1.6万 件 ・ 関連度順
概要と使いどころ
Extract structured data from construction documents using LLMs. Process RFIs, submittals, contracts, specifications. Convert unstructured PDFs to structured JSON/Excel.
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。
Predict construction project costs using Machine Learning. Use Linear Regression, K-Nearest Neighbors, and Random Forest models on historical project data. Train, evaluate, and deploy cost prediction models.
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。
Classify construction data by type (structured, unstructured, semi-structured). Analyze data sources and recommend appropriate storage/processing methods
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。
Reviews pitch decks and investor presentations. Reads slide content, evaluates narrative flow, problem/solution clarity, market sizing, competitive positioning, financial projections, team credibility, and ask clarity. Generates a scored pitch-review.md with slide-by-slide feedback, overall score, top improvements, investor objection predictions, and comparisons to successful decks. Use when reviewing fundraising materials, investor decks, or pitch presentations.
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。
Structured deal assessment using MEDDIC, BANT. Risk scoring, required evidence by stage, red flag detection, coaching points.
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 a contract and generates redline suggestions with replacement language. Identifies unfavorable terms, missing protections, ambiguous language, liability exposure, IP risks, termination traps, and auto-renewal gotchas. Produces a contract-review.md with clause-by-clause analysis, risk ratings, tracked changes format, and negotiation talking points. Use when the user wants redline markup, contract markup, or suggested contract edits.
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。
Formats review deliverables with consistent structure for comparable findings. Use when finalizing any review or analysis that must be shared or compared.
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。
Defines the contract for deferred-item capture across plugins. Use when building or validating a plugin's deferred-capture wrapper or adding source labels.
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。
Applies layered n-tier architecture with enforced boundaries. Use when designing moderate systems needing clear presentation, domain, and persistence layers.
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。
On-demand senior red-team lead advisor. Call at a decision point or obstacle to get wiki-grounded direction instead of hammering blindly. Dispatches a fresh RTL subagent that reads the engagement state + evidence + wiki and returns ranked directions with an explicit STOP. Use for "redteamlead", "RTL", "I'm stuck", "where do I go", "what next", "which vector", "should I keep hammering this".
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。
XSS hunting - reflected, stored, DOM-based. Marker discipline to avoid false positives. Blind-XSS beacons for stored contexts. SVG/markdown/redirect vectors. 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。
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。
Implements HIPAA Privacy Rule requirements under 45 CFR §164.500-534 for covered entities and business associates. Covers minimum necessary standard, treatment-payment-operations exceptions, directory opt-out, personal representative rules, and authorization requirements. Keywords: HIPAA Privacy Rule, PHI, minimum necessary, TPO, authorization, covered entity.
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 the establishment and management of joint controller arrangements under GDPR Article 26, including determination of joint controllership, allocation of responsibilities, and transparency obligations. Activate when two or more controllers jointly determine purposes and means of processing, or when evaluating shared data platforms. Keywords: joint controller, Article 26, shared responsibility, arrangement, joint determination.
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 implementation of GDPR Article 42-43 data protection certification mechanisms including accredited certification bodies, criteria development, and periodic review. Activate when pursuing privacy certifications, evaluating certification bodies, or developing certification criteria. Keywords: certification, Article 42, Article 43, accreditation, seal, privacy mark.
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 GDPR certification mechanism implementation per Articles 42-43 including accredited certification body selection, certification criteria per EDPB guidelines, certification scope, periodic audit requirements, seal and mark usage rules, and relationship to codes of conduct. Covers EDPB/ENISA certification framework and national accreditation. Keywords: GDPR certification, Article 42, Article 43, certification body, EDPB, seal, mark, accreditation.
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。
Provides GDPR Article 13 information at the point of direct data collection, covering all required elements under Art. 13(1)(a)-(f) and Art. 13(2)(a)-(g), layered notice design, and timing requirements. Activate for Art. 13, direct collection notice, privacy notice at collection, data collection information 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。
Manages Data Subject Access Request procedures for employee requests under Art. 15 GDPR. Covers scope of disclosable HR records, emails, CCTV footage, performance reviews, monitoring data, and training records. Implements third-party data redaction, legal professional privilege, exemptions for ongoing proceedings, and the one-month response timeline. Keywords: DSAR, subject access request, Art. 15, employee records, redaction, privilege, HR data, SAR.
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 DPIA for migrating personal data to cloud infrastructure covering controller-processor analysis under Art. 28, international transfer assessment, encryption requirements, and shared responsibility model evaluation. Activate for cloud adoption, SaaS procurement, or data centre migration projects. Keywords: cloud migration, DPIA, Art. 28, processor, encryption, shared responsibility, SaaS, IaaS, PaaS.
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。
Designs and implements privacy notices for children that comply with GDPR Articles 12-14, UK AADC Standard 4, and COPPA Section 312.4. Covers plain language, visual explanations, layered information, age-appropriate vocabulary, and interactive notice elements. Keywords: children privacy notice, transparency, plain language, visual, age-appropriate, layered notice.
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。
Managing privacy risks from AI-driven inferences about individuals including derived data classification, profiling under GDPR Art. 22, inference accuracy obligations, and controlling automated personality/behaviour predictions. Keywords: AI inference, derived data, profiling, automated predictions, GDPR.
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。
Design, audit, or implement application structured logging architecture from repository evidence. Use when a user asks whether or where to add logs, how to choose or migrate a logger, how to standardize events/fields/levels/redaction, how to add HTTP access or panic logs, or why production logs cannot answer an incident question. Do not use merely to tail platform logs or to design a full metrics, tracing, SLO, and incident-management program.
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 distinctive, production-grade web frontend interfaces with high design quality. Use when the user explicitly asks to build or substantially redesign a web component, page, or application. Only applies when repository inspection confirms that the visual surface is a browser-rendered web UI. Do not use for native or GPU-rendered graphics, shaders, games, terminal UIs, or other non-web visual systems. Ignore incidental mentions and traces.
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。
Accredible integration. Manage data, records, and automate workflows. Use when the user wants to interact with Accredible 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。