Results for “pe-analysis”

Full ASVS 5.0 compliance assessment against a codebase and/or architecture diagrams. Reads all 346 controls from the companion CSV, performs targeted code analysis per control, and produces a complete matrix marked COMPLIANT / NON_COMPLIANT / NOT_RELEVANT — with per-control reasoning and evidence (code snippets, file:line references, diagram observations). Outputs a reviewed CSV matrix and a self-contained HTML evidence report.

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.

PTES-aligned adversarial security audit for backend, frontend, and mobile applications. Produces a CVSS-scored Hacker Report with verified PoCs and phased remediation.

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.

Audit frontend API calls against backend implementation for contract alignment and network shape. Use when "API audit", "FE-BE contract", or "review frontend API integration". Live 4xx/5xx reproduction → debug-fe-be-integration.

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.

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.

Audits a website from three perspectives—design, SEO/technical, and LLM/AI-search visibility—and produces a scored, evidence-bound report with prioritized improvements.

S-parameters, insertion loss, fields, currents, mesh control, and solver selection for RF PCB performance validation. TRIGGER: user asks to compute S-parameters, analyze insertion/return loss, extract fields or currents, compare MoM vs FEM, or control mesh for any RF PCB component. Invoke BEFORE writing sparameters() or solver code — API is non-obvious. SKIP: designing or creating components (use the specific matlab-design-pcb-* skill), material/stackup setup only (use matlab-manage-pcb-material), optimization sweeps (use matlab-optimize-pcb-design), PDN/IR-drop analysis (use matlab-analyze-pcb-pdn).

Optimize RF PCB dimensions for bandwidth, return loss, or area via patternsearch and surrogateopt with constraints. TRIGGER: user asks to optimize an RF PCB component for performance (bandwidth, return loss, insertion loss, area) or apply constraints to a design. Invoke BEFORE writing optimization code — RF PCB Toolbox has a built-in optimize() function that differs from generic fmincon/ga approaches. SKIP: designing a component from scratch without an optimization objective (use the specific matlab-design-pcb-* skill), EM analysis without optimization (use matlab-analyze-em), material/stackup setup only (use matlab-manage-pcb-material).