「ml pipelines」の検索結果

Neo4j Graph Data Science (GDS) embedded plugin via Python client or Cypher — covers graphdatascience client 2.x, GraphDataScience, gds.graph.project.native, gds.graph.project.cypher, snake_case endpoints, graph catalog operations, stream/stats/mutate/write modes, memory estimation, PageRank, Louvain, WCC, FastRP, KNN, Node Similarity, ML pipelines, and cleanup. Use for Aura Pro, self-managed, local, or offline Neo4j DBMS with the GDS plugin installed. Does NOT cover Aura Graph Analytics GDS Sessions, AuraGraphDataScience, GdsSessions, gds.graph.project.remote, or AuraDB Cypher API projection/session management — use neo4j-aura-graph-analytics-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover driver setup — use neo4j-driver-python-skill or other driver skill.

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Professional DOCX document creation, editing, and formatting using OpenXML SDK (.NET). Three pipelines: (A) create new documents from scratch, (B) fill/edit content in existing documents, (C) apply template formatting with XSD validation gate-check. MUST use this skill whenever the user wants to produce, modify, or format a Word document — including when they say "write a report", "draft a proposal", "make a contract", "fill in this form", "reformat to match this template", or any task whose final output is a .docx file. Even if the user doesn't mention "docx" explicitly, if the task implies a printable/formal document, use this skill.

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Converting document formats (Markdown/Word/Excel/PDF/HTML) and generating reusable conversion scripts. Use for distributable specs, accessibility-compliant PDFs, or Pandoc/LibreOffice pipelines.

日本語の概要は準備中です。原文の説明を表示しています。

Guidance for scaffolding new Rust projects. Use when: (1) starting a new Rust project or workspace, (2) configuring Cargo.toml best practices, (3) setting up CI pipelines for Rust, (4) organizing a multi-crate workspace, (5) configuring clippy, rustfmt, and linting.

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Configures CI/CD pipelines for automated Python package publishing to PyPI or GitLab Package Registry. Use when creating GitHub Actions or GitLab CI release workflows, setting up trusted publishing or API token-based PyPI authentication, configuring version management with git tags and hatch-vcs, writing pyproject.toml publishing metadata, testing packages against TestPyPI, or documenting the release process for a Python project.

日本語の概要は準備中です。原文の説明を表示しています。

Use when scraping JavaScript-heavy pages or SPAs, crawling multiple URLs concurrently, extracting structured data with reusable CSS/JSON schemas, or building automated web data pipelines. Wraps the Crawl4AI library (`crwl` CLI and Python SDK) with schema-generation patterns for LLM-free extraction. Triggers on crawl4ai, crwl, scrape JS-heavy site, scrape SPA, headless browser scrape, schema-based extraction, batch crawl, sitemap crawl, web data pipeline. SKIP when a static HTML page can be read with `defuddle` / `fetch-web` — those are faster cold-start and don't need a browser.

日本語の概要は準備中です。原文の説明を表示しています。

Scaffold Quarkus + LangChain4j projects end-to-end and add agentic components to existing ones — AI services, tools, agents and multi-agent workflows, RAG pipelines, MCP clients and servers (Model Context Protocol), guardrails, and embedding store setups. Use this whenever the user wants a new Quarkus + LangChain4j project or module created, or an agentic component added to an existing project in this stack, including a baseline pom.xml, application.properties, project layout, or starter classes.

日本語の概要は準備中です。原文の説明を表示しています。

Generate interactive, animated, responsive HTML-based terminal simulators to showcase CLI commands, scripts, pipelines, or interactive workflows. Use this whenever you need to visually showcase a command line sequence, mock interactive CLI tools, or simulate code executions.

日本語の概要は準備中です。原文の説明を表示しています。

Audit manufacturing predictive maintenance systems for OPC-UA/MQTT sensor data pipelines, time-series storage retention, ML model lifecycle (training-serving skew, drift detection, retraining triggers), MTBF/MTTF/MTTR reliability calculations, Weibull survival analysis.

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Designs a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, or writing agent code.

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Use this skill when authoring or debugging GitLab CI/CD pipelines (.gitlab-ci.yml) — e.g. "add a CI pipeline for this Drupal project", "run phpcs/phpstan/phpunit in GitLab CI", "deploy with drush from a pipeline", "why is my job not running", "cache composer dependencies", or anything involving stages, rules, artifacts, environments, or GitLab runners.

日本語の概要は準備中です。原文の説明を表示しています。

Invoke and resume YAML-defined pipelines by name — /pipeline auto-dev runs the full release pipeline

日本語の概要は準備中です。原文の説明を表示しています。

Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.

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Introduce people to AI, agentic AI, and AI skills with appropriate pedagogy for any audience. Covers LLM analogies, progressive complexity paths, aha-moment demos, audience-tailored explanations, workshop design, and overcoming emotional barriers to AI adoption. Activate on 'explain AI', 'introduce AI', 'AI workshop', 'AI demo', 'teach AI', 'AI literacy', 'onboard to AI', 'AI evangelism', 'explain agentic', 'explain skills to', 'AI training session', 'AI onboarding'. NOT for teaching ML engineering (use ai-engineer), not for building AI features (use ai-engineer), not for prompt engineering (use prompt-engineer), not for building training data pipelines.

日本語の概要は準備中です。原文の説明を表示しています。

Scans GitHub Actions workflows and CI/CD pipeline configurations for supply chain attack vectors including unpinned actions, script injection via expressions, dependency confusion, and secrets exposure. Uses PyGithub and YAML parsing for automated audit. Use when hardening CI/CD pipelines or investigating compromised build systems.

日本語の概要は準備中です。原文の説明を表示しています。

Configures Codemagic CI/CD pipelines for Flutter using `codemagic.yaml`. Use when setting up automated Flutter builds on Codemagic, configuring iOS code signing without Fastlane Match, deploying to App Store Connect or Google Play from Codemagic, caching pub dependencies in Codemagic, setting up encrypted environment variable groups, running tests with Codemagic's test dashboard, or choosing between Codemagic and GitHub Actions + Fastlane for a Flutter CI/CD pipeline.

日本語の概要は準備中です。原文の説明を表示しています。

Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster.

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with Coveo — coveo enterprise search platform management including sources, indexes, query pipelines, machine learning models, usage analytics, and security providers. Covers indexing health, query performance, ML model status, content freshness, and license utilization.

日本語の概要は準備中です。原文の説明を表示しています。

Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.

日本語の概要は準備中です。原文の説明を表示しています。

Create, generate, or scaffold GitHub Actions workflows, action.yml, or .github/workflows CI/CD pipelines.

日本語の概要は準備中です。原文の説明を表示しています。

Create polished, validated architecture, workflow, sequence, data-flow, and lifecycle/state diagrams as explorable standalone HTML with inline SVG, dark/light themes, optional trace motion, and PNG/JPEG/WebP/SVG/WebM export. Accept plain-language requirements or pasted Mermaid flowchart, sequenceDiagram, and stateDiagram input; inspect repository evidence when the diagram must reflect real code. Use when the user asks to visualize system architecture, infrastructure, cloud/security/network topology, technical workflows, API call sequences, request lifecycles, data pipelines, ETL/ELT, data lineage, state machines, or to convert/beautify Mermaid.

日本語の概要は準備中です。原文の説明を表示しています。

Write custom Semgrep SAST rules in YAML to tespit etme
application-specific vulnerabilities, enforce coding standards, and integrate into CI/CD pipelines.

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Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

日本語の概要は準備中です。原文の説明を表示しています。

Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.

日本語の概要は準備中です。原文の説明を表示しています。