672 件 ・ 関連度順
概要と使いどころ
When the user needs to prepare for SOC 2, build a compliance roadmap, assess security posture, quantify security risk, or says "we need SOC 2", "security audit", "compliance", "enterprise customer wants SOC 2", "CISO advice".
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。
Excel/spreadsheet processing for construction: estimates, schedules, tracking logs, quantity takeoffs. Formulas, formatting, 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。
Automatically generate estimates from QTO data. Apply pricing rules to BIM quantities for cost estimates.
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。
Extract quantities from BIM/CAD data for cost estimation. Group by type, level, zone. Generate QTO 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。
Generate Quantity Take-Off (QTO) reports from BIM/CAD data. Extract volumes, areas, counts by category. Group elements, apply calculation rules, and create cost estimates automatically.
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。
Analyze construction drawings to extract dimensions, annotations, symbols, and metadata. Support quantity takeoff and design review automation.
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。
Comprehensive Pandas toolkit for construction data analysis. Filter, group, aggregate BIM elements, calculate quantities, merge datasets, and generate reports from structured construction 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。
Calculate material waste factors and losses using CWICR norms. Apply waste percentages, cutting losses, and spillage factors to material quantities.
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 RVT/RFA files to Excel databases. Extract BIM element data, properties, and quantities.
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 material procurement lists from CWICR data. Calculate quantities with waste factors, group by supplier categories, and create purchase orders.
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。
Extract structured data from IFC (Industry Foundation Classes) files using IfcOpenShell. Parse BIM models, extract quantities, properties, spatial relationships, and export to various formats.
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 bills of quantities and estimates in OpenConstructionERP: search cost items, build BOQ sections, link BIM elements in bulk, validate the BOQ, and export GAEB/XLSX/JSON. Use for any estimating workflow on the platform.
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。
Technical due diligence for M&A, investment, or acquisition. Reads a target company's codebase and generates a comprehensive tech DD report with architecture assessment, tech debt quantification, scalability analysis, security posture, team capability inference, build system quality, test coverage, deployment maturity, and open source license risks. Outputs tech-dd-report.md formatted like a real investment memo with risk ratings, remediation costs, and go/no-go recommendation.
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。
Market salary research, accomplishment quantification, negotiation scripts, total compensation analysis, timing strategy.
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。
Turn sales history and current stock counts into reorder points, order quantities, and a this-week purchase list -- so a product business stops stocking out of winners and stops burying cash in losers. Handles seasonality, supplier lead times, and the dead-stock cleanup.
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。
API reference: HealthKit. Query for HKHealthStore, HKQuantitySample, workouts, health data read/write.
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。
Tạo AI Skill mới từ ý tưởng hoặc quy trình công việc. Kết hợp phỏng vấn thông minh, pattern detection, và quantitative evaluation để tạo skill chất lượng production. Dùng khi user nói "tạo skill", "biến quy trình thành skill", "make a new skill", "tự động hóa công việc này", hoặc mô tả quy trình lặp lại mà họ muốn AI tự làm. Cũng dùng khi user muốn cải thiện, test, hoặc optimize skill hiện có.
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 Cirq to build, simulate, transform, serialize, validate, and provider-package quantum circuits.
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 Baichuan2 chat inference, deployment/quantization, and fine-tuning 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。
Route AIMET install, PyTorch/ONNX quantization, GenAILab, model access, cluster/Pod, Qualcomm SDK, optimization, export, and repository tasks to focused operating guidance.
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。
Business-logic flaw hunting - workflow/state bypass, price/quantity tampering, negative/overflow values, coupon/refund abuse, mass assignment, and logic races. The top-paying bug class with no scanner coverage. 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。
Analyze cross-channel campaign data, quantify uncertainty, and propose evidence-labeled budget tests without overstating causality.
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。
Assessment of pseudonymization techniques and re-identification risk. Covers tokenization, hashing, encryption-based pseudonymization, and hybrid approaches. Includes re-identification risk scoring using the motivated intruder test, quantitative metrics (marketer, journalist, prosecutor models), and linkage attack resilience evaluation. References ENISA 2019 pseudonymization report.
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。
Conducts privacy auditing of AI models including training data extraction testing, membership inference attacks, model inversion testing, and attribute inference assessment. Uses ML Privacy Meter and related tools to quantify privacy leakage. Keywords: model audit, membership inference, privacy meter, model inversion, training data extraction.
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。