1,148 件 ・ 関連度順
概要と使いどころ
Phone calling skill for OpenClaw: agent makes real outbound phone calls to users for alerts, briefings, reminders, and urgent notifications. Managed service, no Twilio setup needed. 100+ countries, 70+ voices.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when choosing or running NeMo Relay installation for the CLI, Python, Node.js, Rust, OpenClaw, Hermes, or maintained framework integrations before runtime configuration or quick-start setup.
日本語の概要は準備中です。原文の説明を表示しています。
Use acpx as a headless ACP CLI for agent-to-agent communication, including installed-agent inspection, prompt/exec/sessions workflows, session scoping, queueing, permissions, output formats, system-prompt overrides, and multi-agent flows authored with defineFlow/decision/decisionEdge.
日本語の概要は準備中です。原文の説明を表示しています。
Crabbox/Testbox remote proof: portable provider routing, untrusted isolation, Linux/macOS/Windows/WSL2, live E2E, diagnostics, cleanup.
日本語の概要は準備中です。原文の説明を表示しています。
Peptide-spectrum matching and protein identification from MS/MS data. Use when identifying peptides from tandem mass spectra. Covers database searching, spectral library matching, and FDR estimation using target-decoy approaches.
日本語の概要は準備中です。原文の説明を表示しています。
Supervised and unsupervised multi-omics integration with mixOmics. Includes sPLS for pairwise integration and DIABLO for multi-block discriminant analysis. Use when performing supervised multi-omics integration or identifying features that discriminate between groups.
日本語の概要は準備中です。原文の説明を表示しています。
KEGG pathway and module enrichment analysis using clusterProfiler enrichKEGG and enrichMKEGG. Use when identifying metabolic and signaling pathways over-represented in a gene list. Supports 4000+ organisms via KEGG online database.
日本語の概要は準備中です。原文の説明を表示しています。
Enumerates chemical libraries through reaction SMARTS transformations using RDKit. Generates virtual compound libraries from building blocks using defined chemical reactions with product validation. Use when creating combinatorial libraries or enumerating products from synthetic routes.
日本語の概要は準備中です。原文の説明を表示しています。
Data-independent acquisition (DIA) proteomics analysis with DIA-NN and other tools. Use when analyzing DIA mass spectrometry data with library-free or library-based workflows for deep proteome profiling.
日本語の概要は準備中です。原文の説明を表示しています。
Calculates molecular descriptors and fingerprints using RDKit. Computes Morgan fingerprints (ECFP), MACCS keys, Lipinski properties, QED drug-likeness, TPSA, and 3D conformer descriptors. Use when featurizing molecules for machine learning or filtering by drug-likeness criteria.
日本語の概要は準備中です。原文の説明を表示しています。
Quality control and assessment for proteomics data. Use when evaluating proteomics data quality before downstream analysis. Covers sample metrics, missing value patterns, replicate correlation, batch effects, and intensity distributions.
日本語の概要は準備中です。原文の説明を表示しています。
Navigate protein structure hierarchy using Biopython Bio.PDB SMCRA model. Use when accessing models, chains, residues, and atoms, iterating over structure levels, or extracting sequences from PDB files.
日本語の概要は準備中です。原文の説明を表示しています。
Filter reads by quality scores, length, and N content using Trimmomatic and fastp. Apply sliding window trimming, remove low-quality bases from read ends, and discard reads below thresholds. Use when reads have poor quality tails or require minimum quality for downstream analysis.
日本語の概要は準備中です。原文の説明を表示しています。
Multi-Omics Factor Analysis (MOFA2) for unsupervised integration of multiple data modalities. Identifies shared and view-specific sources of variation. Use when integrating RNA-seq, proteomics, methylation, or other omics to discover latent factors driving biological variation across modalities.
日本語の概要は準備中です。原文の説明を表示しています。
Similarity Network Fusion (SNF) for patient stratification using multi-omics data. Integrates multiple data types into a unified patient similarity network. Use when performing patient stratification or integrating multi-omics data into unified similarity networks.
日本語の概要は準備中です。原文の説明を表示しています。
Visualize enrichment results using enrichplot package functions. Use when creating publication-quality figures from clusterProfiler results. Covers dotplot, barplot, cnetplot, emapplot, gseaplot2, ridgeplot, and treeplot.
日本語の概要は準備中です。原文の説明を表示しています。
Taxonomic classification of ASVs using reference databases like SILVA, GTDB, or UNITE. Covers naive Bayes classifiers (DADA2, IDTAXA) and exact matching approaches. Use when assigning taxonomy to ASVs after DADA2 amplicon processing.
日本語の概要は準備中です。原文の説明を表示しています。
Remove sequencing adapters from FASTQ files using Cutadapt and Trimmomatic. Supports single-end and paired-end reads, Illumina TruSeq, Nextera, and custom adapter sequences. Use when FastQC shows adapter contamination or before alignment of short reads.
日本語の概要は準備中です。原文の説明を表示しています。
Gene Set Enrichment Analysis using clusterProfiler gseGO and gseKEGG. Use when analyzing ranked gene lists to find coordinated expression changes in gene sets without arbitrary significance cutoffs. Detects subtle but coordinated expression changes.
日本語の概要は準備中です。原文の説明を表示しています。
Meta-agent that routes bioinformatics requests to specialised sub-skills. Handles file type detection, analysis planning, report generation, and reproducibility export.
日本語の概要は準備中です。原文の説明を表示しています。
Handle paired-end FASTQ files (R1/R2) using Biopython. Use when working with Illumina paired reads, synchronizing pairs, interleaving/deinterleaving, or filtering paired data.
日本語の概要は準備中です。原文の説明を表示しています。
Perform geometric calculations on protein structures using Biopython Bio.PDB. Use when measuring distances, angles, and dihedrals, superimposing structures, calculating RMSD, or computing solvent accessible surface area (SASA).
日本語の概要は準備中です。原文の説明を表示しています。
Gene Ontology over-representation analysis using clusterProfiler enrichGO. Use when identifying biological functions enriched in a gene list from differential expression or other analyses. Supports all three ontologies (BP, MF, CC), multiple ID types, and customizable statistical thresholds.
日本語の概要は準備中です。原文の説明を表示しています。
Reactome pathway enrichment using ReactomePA package. Use when analyzing gene lists against Reactome's curated peer-reviewed pathway database. Performs over-representation analysis and GSEA with visualization and pathway hierarchy exploration.
日本語の概要は準備中です。原文の説明を表示しています。