Results for “sbml”

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

Generates OMG-compliant BPMN 2.0 XML and SVG diagrams from natural language process descriptions, with validation, automatic layout, and optional process optimization advisories.

Generate XML configuration files and execute SSMA Console commands for Oracle to SQL Server database migration, including schema conversion and data migration.

Entrena modelos de machine learning con Scikit-learn, LightGBM y XGBoost, desde un baseline hasta un modelo productivo con validación robusta y explicabilidad.

Front door to the small-business toolkit — routes a vague or specific request to the best skill and explains what's available.

Identifies and exploits SMB protocol vulnerabilities using Metasploit Framework during authorized penetration tests to demonstrate risks from unpatched Windows systems, misconfigured shares, and weak authentication in enterprise networks.

Choose between DML statements and Database class methods for bulk Salesforce operations, handling partial success, DMLOptions, and error collection.

Detect adversary lateral movement across networks using Splunk SPL queries against Windows authentication logs, SMB traffic, and remote service abuse.

Battery Management System reference — cell balancing, SOC estimation, thermal management, and protection circuits. Use when designing BMS for lithium-ion packs, evaluating battery safety, or understanding cell monitoring.

Sets up the BMad ML module in a project. Use when the user requests to 'install ML module', 'configure BMad ML', or 'setup BMad ML'.

Sets up BMad Builder module in a project. Use when the user requests to 'install bmb module', 'configure BMad Builder', or 'setup BMad Builder'.

Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.

SA-1B: Segment Anything 1 Billion Masks Dataset

Sets up BMad Builder module in a project. Use when the user requests to 'install bmb module', 'configure BMad Builder', or 'setup BMad Builder'.

Query Belgian railway (NMBS/SNCB) schedules via the irail CLI.

Exploits the MS17-010 (EternalBlue) vulnerability in Microsoft's SMBv1 implementation for authorized security testing, red team exercises, and penetration testing engagements.

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.

Detects and analyzes Bluetooth Low Energy (BLE) security attacks including sniffing, replay attacks, GATT enumeration abuse, and Man-in-the-Middle interception using Ubertooth One, nRF52840, bleak, and crackle.

Onboard a new small-business owner — connect their first two tools, run one recipe, interview them, store context, and set a weekly check-in.

Generate SKILL.md files with proper YAML frontmatter, capabilities documentation, and usage examples following Babysitter SDK conventions.

Checks library loans and searches the National Library Board of Singapore catalogue with filters for availability, material type, collection, and location.

Queries Belgian railway (NMBS/SNCB) schedules via the iRail CLI, including departures, connections, train compositions, and service disruptions.

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

Trains machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.

Projeta métricas, logs, traces, dashboards, alertas e SLO com Prometheus, Grafana, Loki, Tempo, Mimir, Zabbix e Alertmanager, incluindo consultas PromQL e boas práticas de alertas.

Build effective detection rules using Splunk Search Processing Language (SPL) correlation searches to identify security threats in SOC environments.

Use when planning or sequencing an ICML manuscript workflow end to end - from topic selection and drafting through OpenReview submission, double-blind review, author response, camera-ready, PMLR publication, public original-submission release, and rerouting decisions. Use when you need the next ICML skill, the official page to reopen, or the blocking gap for the current stage.

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.

Essa habilidade deve ser usada ao trabalhar com dados de intervalos genômicos (arquivos BED) para tarefas de machine learning. Use para treinar embeddings de regiões (Region2Vec, BEDspace), análise de scATAC-seq de célula única (scEmbed), construir picos consensuais (universos), ou qualquer análise baseada em ML de regiões genômicas. Aplica-se a coleções de arquivos BED, dados scATAC-seq, conjuntos de dados de acessibilidade de cromatina e aprendizado de recursos genômicos baseado em regiões.

Generate CycloneDX and SPDX SBOMs from container images and filesystems, scan them for vulnerabilities with Grype, and sign attestations with Cosign for supply-chain trust.

Train unsupervised machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.