design-concepts
Creates conceptual designs that illustrate design strategy and approach. Leverages research insights and design briefs to develop UI concepts, mood boards, and interactive prototypes. Translates strategic direction into visual design explorations that communicate intent and gather feedback.
design-production
Creates production-ready design files, prototypes, and specifications for development teams. Translates validated concepts into detailed, implementable designs with comprehensive specs for developers. Produces Figma files (via API), high-fidelity prototypes, design specifications, and animation files.
design-research
Conducts user experience research and analysis to inform design decisions. Reviews first-party and third-party user data, analyzes industry trends from UX and visual design perspectives, and plans user research studies. Creates personas, customer segments, design principles, design roadmaps, and research discussion guides.
token-economy
Apply token optimization when writing docs, changelogs, MCP tasks. Quality #1, Tokens #2.
unused-code-cleanup
Systematically identify and remove unused imports, variables, and dead code from TypeScript/React projects using --noUnusedLocals and --noUnusedParameters compiler flags
safe-file-removal
Use safe-rm command to safely 'remove' files by renaming them to .obsolete instead of permanent deletion. Reversible, collision-safe, hook-compliant.
changelog-updater
Update CHANGELOG.md and TEST-CHANGELOG.md with new entries following Keep a Changelog format and token optimization principles. Use when adding changes to the changelog, documenting new features, fixes, or optimizations.
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.
refining-requirements
Use when PRD is prose-heavy, ambiguous, has scattered file paths, missing API contracts, or says "similar to X" without explanation. Transforms rough requirements into implementation-ready specs. Auto-detects tech stack, validates file paths (EXISTS/CREATE/VERIFY markers), handles greenfield and multi-stack projects. Do NOT use for simple bug fixes, typos, or already-structured Jira tickets with clear file paths and acceptance criteria.
debugging-orm-queries
Converts ORM calls to raw SQL and analyzes query performance. Detects N+1 queries, missing indexes, and other anti-patterns. Use when debugging slow queries, tracing ORM-generated SQL, or optimizing database performance for Sequelize, Prisma, TypeORM (Node.js), GORM, sqlc, sqlx, ent (Go), or SQLAlchemy, Django ORM, Peewee (Python).
troubleshooting-kubernetes
Diagnoses and fixes Kubernetes issues with interactive remediation. Use when pods crash (CrashLoopBackOff, OOMKilled), services unreachable (502/503, empty endpoints), deployments stuck (ImagePullBackOff, pending). Also use when tempted to run kubectl fix commands directly without presenting options, or when user says "just fix it" for K8s issues.
writing-skills
Use when creating new skills, editing existing skills, or verifying skills work before deployment - applies TDD to process documentation by testing with subagents before writing, iterating until bulletproof against rationalization
testing-skills-with-subagents
Use when creating or editing skills, before deployment, to verify they work under pressure and resist rationalization - applies RED-GREEN-REFACTOR cycle to process documentation by running baseline without skill, writing to address failures, iterating to close loopholes
creating-handoffs
Creates comprehensive handoff documents for seamless AI agent session transfers. Triggered when: (1) user requests handoff/memory/context save, (2) context window approaches capacity, (3) major task milestone completed, (4) work session ending, (5) user says 'save state', 'create handoff', 'I need to pause', 'context is getting full', (6) resuming work with 'load handoff', 'resume from', 'continue where we left off'. Proactively suggests handoffs after substantial work (multiple file edits, complex debugging, architecture decisions). Solves long-running agent context exhaustion by enabling fresh agents to continue with zero ambiguity.
optimizing-queries
Analyzes and optimizes SQL/NoSQL queries for performance. Use when reviewing query performance, optimizing slow queries, analyzing EXPLAIN output, suggesting indexes, identifying N+1 problems, recommending query rewrites, or improving database access patterns. Supports PostgreSQL, MySQL, SQLite, MongoDB, Redis, DynamoDB, and Elasticsearch.
detecting-ai-code
Use when auditing code for AI authorship, reviewing acquisitions/contractors, verifying academic integrity, or during code review - provides systematic tiered framework for detecting fully AI-generated AND AI-assisted code patterns with confidence scoring
code-reviewer
Comprehensive code review skill for TypeScript, JavaScript, Python, Swift, Kotlin, Go. Includes automated code analysis, best practice checking, security scanning, and review checklist generation. Use when reviewing pull requests, providing code feedback, identifying issues, or ensuring code quality standards.
office:calendar-management
Manage calendar events, check conflicts, handle scheduling from emails. Use when adding events or coordinating meetings to ensure proper timezone handling and conflict detection.
office:email-management
Handle email tasks (checking inbox, drafting replies, managing threads, adding events to calendar). Use when working with emails to prevent common mistakes like broken threading or missing recipients.
office:crm-management
Manage contacts, companies, deals, and relationships. Use when adding contacts, logging interactions, or working with CRM data to prevent duplicates and maintain data quality.
office:onboarding
Set up your personal productivity style and preferences. Use when you first install office or want to customize your workflow patterns.
workflow
Enforces development phases. Triggers on implement, build, create, fix, refactor.
debug
Expert debugging methodology. Triggers on debug, investigate, fix bug, troubleshoot.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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`).
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.
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.
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.
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.
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.
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.
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.
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.
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.
hic-compartment-shift
This skill performs A/B compartment shift analysis between two Hi-C samples.
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.
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.
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.
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.
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).
Page 314 of 417 · 20825 results
