Junjie Luo
Community@jluo41 · Baltimore, MD
Ph.D. Student in BIDS @ Johns Hopkins School of Medicine
Agent Skills by Junjie Luo
Showing 25 vetted skills indexed across 3 GitHub repositories.
logseq-templates
Create reusable LogSeq block templates with dynamic variables via the /Template command.
logseq-whiteboards
Create and edit whiteboard EDN canvases inside a LogSeq graph.
logseq-queries
Filters LogSeq blocks and pages using simple and advanced Datalog queries.
logseq-markdown
Automate creation and editing of LogSeq block-based Markdown with properties and references.
logseq-cc-records
Record Claude Code conversations to today's LogSeq journal with time-stamped summaries.
haipipe-data
Manage four-stage haipipe-data pipelines from raw source to AIData.
cc-session-summary
Export Claude Code sessions into a single self-contained Markdown file.
haipipe-end
Package trained ModelInstance_Set into Endpoint_Set and deploy to Databricks or local.
haipipe-data-0-overview
Explain the haipipe architecture and its 6-layer data pipeline.
haipipe-nn
Coordinate the haipipe-nn four-layer workflow for NN pipeline development.
chronicle-email
Index MS365 Outlook emails into monthly Markdown files with day sections.
notebook-cell-python
Convert Python scripts into Jupyter notebooks and Markdown documentation.
paper-incubator
Create and refine LaTeX academic paper documents through interactive conversation.
haipipe-data-2-record
Process SourceSets into temporally-aligned RecordSets with configurable pipelines.
haipipe-data-4-aidata
Transform CaseSets into ML-ready AIDataSets with splitting and input transformations.
haipipe-nn-3-instance
Defines a unified interface for managing AI models and their behaviors.
haipipe-nn-1-algorithm
Integrate external ML algorithms into a four-layer NN pipeline via Tuner interfaces.
haipipe-nn-0-overview
Outline the 4-layer NN pipeline architecture and YAML model templates.
haipipe-data-1-source
Convert CSV, XML, Parquet, and JSON files into standardized SourceSet assets.
haipipe-nn-4-modelset
Package trained AI models into versioned assets with training results.
coding-by-logging
Log structured code review discussions and AI execution confirmations in markdown sessions.
haipipe-data-3-case
Generate ML-ready feature sets from patient time-series data using TriggerFn and CaseFn modules.
haipipe-nn-2-tuner
Standardize machine learning model tuning interfaces with data conversion, fitting, inference, and serialization.
evaluation-display-skill
Generate evaluation figures and LaTeX tables from model metrics.