「data flow」の検索結果

Vulnerability audit and dataflow analysis built on the neug CPG engine. file_audit (default, recommended): first builds a function-level summary graph (tier1) over the entire project, then incrementally builds detailed CPGs (tier2) on demand for chosen functions; both tiers coexist in a single neug database (unified schema, cross-layer refines edges; C/Java/Python/Rust). All capabilities are exposed as CLI tool subcommands (audit/overview/build/query/methods/paths), so an agent can drive the audit step by step via shell; the audit subcommand can also run the built-in LLM loop. The same tooling also supports a dataflow analysis mode (Workflow C): no vulnerability hunting, only variable-level dataflow tracing for functions of interest, producing dataflow_analysis.json. repo_audit: multi-round LLM/Cypher audit over a full-repo CPG. Use when the user asks to audit a codebase/project for vulnerabilities, scan a project with CPG/neug, run repo_audit/file_audit, find source-to-sink taint paths, or analyze dataflow inside a function. Supports C, Java, Python, and Rust.

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Expert guidance for Salesforce Flow implementation from design through deployment. Use this skill when building Screen Flows, Record-Triggered Flows, Schedule-Triggered Flows, or Autolaunched Flows. Covers Flow design patterns, metadata XML generation, variable/element reference validation, governor limit optimization, and sf CLI deployment. Critical for preventing reference errors (undeclared variables, invalid element references) and deployment failures. Use when creating new Flows, debugging Flow errors, optimizing Flow performance, or deploying Flows to production.

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Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces. Covers FCS 2.0/3.0/3.1/3.2 internals ($PnE linear-vs-log, $DATATYPE, $SPILLOVER vs SPILL vs $COMP, $TIMESTEP), channel/parameter metadata, the silent linearize/truncate defaults, and R (flowCore, flowWorkspace, CytoML) plus Python (FlowKit, readfcs) readers. Use when loading flow or mass cytometry data, mapping detector channels to antibodies, extracting the event matrix, choosing a reader, or bridging FCS to the scanpy/AnnData ecosystem before preprocessing.

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Implements data lineage tracking for privacy compliance including origin tracking, transformation logging, access auditing, deletion verification, and cross-system lineage graphs. Covers source-to-sink mapping, GDPR Art. 30 RoPA integration, automated lineage discovery, and breach impact scoping. Keywords: data lineage, data provenance, data flow mapping, transformation logging, deletion verification.

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。

软件架构 (含 应用架构 / 系统架构 / 分布式 后端架构 / 前后端 数据流 与 数据模型 设计 / API 边界 / 可扩展性 / 演进式架构 — 不含 硬件架构 / 纯企业架构 / 纯 DevOps / 纯安全架构) (software architecture — application & system architecture for web / mobile / distributed back-ends; designing services + data models + data flow + integration boundaries; making explicit trade-offs about scalability / coupling / consistency / evolvability; not hardware / chip architecture, not pure enterprise-architecture (TOGAF/Zachman), not pure DevOps / SRE, not pure security architecture (those are adjacent disciplines).) Master OS — automated mastery of software architecture — application & system architecture for web / mobile / distributed back-ends; designing services + data models + data flow + integration boundaries; making explicit trade-offs about scalability / coupling / consistency / evolvability; not hardware / chip architecture, not pure enterprise-architecture (TOGAF/Zachman), not pure DevOps / SRE, not pure security architecture (those are adjacent disciplines).: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on software architecture — application & system architecture for web / mobile / distributed back-ends; designing services + data models + data flow + integration boundaries; making explicit trade-offs about scalability / coupling / consistency / evolvability; not hardware / chip architecture, not pure enterprise-architecture (TOGAF/Zachman), not pure DevOps / SRE, not pure security architecture (those are adjacent disciplines). problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「软件架构」「系统设计」「system design」「应用架构」「service design」

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Use when designing deployable Salesforce configuration with Custom Metadata Types, especially when choosing between CMTs, Custom Settings, and Custom Objects, protecting packaged defaults, or exposing config to Apex, Flow, and formulas. Triggers: 'custom metadata vs custom settings', 'deployable config', 'protected custom metadata', 'feature flags in Salesforce'. NOT for hierarchical Custom Settings resolution, getInstance/getValues, or their SOQL cost — use admin/custom-metadata-types-and-settings. NOT for high-churn transactional or user-managed business records — use admin/object-creation-and-design. NOT for secrets — use integration/named-credentials-setup. More trigger keywords: __mdt, CustomMetadata .md file, customMetadata folder, getInstance, getAll, xsi:type, visibility PackageProtected, MetadataRelationship, EntityDefinition field, $CustomMetadata.

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Use this skill when designing or building CRM Analytics Data Prep recipes — including node selection, join patterns, bucket field configuration, formula expressions, and scheduling. Triggers: 'build a recipe', 'join datasets in analytics', 'bucket a measure field', 'schedule a recipe', 'data prep transformation'. NOT for dataflow JSON and its node types — use admin/analytics-dataflow-development. NOT for tuning a slow or oversized dataset — use data/analytics-dataset-optimization.

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。

Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces. Covers FCS 2.0/3.0/3.1/3.2 internals ($PnE linear-vs-log, $DATATYPE, $SPILLOVER vs SPILL vs $COMP, $TIMESTEP), channel/parameter metadata, the silent linearize/truncate defaults, and R (flowCore, flowWorkspace, CytoML) plus Python (FlowKit, readfcs) readers. Use when loading flow or mass cytometry data, mapping detector channels to antibodies, extracting the event matrix, choosing a reader, or bridging FCS to the scanpy/AnnData ecosystem before preprocessing.

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。

iOS Core Data architect for persistent storage, CloudKit sync, schema migrations, and SwiftData migration. Activate on: Core Data, NSManagedObject, CloudKit sync, Core Data migration, NSFetchedResultsController, NSPersistentContainer, SwiftData migration path. NOT for: SwiftUI state management (use swiftui-data-flow-expert), server databases (use data-pipeline-engineer), SQLite direct (use mobile-offline-sync-architect).

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。

Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design 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。

Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces. Covers FCS 2.0/3.0/3.1/3.2 internals ($PnE linear-vs-log, $DATATYPE, $SPILLOVER vs SPILL vs $COMP, $TIMESTEP), channel/parameter metadata, the silent linearize/truncate defaults, and R (flowCore, flowWorkspace, CytoML) plus Python (FlowKit, readfcs) readers. Use when loading flow or mass cytometry data, mapping detector channels to antibodies, extracting the event matrix, choosing a reader, or bridging FCS to the scanpy/AnnData ecosystem before preprocessing.

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。

Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).

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予測市場や取引エージェントの処理を、規約・データ品質・秘密情報・プライバシー・取引実行の観点で点検し、問題点と必要な対策を整理するスキル。

• 取引サービスの認証を加える前の確認
• 資産情報とAPIキーの扱いの点検
• 市場データの品質と出典表示の確認

Govern Power Automate flows and Power Apps at scale using the FlowStudio MCP cached store. Classify flows by business impact, detect orphaned resources, audit connector usage, enforce compliance standards, manage notification rules, and compute governance scores — all without Dataverse or the CoE Starter Kit. Load this skill when asked to: tag or classify flows, set business impact, assign ownership, detect orphans, audit connectors, check compliance, compute archive scores, manage notification rules, run a governance review, generate a compliance report, offboard a maker, or any task that involves writing governance metadata to flows. Requires a FlowStudio for Teams or MCP Pro+ subscription — see https://mcp.flowstudio.app

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。

Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis to identify flows with abnormal byte counts, connection durations, and periodic timing 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。

Draft a funder-compliant Data Management Plan (NSF DMP, NIH DMS Policy 2023, ERC, Horizon Europe) by composing the confidential-data and environment-capture primitives. Sections cover data description, formats/metadata, storage/backup, access/sharing, preservation/archiving, and roles. Use when user says "data management plan", "DMP", "DMSP", "NIH data sharing plan", "write the data plan for my grant", or when a grant proposal needs a data-management section. NOT a submission tool — produces a draft the user pastes into the funder portal (DMPTool, NIH ASSIST, Horizon Europe portal).

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。

Apache Airflow DAG Patterns workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Apache Airflow DAG Patterns workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Draws an activity flow / system architecture diagram as ONE self-contained, INTERACTIVE HTML+SVG page (2-pane app layout — left: diagram, default fit-to-view with zoom/pan; right: sidebar with explainer cards), styled like a real hand-drawn enterprise doc (Stripe/AWS-docs style — real brand logos for MongoDB/Odoo/n8n/etc. from a bundled Simple Icons library, neutral white boxes, sans-serif, no "AI neon" look), scroll-to-zoom, auto light/dark with a toggle button — clicking a flow (a legend chip, a line, or a step badge) highlights that flow with traveling dots along the arrows in their direction, a ping at the destination box, and a sidebar panel that slides out, while everything else dims; two-way arrows (OUT = solid blue / RETURN = dashed green) carry numbered step badges, colored by semantic meaning (red = auth checkpoint, purple = scheduled flow, amber = data pipeline), a small "hop" arc where two lines are forced to cross, dashed group regions, one explainer card per flow in the right sidebar. Use this skill ANY TIME the user wants to "draw a diagram", "draw a flow", "draw a system diagram", "redraw this so it's easier to follow", "interactive diagram", "two-way diagram", "request/response flow", "make a free-form HTML diagram" — even if they never say the word HTML. Especially good when both the request direction AND the response direction need to be shown, or when a templated tool (archify, mermaid) is too rigid. Do NOT use for data charts (bar/line/pie), and do not use when the user asks specifically for archify or 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。

Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training 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。

Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis to identify flows with abnormal byte counts, connection durations, and periodic timing 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。

Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis to identify flows with abnormal byte counts, connection durations, and periodic timing 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。

Govern Power Automate flows and Power Apps at scale using the FlowStudio MCP cached store. Classify flows by business impact, detect orphaned resources, audit connector usage, enforce compliance standards, manage notification rules, and compute governance scores — all without Dataverse or the CoE Starter Kit. Load this skill when asked to: tag or classify flows, set business impact, assign ownership, detect orphans, audit connectors, check compliance, compute archive scores, manage notification rules, run a governance review, generate a compliance report, offboard a maker, or any task that involves writing governance metadata to flows. Requires a FlowStudio for Teams or MCP Pro+ subscription — see https://mcp.flowstudio.app

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。

Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis to identify flows with abnormal byte counts, connection durations, and periodic timing 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。