1,207 件 ・ 関連度順
概要と使いどころ
Migrates databases between providers (Postgres, MySQL, Supabase, PlanetScale, MongoDB). Reads source schema, generates migration scripts, handles data type mapping, foreign keys, indexes, triggers, stored procedures. Validates migration with row counts and checksums. Generates migration-plan.md with step-by-step execution guide, rollback procedures, estimated downtime.
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。
Merges, appends, joins, and deduplicates multiple CSV, TSV, and Excel files (including multi-sheet workbooks) with pandas - mapping mismatched column names to one schema, normalizing keys, resolving conflicting values, tracking which file each row came from, and verifying the row math. Use when the user wants to combine spreadsheets or exports, stack monthly files, consolidate contact or lead lists, VLOOKUP-style join two sheets on an ID or email, or dedupe records across sources, even if they only say "put these together" or "clean up these lists into one".
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。
Maps the full customer journey from first touch to advocacy. Generates a comprehensive customer-journey.md with all stages, touchpoints, emotions, pain points, opportunities, Mermaid diagrams, and metrics. Use when mapping customer experience, designing onboarding flows, identifying churn risks, or optimizing conversion funnels.
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。
Convert a light-only site or app to a real dark mode -- semantic token mapping, elevation-based surfaces, recalibrated shadows and imagery -- not a naive inversion. Handles Tailwind, CSS variables, and styled-components, plus the toggle, persistence, and no-flash boot.
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。
Analyzes code change impact with risk scoring and affected-node mapping. Use before merging to understand what a change touches and what lacks test coverage.
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 Gaussian-SLAM for CUDA RGB-D dense SLAM, Gaussian-splatting mapping, dataset/configuration preflight, checkpoint evaluation, reconstruction metrics, and global-map refinement.
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 geemap for Google Earth Engine interactive mapping, geospatial conversion, visualization, timelapse, and ML helper workflows.
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 GeoPandas GeoDataFrame and GeoSeries workflows, vector geospatial I/O, CRS-aware spatial operations, mapping, geocoding, and GeoPandas-specific validation.
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。
Guides CUDA-accelerated cuRobo v2 robotics workflows for robot models, differentiable kinematics, collision-aware IK and motion planning, trajectory optimization, MPC, perception mapping, and motion retargeting.
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 ArcGIS API for Python for GIS administration, spatial dataframes, feature/raster analysis, mapping, location services, geospatial deep learning, app automation, and Knowledge Graph workflows.
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。
Microsoft 365 / Entra ID attack - tenant discovery, user enumeration via OneDrive differential (2026 verified), AADSTS code reference, Smart Lockout math (hard cap 1-2 attempts/user), ROPC validation, Conditional Access mapping. Wiki-first, FIND schema output.
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。
Recover and review local human-AI conversations from Claude Code, Codex, opencode, Grok Build, and Cursor. Use when the user wants to revisit, search, extract, summarize, or compare work across Agent session stores, time ranges, projects, or named conversations. Follow the user's requested output; question clustering, problem-space mapping, artifact review, drift analysis, and insights are optional lenses.
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。
Vendor certification acceptance criteria and equivalence mapping. Covers ISO 27701, SOC 2 Privacy, APEC CBPR, EU Code of Conduct evaluation, certification scope analysis, gap supplementation requirements, and cross-framework equivalence assessment.
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。
SaaS vendor data processing inventory management. Covers shadow IT discovery, API-based data flow detection, processing purpose mapping, contract status tracking, and continuous inventory reconciliation for cloud service providers.
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。
Integrates Records of Processing Activities with privacy management platforms including OneTrust, TrustArc, Collibra, and DataGrail. Covers API-based synchronization, data mapping import, and automated RoPA population from enterprise tools. Activate for RoPA tool setup, OneTrust integration, TrustArc sync, privacy platform configuration.
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。
Implement the NIST Privacy Framework IDENTIFY function including ID.BE business environment, ID.DA data actions, ID.IM improvement, and ID.RA risk assessment subcategories. Provides control mapping, gap analysis templates, and implementation workflows for privacy risk identification.
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。
Preparation guide for ISO 31700 privacy by design for consumer goods certification. Covers the 30 requirements across design, production, and disposal phases. Includes gap assessment methodology, remediation planning, and mapping to GDPR Article 25 data protection by design obligations for consumer-facing products 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。
Integrating Global Privacy Control (GPC) signals with cookie consent platforms. Covers GPC signal detection in browsers, automatic opt-out triggering, mapping GPC to US state privacy laws, and CMP integration for CCPA, CPA, and CTDPA compliance.
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。
Configuring Google Consent Mode v2 for privacy-compliant measurement and advertising. Covers default and update commands, consent state mapping to GA4 and Google Ads, conversion modeling with cookieless pings, and EEA requirements effective March 2024.
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。
Implementation guide for Global Privacy Control (GPC) automated opt-out signal per CPRA Section 1798.135(e). Covers Sec-GPC HTTP header detection, JavaScript navigator.globalPrivacyControl API, and state-specific requirements for CA, CO, CT, MT, TX, and OR. Includes server-side detection code and compliance mapping.
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。
Guides continuous privacy compliance monitoring implementation including automated control testing, evidence collection automation, real-time compliance dashboards, alert-based remediation workflows, regulatory change integration, and deviation management. Covers GRC platform configuration, control framework mapping, and compliance-as-code approaches. Keywords: continuous compliance, automated monitoring, evidence collection, dashboard, regulatory change, compliance-as-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。
Complete guide to LINDDUN privacy threat modeling methodology covering seven threat categories: Linking, Identifying, Non-repudiation, Detecting, Data Disclosure, Unawareness, and Non-compliance. Includes DFD-based analysis, threat tree catalogs, mitigation mapping to privacy design patterns, and step-by-step process.
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。
Compares PIA/DPIA methodologies: CNIL PIA tool, ICO DPIA template, NIST Privacy Framework, and ISO 29134. Provides methodology selection criteria based on regulatory jurisdiction, organisation maturity, processing complexity, and resource availability. Covers regulatory acceptance, tool features, and cross-methodology mapping. Keywords: PIA methodology, CNIL, ICO, NIST Privacy Framework, ISO 29134, DPIA comparison, assessment.
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。
Cloud service provider privacy assessment framework. Covers ISO 27018 cloud privacy controls, CSA STAR certification, SOC 2 Type II evaluation, shared responsibility model mapping, data residency verification, and cloud-specific privacy risk analysis.
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。