft-standards
Implement NFT standards (ERC-721, ERC-1155) with proper metadata handling, minting strategies, and marketplace integration. Use when creating NFT contracts, building NFT marketplaces, or implementing digital asset systems.
Practicing TDD
Guide Test-Driven Development workflow with Red-Green-Refactor cycle. Use when developing features, fixing bugs, or when user mentions TDD/テスト駆動開発/test-first.
paypal-integration
Integrate PayPal payment processing with support for express checkout, subscriptions, and refund management. Use when implementing PayPal payments, processing online transactions, or building e-commerce checkout flows.
prometheus-configuration
Set up Prometheus for comprehensive metric collection, storage, and monitoring of infrastructure and applications. Use when implementing metrics collection, setting up monitoring infrastructure, or configuring alerting systems.
prompt-engineering-patterns
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
pci-compliance
Implement PCI DSS compliance requirements for secure handling of payment card data and payment systems. Use when securing payment processing, achieving PCI compliance, or implementing payment card security measures.
rag-implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
distributed-tracing
Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.
javascript-testing-patterns
Implement comprehensive testing strategies using Jest, Vitest, and Testing Library for unit tests, integration tests, and end-to-end testing with mocking, fixtures, and test-driven development. Use when writing JavaScript/TypeScript tests, setting up test infrastructure, or implementing TDD/BDD workflows.
gitops-workflow
Implement GitOps workflows with ArgoCD and Flux for automated, declarative Kubernetes deployments with continuous reconciliation. Use when implementing GitOps practices, automating Kubernetes deployments, or setting up declarative infrastructure management.
fastapi-templates
Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.
Applying Next.js Basic Principles
Apply Next.js design principles and best practices for App Router, Server Components, caching strategies, and modern patterns including Next.js 16 updates. Use when building Next.js applications, implementing features, reviewing architecture, migrating to Next.js 16, or when the user mentions Next.js development, components, routing, optimization, or version updates.
Installing Plugins Manually
Manually install Claude Code plugin components when official plugin installation fails. Use when `/plugin install` succeeds but plugins don't load, when verifying plugin installation, or when user mentions plugin installation issues.
react-modernization
Upgrade React applications to latest versions, migrate from class components to hooks, and adopt concurrent features. Use when modernizing React codebases, migrating to React Hooks, or upgrading to latest React versions.
Logging Implementation
Manage implementation logs in _docs/templates/ with consistent format. Use when starting project work, completing implementations, or when user mentions 実装ログ/implementation log.
multi-agent-orchestrator
Orchestrate parallel CLI agents (Claude Code, Codex, Gemini) for competitive evaluation. Use when user says "run multi-agent", "compare agents", "launch competitive evaluation", "use parallel agents", or complex tasks (>7/10) where multiple approaches exist and best solution matters.
claude-code-sessions
Use when user says "resume session", "what did I work on", or "find conversation about [topic]". Automatically searches, resumes, and analyzes Claude Code sessions. Handles session discovery, content search, and automatic session restoration from any directory.
skill-creator
Create/improve self-healing Claude Code skills with Knowledge Framework and version tracking. Use when user says "create skill", "improve skill", "update skill", "fix skill error", "создай скил", "улучши скил", or works with SKILL.md and SKILL_RELEASE_LOG.md files.
youtube-to-knowledge-doc
Use when user provides YouTube URL and says "document this", "create notes", or "save this video". Automatically extracts transcript, determines folder placement, and generates Knowledge Framework documentation with MECE structure, Mermaid diagrams, and clickable timestamp citations.
knowledge-framework
Auto-applies MECE/BFO framework (thesis, Mermaid TD/LR, numbered sections, Ground Truth) to ANY .md file creation.
prd-generator
Transform product requirements, ideas, or concepts into professional development resources. Use when users request help with product planning, PRD creation, work breakdown, or converting ideas into structured development plans. Triggers include phrases like "create a PRD", "break down this feature", "plan this product", "write requirements", "work breakdown structure", or providing product ideas that need to be formalized into development artifacts.
museum-search
Search the Met Museum Open Access Paintings collection using semantic search, find similar artworks, and search by image. Use this when users ask about art, paintings, museum collections, or want to find artworks by description, visual similarity, or artist.
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.
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.
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.
hic-compartment-shift
This skill performs A/B compartment shift analysis between two Hi-C samples.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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