1.8K skills · sorted by relevance
Bayesian parameter estimation with MCMC (emcee) and probabilistic programming (PyMC). Posterior distributions, corner plots, model evidence, convergence diagnostics. Use when you need full posterior distributions, not just point estimates.
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Build AI scientist systems with the ToolUniverse Python SDK for scientific research. Covers the 3 calling patterns (`tu.run` portable dict API, `tu.tools.X` function API, direct class instantiation), tool loading, batch execution, MCP server integration, and embedding-based tool search. Use for SDK programming, custom tool composition, benchmarking pipelines, and integrating ToolUniverse into research workflows.
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Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.
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Runs the MS-DIAL preprocessing workflow (peak picking, MS2Dec spectral deconvolution, alignment, gap-filling) and imports the alignment-result table into R or Python with honest filtering. Use when preprocessing LC-MS DDA/DIA (SWATH) raw data with MS-DIAL, deciding MS-DIAL vs XCMS, configuring the MsdialConsoleApp console run, or parsing an MS-DIAL export into a clean feature matrix. For programmatic R peak detection and the feature-table-as-artifact framing see metabolomics/xcms-preprocessing; for lipid annotation mode see metabolomics/lipidomics; for MSI-level confidence honesty see metabolomics/metabolite-annotation; for drift correction and QC see metabolomics/normalization-qc.
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Programmatic untargeted LC-MS feature extraction in R with the modern xcms 4.x MsExperiment/XcmsExperiment API, taking raw mzML to a feature table via CentWave peak detection, retention-time alignment, peak-density correspondence, gap-filling, CAMERA redundancy collapse, and built-in QC feature filtering. Use when converting centroided LC-MS runs into a features-by-samples matrix and deciding centWave/grouping/alignment parameters. For drift correction and QC/CV filtering execution see metabolomics/normalization-qc; for metabolite identification see metabolomics/metabolite-annotation; for the MS-DIAL GUI alternative with MS2Dec deconvolution see metabolomics/msdial-preprocessing; for downstream statistics see metabolomics/statistical-analysis.
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When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," or "SEO health check." For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema-markup.
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Plan AI pilot programs with success criteria. TRIGGERS - Use when user needs help with ai-pilot-plan related tasks.
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Design white-label programs. TRIGGERS - Use when user needs help with agency-white-label-plan related tasks.
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Plan affordable housing programs. TRIGGERS - Use when user needs help with affordable-housing-plan related tasks.
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Optimize affiliate programs with commission and activation. TRIGGERS - Use when user needs help with affiliate-optimization related tasks.
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Design 360-degree feedback programs with surveys. TRIGGERS - Use when user needs help with 360-feedback related tasks.
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OpenAI API (developers.openai.com) の Responses API リファレンス。 text generation, embeddings, gpt-6-astra / gpt-6.1-sol / gpt-6-luna, structured outputs, streaming, function calling, programmatic / async tool calling, conversation state, prompt caching, prompt cache diagnostics, reasoning (configuration_update), background mode, compaction, multi-agent, WebSocket mode (mid-turn steering, stream_id), webhooks, error codes, rate limits, Realtime server controls, 公式 SDK (Python / JS / .NET / Java / Go / Ruby), openai CLI, Chat Completions / Assistants 移行, deprecations。
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Rust 言語リファレンス。 別名: rustlang, rs (拡張子)、Rust-lang。 TRPL (The Rust Programming Language)、Rust by Example、Reference、 Cargo Book、rustc Book、rustdoc Book、Rustonomicon (Unsafe Rust)、 Edition Guide、Standard Library、Error Index。 所有権、ライフタイム、トレイト、async / await、エラーハンドリング、マクロ。
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Run remote BLAST searches against NCBI servers using Biopython Bio.Blast.NCBIWWW. Use when identifying unknown sequences, finding homologs, picking the correct BLAST program (blastn/blastp/blastx/tblastn/tblastx/psiblast/megablast/dc-megablast), interpreting Karlin-Altschul E-values, avoiding the max_target_seqs trap (Shah 2019), choosing composition-based statistics, or limiting searches by organism. Covers RID lifecycle, database choice (nt/nr/refseq_select/swissprot), word-size and CBS taxonomy.
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Build publication-quality figures in R with ggplot2 using the grammar of graphics (data + aesthetics + geometries + scales + facets + themes) with CVD-safe palettes, cairo_pdf TrueType embedding, programmatic aes via tidy evaluation, and the theme_classic publication baseline. Use when producing static figures in R for papers, presentations, or reports.
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Detects aberrant splicing in single rare-disease patients vs a control panel using FRASER 2.0 (Bioconductor; Beta-binomial autoencoder on Intron Jaccard Index, default delta cutoff 0.1, q hyperparameter), OUTRIDER (gene-level outlier expression via autoencoder denoising), LeafcutterMD (Dirichlet-multinomial outlier mode of LeafCutter for annotation-free junctions), and DROP (Snakemake pipeline integrating FRASER2 + OUTRIDER + monoallelic expression for clinical diagnostics). The statistical model is fundamentally different from differential splicing — single-sample-vs-cohort outlier detection rather than two-group comparison. Standard tool in EU rare-disease (Solve-RD) and NIH UDN programs. Use when applying RNA-seq to undiagnosed Mendelian disease, validating predicted splice variants in clinical samples, or detecting cryptic splicing in disease tissue.
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Build static sites with Nitro CLI - a Python static site generator using nitro-ui for programmatic HTML generation instead of templates
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Comprehensive patterns and techniques for analyzing compiled binaries, understanding assembly code, and reconstructing program logic.
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Master defensive Bash programming techniques for production-grade scripts. Use when writing robust shell scripts, CI/CD pipelines, or system utilities requiring fault tolerance and safety.
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Expert patterns for Azure Functions development including isolated worker model, Durable Functions orchestration, cold start optimization, and production patterns. Covers .NET, Python, and Node.js programming models.
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Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems.
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Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.
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Automate browser testing, web scraping, and user workflow testing with Playwright across multiple browsers. Use when writing end-to-end tests for web applications, automating repetitive browser tasks, scraping data from websites, testing across Chrome/Firefox/Safari, taking screenshots for visual regression testing, testing authentication flows, filling and submitting forms programmatically, testing responsive designs across viewports, or any browser automation requiring reliable, cross-browser testing capabilities.
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Process, parse, create, and manipulate Microsoft Word (.docx) documents programmatically using libraries like docx or mammoth.js for document generation and data extraction. Use when generating Word documents from templates, extracting text and formatting from .docx files, creating reports and invoices, parsing resumes and forms, converting Word to HTML, creating mail merge documents, or automating document workflows.
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