Results for “pmapper”

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.

Generate a complete PHP Model Context Protocol server project with tools, resources, prompts, and tests using the official PHP SDK.

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.

pnpm package manager. Fast, disk-efficient with excellent monorepo support. Use when managing dependencies or setting up monorepos. USE WHEN: user mentions "pnpm", "pnpm workspace", "pnpm-workspace.yaml", asks about "pnpm commands", "pnpm install", "workspace protocol" DO NOT USE FOR: npm (use standard npm commands), yarn (use yarn commands), bun package manager

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

Analyze network packet captures (PCAP/PCAPNG) using Wireshark, tshark, tcpdump, and Python to reconstruct communications, extract files, and identify malicious traffic.

Guides the creation of high-quality MCP servers that let LLMs interact with external services through well-designed tools, covering planning, implementation, testing, and evaluation.

Guides the creation of high-quality MCP servers that let LLMs interact with external services through well-designed tools, covering planning, implementation, testing, and evaluation.

Manage Node.js dependencies with pnpm, including workspaces, catalogs, patches, and supply-chain security.

Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.

Mapeador do Time Reversa Docs. Produz as páginas de estrutura espacial do mini-site: arquitetura 3D (Code City via Three.js), module map 2D (force-directed via D3), e topologia side-by-side (legado vs moderno vs híbrido). Ative com /reversa-docs-mapper, reversa-docs-mapper, regenerar arquitetura, refazer mapa de módulos, code city do projeto.

Detect pnpm workspace structure, optional Turborepo overlay, package metadata, and internal dependency graph for monorepo-aware skills

Filtros de química medicinal. Aplique regras de similaridade a fármacos (Lipinski, Veber), filtros PAINS, alertas estruturais, métricas de complexidade, para priorização de compostos e filtragem de bibliotecas.

Use when converting `PubmedArticle` XML into `Pubmed-entry` ASN.1 text, or into the intermediate XML form used before final ASN.1 emission.

Guides the creation of high-quality MCP servers, covering design, implementation, testing, and evaluation for integrating external services with LLMs.

Build complete mass-spectrometry workflows with pyOpenMS — feature detection, peptide identification, protein quantification, and full LC-MS/MS pipelines across many MS file formats (mzML, mzXML) and algorithms. Use for comprehensive proteomics and MS data processing — for simple spectral comparison and metabolite identification use matchms. Part of the AlterLab Academic Skills suite.

Use when searching one or more protein query sequences against a protein sequence database with HMMER's one-pass sequence-vs-sequence searcher.

Use when running HMMER master or worker daemon services that front `phmmer`, `hmmsearch`, and `hmmscan` against cached databases.

Process and analyze mass spectrometry data: import spectra from MGF, mzML, MSP, and JSON formats; apply 40+ filters for metadata harmonization and peak cleaning; compute spectral similarities (cosine, modified cosine) for compound identification; build reproducible processing pipelines.

Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.

Manages Node.js dependencies with strict resolution, workspaces, catalogs, patches, and overrides, including CI/CD setup and migration guidance.