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Found 1,660 Skills
AI-assisted academic research workflows for literature review, paper writing, peer review, and research pipelines
Builds real-time analytics and automation with PubNub Illuminate. Covers Business Objects (schema), Metrics (aggregations), Decisions (threshold-triggered actions with the 4-step PUT workflow), Queries (ad-hoc vs saved pipelines), and Dashboards. Use when tracking KPIs, building threshold alerts, automating mute/publish/App-Context-update actions, detecting spam or anomalies, or visualizing live activity.
Routes PubNub events to external systems with no code via Events & Actions (E&A). Covers event listeners (Messages, Users, Channels, Push, Memberships), action targets (Webhook, SQS, Kinesis, S3, Kafka, IFTTT, AMQP), filter types (basic vs JSONPath), retry policy, envelopes, and batching. Use when integrating PubNub with Lambda, Kafka, SQS, S3, EventBridge, an analytics pipeline, or any external system.
Cram Engine - An AI tutor well-versed in learning science. Triggered when users mention terms like final exam cramming, final review, exam sprint, last-minute exam preparation, quick exam prep, intensive last-minute review, or use the /cram command. Based on six learning science principles including Cognitive Load Theory, Elaborative Processing, Generation Effect, and Retrieval Practice, it converts key points of university courses into efficient interactive learning sessions through a four-stage pipeline: deconstructing knowledge point tree → teaching each point individually → testing with real exam question types → diagnosing and filling knowledge gaps. Suitable for all qualitative knowledge-intensive university liberal arts courses.
Bash/Linux terminal patterns. Critical commands, piping, error handling, scripting. Use when working on macOS or Linux systems.
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that don't fit in memory.
Docling document parser for PDF, DOCX, PPTX, HTML, images, and 15+ formats. Use when parsing documents, extracting text, converting to Markdown/HTML/JSON, chunking for RAG pipelines, or batch processing files. Triggers on DocumentConverter, convert, convert_all, export_to_markdown, HierarchicalChunker, HybridChunker, ConversionResult.
Expert in deploying backends to EC2 instances using CI/CD pipelines, Docker containers, and GitHub Actions
Automate GitLab project management, issues, merge requests, pipelines, branches, and user operations via Rube MCP (Composio). Always search tools first for current schemas.
Sets up monorepo architecture with Turborepo, pnpm workspaces, shared packages, and optimized build pipelines. Use when users request "monorepo setup", "Turborepo", "pnpm workspaces", "shared packages", or "multi-package repository".
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.
GitLab best practices for merge requests, CI/CD pipelines, issue tracking, and DevOps workflows