「data pipeline」の検索結果

Use the DVC repository skill for data versioning, pipelines, experiments, remotes/cache, metrics/params/plots, Python API usage, and DVC codebase maintenance.

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。

Route ClearML Python SDK tasks across experiment tracking, data/versioned storage, remote execution CLIs, automation pipelines, HPO, schedulers, routers, and services.

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。

Routes CogDL graph-learning workflows for experiments, graph data, models, training wrappers, and pipeline apps.

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。

Routes BasicTS time-series training, dataset, model, and pipeline workflows through focused sub-skills.

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。

Build, debug, serialize, and integrate Albumentations 2.x augmentation pipelines for images, masks, bboxes, keypoints, volumes, and PyTorch-style datasets.

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 anomalib for anomaly-detection install, data/model selection, training and evaluation, deployment and inference, and benchmark 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。

Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

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。

Automated cookie consent validation using Selenium and Playwright. Covers banner interaction testing, consent state verification, tag firing audit after consent choices, regression testing for cookie compliance, and CI/CD pipeline integration.

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。

Modern Python project architecture guide for 2025. Use when creating Python projects (APIs, CLI, data pipelines). Covers uv, Ruff, Pydantic, FastAPI, and async patterns.

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。

Assembla integration. Manage Organizations, Leads, Deals, Pipelines, Users, Goals and more. Use when the user wants to interact with Assembla data.

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。

Api4ai integration. Manage Leads, Persons, Organizations, Deals, Projects, Pipelines and more. Use when the user wants to interact with Api4ai data.

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。

Apollo.io integration. Manage Persons, Organizations, Deals, Leads, Pipelines, Users and more. Use when the user wants to interact with Apollo.io data.

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。

Apex 27 integration. Manage Organizations, Pipelines, Users, Goals, Filters. Use when the user wants to interact with Apex 27 data.

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。

ApiFlash integration. Manage Organizations, Pipelines, Projects, Users, Filters. Use when the user wants to interact with ApiFlash data.

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。

Ambivo integration. Manage Organizations, Pipelines, Users, Filters, Activities, Notes and more. Use when the user wants to interact with Ambivo data.

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。

Affinity integration. Manage Organizations, Leads, Pipelines, Users, Roles, Filters. Use when the user wants to interact with Affinity data.

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。

Agiled integration. Manage Organizations, Leads, Pipelines, Users, Goals, Filters. Use when the user wants to interact with Agiled data.

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。

Accelo integration. Manage Organizations, Leads, Pipelines, Users, Goals, Filters. Use when the user wants to interact with Accelo data.

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。

Idiomatic Go concurrency - sync primitives, channel semantics, the select statement, and the standard channel patterns (cancellation/done-channel, fan-out/fan-in, pipeline, or-done, context, worker-pool). Use when writing or reviewing Go that spawns goroutines, shares state, or coordinates over channels, even if the user never says "concurrency" - e.g. data races, deadlocks, goroutine leaks, sync/context/select usage, or "make this parallel". Distilled from luk4z7/go-concurrency-guide.

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 asked to make a launch video, product film, motion design piece, UI motion reel, Dribbble-style UI animation or animated brand/logo sting — anything rendered to MP4 from HTML with a cursor, morphing UI, springs, beat-synced music and SFX. Carries Chan's full creative direction (one shape never cut, blur content swaps, springs, real data, logo slams, no creative limits) and the proven build/render pipeline (pure seek(t), closed-form springs, Playwright subframes, ffmpeg tmix, numpy beat grid).

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。

Institutional-grade equity research skills for AI agents. 80 Claude-type skills across 14 verticals (equity-research-core, models-and-pitches, bio-pharm, scenarios, quantitative-analysis, idea-generation, options-derivatives, business-intelligence, industry-analysis, macro-strategy, portfolio-strategy, technical-analysis, risk-and-psychology, trading-as-business) powered by agentii.ai's agent-use-ready SEC filing data plane — SEC filings and 15K+ earnings call transcripts, 15.99M XBRL facts, and company profiles for 1,146+ US-public-equity tickers. Features the three-layer retrieval protocol (Document Discovery → Page Map → Deep Read, with deep-outline escalation) — measured, not asserted: 97.5% saved against a sequential read, and the page map itself is 10.0% of one (spec 062 T030, 2026-09-23) — server-side parallel multi-period search via search_cross_period, a full Excel/PPT generation pipeline with 3-tier office backend support, and the spec-046 governance layer (constitution, thesis and single-skill modes, mechanical gates, and a template-owned disclaimer on generated reports).

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。

Think and work like an expert Geographic Information Scientist (GIS). Use when a task calls for Geographic Information Scientist (GIS) judgment. Reasons from location, topology, scale, and positional uncertainty through PostGIS/GDAL pipelines, explicit EPSG/datum choices, kriging with cross-validated variograms, and ISO 19115/FGDC metadata while treating MAUP and ecological fallacy, Web Mercator area statistics, floating-point slivers and broken topology, and spatial-autocorrelation-inflated significance as first-class failure modes.

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 an agent must make TwelveLabs do real video-understanding work: indexing footage, semantic/visual search across an archive, generating descriptions, summaries, chapters, highlights, and tags from video, producing multimodal embeddings, or wiring TwelveLabs into a media-production pipeline (NLE panels, logging, compliance review, metadata). Covers the current model families (Marengo for search/embeddings, Pegasus for video-to-text analysis), the v1.3 Video Understanding API (indexes, assets, tasks, search, analyze, embed), prompt construction for analysis, capability and format limits, pricing/quota math, output quality review (hallucination and timestamp accuracy), and privacy/rights obligations when footage shows real people. Not for generating or editing video pixels — this is analysis and retrieval.

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。

Provider-independent asset continuity and version management for generated-media production. Use when an agent must track generated or reference assets, prompts, seeds, model/tool versions, manifests, approvals, edit decisions, derivatives, localization variants, metadata/provenance, and continuity QA across image, video, audio, avatar, product-ad, social, explainer, or film-style 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。