Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/swaruplab/operon --skill mage-antibody-generator명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Install and run the BD Rhapsody™ Sequence Analysis Pipeline (v3.0) on a shared cluster or remote Linux server with no root and no container runtime. Covers the self-contained install bundle, reference archives, FASTQ manifests, per-library YML generation, SLURM array execution, outputs, sample-tag demultiplexing, and the failure modes that cost hours — wrong Sample_Tags_Version on nuclei runs, uncapped Maximum_Threads, node-local scratch, and pinning a stale `latest` bundle.
Advanced single-cell multi-omics analysis including scRNA-seq, scCITE-seq, scATAC-seq, and TARGET-seq. Use when analyzing single-cell data, cell type identification, trajectory analysis, differential expression, UMAP/clustering, integrating protein and RNA modalities (TotalVI), or working with Scanpy, Seurat, scvi-tools. Includes workflows for MPN, hematologic malignancies, megakaryocyte biology.
Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | mage-antibody-generator |
| description | Ab seq forge |
| keywords | ["antibody","antigen","FASTA","generation","validation"] |
| measurable_outcome | Generate the requested number of antibody sequences (default ≥5) with metadata (model checkpoint, seed) and deliver FASTA files within 10 minutes. |
| license | MIT |
| metadata | {"author":"MAGE Team","version":"1.0.0"} |
| compatibility | [{"system":"Python 3.9+ / GPU"}] |
| allowed-tools | ["run_shell_command","read_file"] |
Run the MAGE antibody generation workflow to propose antigen-conditioned antibody sequences for downstream structural validation.
cd repo and install dependencies, then point to GPU if available.python generate_antibodies.py --antigen_sequence <SEQ> --num_candidates N --output_dir ./results.README.md and repo scripts.