Results for “amap”

Create ASP.NET Minimal API endpoints with proper types and comprehensive OpenAPI/Swagger documentation.

Perform nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows using the umap-learn library.

Performs advanced network reconnaissance using Nmap's scripting engine, timing controls, evasion techniques, and output parsing to discover hosts, enumerate services, detect vulnerabilities, and fingerprint operating systems across authorized target networks.

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

Simulates ARP spoofing attacks in authorized lab or pentest environments using arpspoof, Ettercap, and Scapy to demonstrate man-in-the-middle risks, test network detection capabilities, and validate ARP inspection countermeasures.

Detect and prevent ARP spoofing attacks using ARPWatch, Dynamic ARP Inspection, Wireshark analysis, and custom Python monitoring scripts to protect against man-in-the-middle interception.

Use when masking columns or coordinate ranges in multiple-sequence alignments before downstream HMMER or alignment-processing steps.

>- Advanced Product Quality Planning (APQP) — plan and track a new product launch through all 5 phases, identify deliverables per phase, run gate reviews, and ensure quality outputs are complete before Start of Production (SOP). Use when launching a new part, managing an APQP project, or auditing APQP completeness. Covers AIAG APQP 2nd edition and IATF 16949 §8.3.

Computes mass-spectral similarity and identifies compounds for metabolomics with matchms — comparing mass spectra, scoring similarity (cosine, modified cosine), and searching spectral libraries to annotate unknowns. Use when matching MS/MS spectra, identifying metabolites, or library searching; for full LC-MS/MS proteomics pipelines use pyopenms. Part of the AlterLab Academic Skills suite.

Memory (acaf-memory)

Use for writing, reviewing, debugging, testing, or validating Python mpmath arbitrary-precision numerical code. Trigger on mpf, mpc, mp.dps, workdps, interval arithmetic, high-precision quadrature, root finding, special functions, matrices, inverse transforms, or precision/convergence failures. Do not use for ordinary NumPy vectorization, SymPy symbolic manipulation, decimal currency arithmetic, or machine-float code with no precision requirement.

Action (apaz-action)

Initialize team config for a project. Creates .agenteam/config.yaml (or legacy agenteam.yaml) and generates .codex/agents/*.toml.