'classification' 검색 결과

Build JavaScript or TypeScript applications that run text, image, and audio models in browsers or server runtimes. Guides compatible model selection, GPU or CPU execution, caching, and memory cleanup without requiring a Python server.

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• Add text classification or translation to a browser app
• Implement image classification and object detection
• Transcribe audio using JavaScript

Build image classification and search workflows by comparing images with text descriptions. Covers classification without additional training, similarity scoring, batch processing, and storing image representations in a vector database for retrieval.

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• Classifying images without additional training
• Searching image collections with text descriptions
• Comparing images with candidate descriptions

Supports preparation for EU medical device regulation reviews by organizing device classification, technical documentation, clinical evidence, and post-market monitoring. Includes a tool to identify documentation and compliance gaps and prioritize the next steps.

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• Documenting a device classification rationale under MDR
• Checking technical documentation before a review
• Planning clinical evaluation and identifying evidence gaps

Review a proposed working arrangement before work begins. Apply researched legal tests for the relevant location and purpose, compare employee, independent contractor, agency temp, and vendor structures, and identify gaps between the intended arrangement and the facts.

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• Reviewing a contractor arrangement before work begins
• Comparing agency temp and vendor structures
• Checking classification across legal purposes

Develops data classification policies with tiered handling (public, internal, confidential, restricted), labeling requirements, enforcement mechanisms, and procedures per tier. Covers policy governance, exception handling, and compliance monitoring. Keywords: classification policy, data tiers, handling procedures, labeling, enforcement, data governance, information security.

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River Review のメインエントリポイント。 レビュー依頼の intent classification → 専門 skill 選択 → 実行 → finding verification → feedback classification → fixture / reference / suppression への還元までを束ねる improvement-loop orchestrator。

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Build graph neural networks, models that learn from connected nodes, with PyTorch Geometric. Supports node and graph classification, link prediction, custom datasets, and training approaches for large graphs or graphs containing multiple types of nodes and connections.

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• Build node and graph classification models
• Predict links between nodes
• Train models on graphs with multiple node and connection types

Build Python machine learning workflows for classification, numerical prediction, and grouping similar data. Combine preprocessing with training, compare algorithms, tune model settings, and evaluate performance using data splits suited to the task.

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• Building classification or numerical prediction models
• Grouping similar records and visualizing the results
• Combining missing-value handling with model training

Guides image classification and search with OpenAI's CLIP by comparing images with text descriptions, without additional training. Includes batch processing, image-text matching, and category-based checks for potentially inappropriate images.

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• Classify images using text labels without additional training
• Search an image collection with natural-language descriptions
• Compare how closely images match text descriptions

Fine-tune vision models on custom images using Hugging Face Jobs cloud GPUs. Covers object detection, image classification, and segmentation—identifying image regions—with dataset validation, hardware selection, cost estimates, monitoring, and saving trained models to the Hub.

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• Train an object detector on custom images
• Fine-tune an image classifier
• Adapt SAM/SAM2 to custom image masks

Taxonomic classification of metagenomic reads using Kraken2. Fast k-mer based classification against RefSeq database. Use when performing initial taxonomic classification of shotgun metagenomic reads before abundance estimation with Bracken.

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Prioritizes rare-disease variants from trio/quad WES/WGS with de novo (DeNovoGear, Triodenovo), compound-heterozygous phasing (WhatsHap), mosaic VAF tiering, phenotype-driven ranking (Exomiser, Phen2Gene, AMELIE), ClinGen gene-disease validity gating, and ACMG SF v3.2 secondary findings reporting. Use when running diagnostic exome / genome pipelines, identifying candidate Mendelian disease genes, screening for incidental findings, or auditing VUS reclassification cycles. The ACMG/AMP classification framework (PVS1 decision tree, Pejaver PP3/BP4 calibration, Tavtigian point system) is in clinical-databases/acmg-classification.

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EU AI Act Knowledge Engine — authoritative regulatory Q&A grounded in 70 official EU source documents (including the 2026 Commission draft guidelines on Art. 6 high-risk classification — general principles, Annex I, Annex III). Answers any question about the AI Act with article-level citations from the full regulation text, Commission guidelines, EDPB/EDPS opinions, codes of practice, harmonised standards, FRIA guides, incident templates, and sector-specific guidance. This skill should be used when the user asks about the "EU AI Act", "AI Act regulation", "Regulation 2024/1689", "KI-Verordnung", asks to "explain an AI Act article", "what does Article X say", "AI Act requirements", "AI Act penalties", "AI Act timeline", "GPAI obligations", "high-risk AI", "prohibited AI practices", "AI Act and GDPR", "fundamental rights impact assessment", "AI Act standards", "AI Act national implementation", "AI literacy", "serious incident reporting", "Digital Omnibus", "AI regulatory sandbox", or any question about EU artificial intelligence regulation. Also triggers on German terms: "KI-Gesetz", "KI-Verordnung", "Hochrisiko-KI", "KI-Kompetenz", "Grundrechte-Folgenabschaetzung", "GPAI-Verhaltenskodex". For assessment workflows that produce a classification decision rather than a knowledge answer, offer to run a structured risk-tier classification in-session.

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Harmonises data classification across jurisdictions mapping GDPR special categories vs CCPA sensitive PI (1798.140(ae)) vs HIPAA PHI (160.103) vs LGPD sensitive data (Art. 5-II). Provides cross-regulation mapping matrix for multinational compliance. Keywords: cross-jurisdiction, GDPR, CCPA, HIPAA, LGPD, sensitive data, multinational, classification mapping.

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Implements technical breach detection capabilities including SIEM integration, DLP alert configuration, anomaly detection rules, and insider threat monitoring. Provides a breach classification taxonomy across confidentiality, integrity, and availability dimensions. Covers detection tool selection, alert tuning, and integration with privacy incident response workflows. Keywords: breach detection, SIEM, DLP, anomaly detection, insider threat, classification.

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Classify an image and describe its contents with a local vision LLM. Wraps kuzco's tool_llm_image_classification() ellmer tool. Use when the task needs image classification, object identification, or a structured description of what an image contains.

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Codified expertise for customs documentation, tariff classification, duty optimization, restricted party screening, and regulatory compliance across multiple jurisdictions. Informed by trade compliance specialists with 15+ years experience. Includes HS classification logic, Incoterms application, FTA utilization, and penalty mitigation. Use when handling customs clearance, tariff classification, trade compliance, import/export documentation, or duty optimization.

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Build text and data classification systems with training and evaluation. TRIGGERS - Use when user needs help with ai-classification-system related tasks.

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Classify a proposed code change as Leaf Node or Core Code to decide how much AI usage and review rigor is appropriate. Use this skill whenever the user asks "is this leaf or core", "what kind of change is this", "should I vibe code this", "can AI write this", "how rigorous should the review be", or before starting any non-trivial change. Also useful as a sub-step inside other workflows (planning, PR prep, code review) when classification matters. Asks 6 diagnostic questions about propagation range, change frequency, tech-debt tolerance, examples, failure cost, and review intensity, then outputs a clear classification with the recommended approach. Trigger even when the user describes a change without explicitly asking for classification — the answer changes how they should proceed.

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Codified expertise for customs documentation, tariff classification, duty optimization, restricted party screening, and regulatory compliance across multiple jurisdictions. Informed by trade compliance specialists with 15+ years experience. Includes HS classification logic, Incoterms application, FTA utilization, and penalty mitigation. Use when handling customs clearance, tariff classification, trade compliance, import/export documentation, or duty optimization.

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Semantic image-text matching with CLIP and alternatives. Use for image search, zero-shot classification, similarity matching. NOT for counting objects, fine-grained classification (celebrities, car models), spatial reasoning, or compositional queries. Activate on "CLIP", "embeddings", "image similarity", "semantic search", "zero-shot classification", "image-text matching".

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Prioritizes rare-disease variants from trio/quad WES/WGS with de novo (DeNovoGear, Triodenovo), compound-heterozygous phasing (WhatsHap), mosaic VAF tiering, phenotype-driven ranking (Exomiser, Phen2Gene, AMELIE), ClinGen gene-disease validity gating, and ACMG SF v3.2 secondary findings reporting. Use when running diagnostic exome / genome pipelines, identifying candidate Mendelian disease genes, screening for incidental findings, or auditing VUS reclassification cycles. The ACMG/AMP classification framework (PVS1 decision tree, Pejaver PP3/BP4 calibration, Tavtigian point system) is in clinical-databases/acmg-classification.

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Detects prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack classification.

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Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.

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