「classification」の検索結果

Determines whether a technology qualifies as an AI system under Art. 3(1) of the EU AI Act and classifies its risk tier (prohibited, high-risk, GPAI with systemic risk, limited risk, minimal risk). This skill should be used when the user asks to "classify an AI system under the AI Act", "determine the AI Act risk tier", "check if something is an AI system", "assess prohibited practices", "check high-risk classification", "determine Art. 6 exception applicability", or mentions "KI-Verordnung", "Risikoklassifizierung", Art. 5, Annex III, or GPAI systemic risk.

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。

EU AI Act (Regulation (EU) 2024/1689) compliance advisor — risk classification across all four tiers, all 9 prohibited practices (Art. 5, including the nudification/CSAM prohibition from Dec 2, 2026), all 8 Annex III high-risk use case areas, provider and deployer obligations (Arts. 9–17, 26), GPAI model obligations including the July 2025 Code of Practice (Arts. 51–55), conformity assessment and CE marking (Arts. 43–48), EU AI database registration, Art. 50 transparency (chatbots, synthetic media, AI-generated content), governance (AI Office, AI Board), penalties (Art. 99), confirmed phase-in timeline (Digital Omnibus adopted June 29, 2026: Annex III deferred to Dec 2, 2027; Annex I to Aug 2, 2028), and cross-framework mapping to ISO 42001, NIST AI RMF, and GDPR. Use for any EU AI regulation, AI system classification, or AI compliance question. Current as of July 2026. GPAI enforcement powers activate August 2, 2026.

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。

Fast 15-25 minute triage for preliminary EU AI Act classification and compliance assessment. This skill should be used when the user asks to "do a quick AI Act assessment", "check if the AI Act applies to us", "run a preliminary classification", "do an AI Act triage", "quick check", "preliminary assessment", "Schnellprüfung", "Ersteinschätzung", or needs a fast initial assessment before committing to full 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。

Export Administration Regulations (EAR, 15 CFR Parts 730-774) compliance advisor — ECCN classification across all 10 CCL categories and 5 product groups (A-E), EAR99 determination, jurisdiction analysis (EAR vs ITAR order of review), license requirement analysis via Country Chart, all license exceptions (LVS, GBS, CIV, TMP, RPL, GOV, TSU, ENC, TSR, APP, BAG, AVS, ACE), end-user/end-use controls (Entity List, Denied Persons List, Unverified List, MEU List), deemed export rules, Foreign Direct Product Rule (FDPR), de minimis thresholds, 10 General Prohibitions, SNAP-R license applications, voluntary self-disclosure, civil/criminal penalties, Export Compliance Program (ECP) design, and EAR vs ITAR jurisdiction determination. Use for any dual-use export control, CCL classification, or BIS compliance question.

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。

U.S. customs and trade-law research assistant for HTS classification, CROSS ruling research, CIT/CAFC decision briefing, duty compilation (General + Special + Chapter 99 + AD/CVD + MPF/HMF), country of origin determination, FTA qualification, and end-to-end import compliance review. Triggers on product classification, tariff questions, customs rulings, trade remedy screening (Section 301/232/201), Partner Government Agency admissibility, and UFLPA forced-labor analysis. Produces attorney-reviewable draft work product with an enforced HTSUS authority hierarchy and explicit evidence ledger.

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 a Montessori three-period lesson for introducing concepts through concrete materials and naming. Use when teaching vocabulary, classifications, or concepts through hands-on materials.

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。

A post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure with semi-supervised learning algorithm AdaSampling technique. The current version of scReClassify supports Support Vector Machine and Random Forest as a base classifier.

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。

Core ML, Create ML, Vision framework, Natural Language framework, on-device ML integration. Use when user wants image classification, text analysis, object detection, sound classification, model optimization, or custom model integration. Covers Core ML vs Foundation Models decision.

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 BIM elements using AI and standard classification systems. Map elements to UniFormat, MasterFormat, OmniClass, and CWICR codes.

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。

Classifies agent tasks into 4 risk tiers (GREEN/YELLOW/RED/CRITICAL). Use when assessing action reversibility before committing to an approach.

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。

Classifies personal vs non-personal data per GDPR Art. 4(1) definition test with decision tree for borderline cases. References Breyer v Germany CJEU C-582/14 dynamic IP ruling and WP29 Opinion 4/2007. Keywords: personal data, GDPR Art 4, data classification, Breyer ruling, identifiability test, PII.

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 data classification labels and tagging systems including metadata tagging, DLP integration, automated label propagation, user-applied labels, and label inheritance rules. Covers Microsoft Purview sensitivity labels and enterprise labeling architecture. Keywords: data labeling, sensitivity labels, metadata tagging, DLP integration, label propagation, Purview, classification.

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 automated PII discovery and classification using tools like Microsoft Purview, BigID, OneTrust DataDiscovery, and AWS Macie. Covers scanning schedules, accuracy tuning, false positive management, and integration patterns. Keywords: data discovery, PII scanning, Purview, BigID, Macie, OneTrust, automated classification, data cataloging.

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 product approach into one of six types (Clone, Unbundle, Undercut, Slice, Wrapper, Innovation) based on competitive landscape. Triggers on PRD v0.2 work after competitive analysis, or when user asks "what type of product should we build?", "should we clone or innovate?", "is this a fast-follow opportunity?", "how should we position against competitors?", "clone vs undercut", "unbundle vs slice", or requests help choosing product strategy. Outputs BR- entries for product type classification and inherited GTM constraints.

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。

Hybrid fingerprint + LLM pipeline for bug classification, deduplication, and ticket generation. Normalizes CI logs, creates stable fingerprints, clusters near-duplicates, then uses LLM for severity classification and ticket writing. Includes bug reporting templates and severity/priority matrix. Use when: "bug triage," "classify bugs," "failure analysis," "auto-classify," "CI failures," "bug report," "defect template." Not for: runtime self-healing of one flaky locator — use test-reliability. Not for: designing new tests from production telemetry — use observability-driven-testing. Related: qa-metrics, qa-dashboard, ci-cd-integration, qa-project-context.

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 a Montessori three-period lesson for introducing concepts through concrete materials and naming. Use when teaching vocabulary, classifications, or concepts through hands-on materials.

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。

Query patent bibliographic (catalog) information from the Nexscope patent database by patent ID or publication number. Triggered when users mention patent bibliographic info queries, patent catalog information, patent applicant queries, patent inventor queries, patent classification codes, patent abstract retrieval, patent citation analysis, patent priority claims, patent application citations, patent examiner information, patent bibliographic data, inventor lookup, applicant lookup, patent classification, patent metadata, Nexscope, or patent citations. Even if the user does not explicitly mention "bibliographic info," this skill should be triggered whenever their need involves querying detailed metadata for a specific patent by patent ID or publication number.

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。

MTProto-over-WebSocket tunnel for Telegram: DC classification, obfuscated2 detection, TLS backend choice, and the relay loop. Use when touching Telegram DC routing, MTProto classification, or ripdpi-ws-tunnel.

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。

Advises on setting up LLM-based text classification as a validated measurement design. Covers codebook construction, learning-regime and model choice, prompt construction, validation against human coding with kappa and F1, hybrid human-LLM review, downstream measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline for repeated free text against a closed codebook. Use when the user wants to classify, code, or label text at scale. Category discovery goes to topic-modeling.

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。

Advises on setting up and validating LLM text classification: codebook, learning regime and model choice, prompts, validation against human coding (kappa, F1), hybrid review, measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline. Use to classify, code, or label text at scale; category discovery goes to topic-modeling.

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。

Probe the runtime state of a named feature flag in a CLI binary. Covers the four-pronged evidence protocol (binary strings, live invocation, on-disk state, platform cache), the four-state classification (LIVE / DARK / INDETERMINATE / UNKNOWN), gate-vs-event disambiguation, conjunction-gate handling, and skill-substitution scenarios where a flag appears DARK but the capability is delivered by other means. Use when verifying whether a documented or inferred capability has rolled out, when auditing dark-launched features, or when a prior probe's conclusions need refreshing against a new binary version.

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。

Catalog and classify materials using standard library systems. Covers descriptive cataloging, subject headings, call number assignment using Dewey Decimal and Library of Congress Classification, MARC record basics, shelf organization, and authority control for consistent access points. Use when organizing a personal, institutional, or community library from scratch, assigning call numbers and subject headings to new acquisitions, reclassifying a collection that has outgrown its original system, or establishing authority control for authors, series, or subjects.

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。

Build a customer-facing Azure Update PowerPoint from Azure Updates MCP results, including customer classification, Japan region stamps, UPDATE Points, speaker notes, and Verify-Pptx gate checks. Use when creating or updating an Azure Update / Azure アップデート customer deck, bootstrapping a new Azure Update PPTX workspace, reviewing Deploy Region support, or re-applying manifest JSON to an existing deck.

Use this skill when configuring Marketing Cloud Engagement (formerly ExactTarget): business units, user roles, sender profiles, delivery profiles, send classifications, and account-level settings. NOT for MCAE / Account Engagement (Pardot) setup — use admin/mcae-pardot-setup. NOT for designing a multi-brand parent/child BU hierarchy — use architect/multi-bu-marketing-architecture.

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。