「data flow」の検索結果

Manage the NV oOS Pro Toolkit CRM — leads, deals, companies, contacts, customers, activities, and support tickets. Covers the complete sales pipeline from lead capture through qualification, deal management, and post-conversion customer tracking with BANT/MEDDIC scoring, activity logging, and workflow automation. Use when working with CRM records, managing pipeline stages, qualifying leads, creating company profiles, logging calls/emails/meetings, or troubleshooting CRM data issues.

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Manage the NV oOS Pro Toolkit Project Management system — projects, tasks, events, sprints, task plans, task templates, and PM workflow rules. Covers complete project lifecycle (planning → active → completed), task tracking with priorities and effort estimation, sprint planning, event scheduling with attendees, and template-driven task plan generation. Use when managing projects, creating or tracking tasks, planning sprints, scheduling events, generating task plans from templates, assigning work to team members, or troubleshooting project management 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。

Ensure architecture diagrams are readable, consistent with text, and clear on scope, boundaries, and data flow.

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 a HubSpot Real-Time capture using flowctl to stream CRM data into Estuary collections. Use when setting up a HubSpot source for contacts, companies, deals, tickets, or other HubSpot resources. Use when user says "capture HubSpot", "stream from HubSpot", "HubSpot CDC", or "connect HubSpot to Estuary".

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Implements BLoC (v9.x) event-driven state management for Flutter with unidirectional data flow using the flutter_bloc package. Use when implementing Bloc/Cubit classes, mapping Events to States, consuming state with BlocBuilder/BlocListener/BlocConsumer, applying event transformers (restartable/droppable/sequential) for concurrency control, setting up BlocProvider/MultiBlocProvider DI, integrating Freezed for sealed union types and exhaustive pattern matching, or testing with blocTest. Ideal for enterprise apps requiring business logic separation, precise concurrency control (search debouncing, form deduplication), or event replay debugging.

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。

Generative UI (GenUI) for Flutter using AI models like Google Gemini, Anthropic Claude, or OpenAI via the genui package. Use this skill when building dynamic AI-driven interfaces, conversational UI flows, server-rendered screen layouts, LLM-powered dashboards, chat-based UI generation, or implementing prompt-to-UI systems. Supports AI-generated widgets, reactive data binding, ContentGenerator integration, and A2uiMessageProcessor for real-time UI updates from AI responses. Ideal for apps where UI structure is determined by AI/LLM output rather than hardcoded layouts.

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Guides test implementation following the test pyramid (75% unit, 20% integration, 5% E2E), Arrange-Act-Assert pattern, and project coverage thresholds (branches 70%, functions 80%, lines 80%, statements 80%). Covers test naming conventions, test independence, mock and stub patterns, test data management with fixtures and builders, and CI integration. Use when writing tests, reviewing test code, or setting up test infrastructure.

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Automate CircleCI tasks via Rube MCP (Composio): trigger pipelines, monitor workflows/jobs, retrieve artifacts and test metadata. Always search tools first for current schemas.

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。

Plan or review FriendlyBet architecture, implementation, schema, data flows, scoring, provider integrations, static/PWA behavior, Supabase/RLS impact, migrations, automation, and technical sequencing. Use when a task asks how to build, refactor, generalize, integrate, test, or safely ship FriendlyBet code.

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 or refresh docs/ARCHITECTURE.md from current codebase state with evidence-backed component and data-flow mapping

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Analyzes codebase structure, data flow, module relationships, and key patterns to generate and maintain a living architecture document (ARCHITECTURE.md).

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Maintain IT asset inventory and configuration management database. Track hardware, software, and cloud resources. Use when managing IT assets.

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Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.

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Deploy web apps with backend APIs, database, and file storage. Use when the user asks to deploy or publish a website or web app and wants a public URL. Uses HTTP API via curl.

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Extract product data, prices, reviews, and seller information from any e-commerce platform using Apify's E-commerce Scraping Tool.

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Generate output schemas (dataset_schema.json, output_schema.json, key_value_store_schema.json) for an Apify Actor by analyzing its source code. Use when creating or updating Actor output schemas.

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。

AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.

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Access 20+ years of global financial data: equities, options, forex, crypto, commodities, economic indicators, and 50+ technical indicators.

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。

Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls.

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Run structured AI red team exercises for jailbreak resistance, data exfiltration risk, harmful output controls, and agent tool abuse resilience.

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。

Prompt engineering regression test matrix register: baseline outputs, semantic drift thresholds, judge evaluations, and golden dataset.

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Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.

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。

Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or

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。

PostgreSQL database analysis, performance tuning, and health monitoring. You MUST read this entire skill document before executing any PostgreSQL operations — it contains mandatory workflows, safety constraints, and two-phase execution rules that prevent common errors like hallucinated column names and unsafe queries.

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。