Packs
1 packResults for “pe-files”
10 skillsgeniml
Train unsupervised machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.
30.2k · bundle
verify-ui
Visually verify UI changes with Playwright screenshots and Pencil layout checks before proceeding. Use when a change touches frontend/UI files and needs visual confirmation, not just passing tests.
1
markitdown
Convert various file formats (PDF, Office documents, images, audio, web content, structured data) to Markdown optimized for LLM processing. Use when converting documents to markdown, extracting text from PDFs/Office files, transcribing audio, performing OCR on images, extracting YouTube transcripts, or processing batches of files. Supports 20+ formats including DOCX, XLSX, PPTX, PDF, HTML, EPUB, CSV, JSON, images with OCR, and audio with transcription.
1k · bundle
More results
repo-scan
Scans source code repositories across C++, Android, iOS, and Web to classify files, detect embedded third-party libraries, and produce actionable four-level verdicts per module with interactive HTML reports.
226k
geniml
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
3 · bundle
aem-rde
Deploy, inspect, log-tail, snapshot, and troubleshoot AEM Rapid Development Environments using the Adobe I/O CLI plugin.
142 · bundle
ops
Execute AEM Edge Delivery Services admin operations: manage content, cache, code, indexes, sitemaps, snapshots, logs, users, jobs, sites, config, secrets, API keys, tokens, profiles, and versioning. Also supports Document Authoring operations.
142 · bundle
geniml
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
0 · bundle
geniml
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
0 · bundle
geniml
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
5 · bundle