「content moderation」の検索結果

Build content moderation applications using the Azure AI Content Safety SDK for Java.

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Build content moderation applications with Azure AI Content Safety SDK for Java. Use when implementing text/image analysis, blocklist management, or harm detection for hate, violence, sexual conten...

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 content moderation applications using the Azure AI Content Safety SDK for Java.

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。

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

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 AI content moderation systems. TRIGGERS - Use when user needs help with ai-moderation-system related tasks.

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 AI content moderation systems. TRIGGERS - Use when user needs help with ai-content-moderator related tasks.

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。

Windows アプリ開発 (Windows AI Foundry) の AI/ML 統合リファレンス。 Windows AI APIs、LanguageModel, TextRecognizer, ImageScaler, ImageObjectExtractor, ImageObjectRemover, ImageDescriptionGenerator, SemanticSearch, ContentModeration, Phi Silica LoRA、Windows ML、ONNX Runtime、execution providers、LearningModel、 Foundry Local、CLI、REST API、SDK、model catalog、MCP サーバー、App Actions、 IActionProvider、Agent Launchers、DirectML、IDMLDevice、operators、tensors。

Build content moderation applications using the Azure AI Content Safety SDK for Java.

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 content moderation applications using the Azure AI Content Safety SDK for Java.

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 content moderation applications using the Azure AI Content Safety SDK for Java.

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。

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

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。

Deploys Llama Guard 3 safety classification, NeMo Guardrails programmable dialogue rails, and LLM Guard input/output scanner pipelines as complementary runtime defenses that inspect and constrain LLM prompts and responses. Use when adding a production runtime safety layer to an LLM, RAG, or agent application to block jailbreaks, prompt injection (OWASP LLM01), toxic content, or sensitive-data leakage before it reaches or leaves the model.

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 reviewing, designing, or modifying Java enterprise systems that may support intermediary services, hosting services, online platforms, marketplaces, content moderation, recommender systems, advertising delivery, complaint workflows, transparency reporting, or systemic-risk evidence under the EU Digital Services Act. This should trigger for requests such as Review a Java online platform for DSA controls; Design notice-and-action or appeal workflows; Add recommender, ad transparency, moderation, audit, researcher access, or privacy-safe observability evidence; Assess online-platform transparency controls before production release. Part of Plinth Toolkit

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Implements content safety filters with PII redaction, policy constraints, prompt injection detection, and safe refusal templates. Use when adding "content moderation", "safety filters", "PII protection", or "guardrails".

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Use when generating or editing video with Seedance models on Scenario via MCP: text-to-video, image-to-video, first and last frame anchors, reference-to-video from identity, product, or world references, prompt-based editing, extending a clip, audio-conditioned motion or voice, shot sound without music, first-frame versus reference conditioning, or an output blocked by moderation. Keywords: Seedance 2.5 and 2.0, ByteDance, T2V, I2V, V2V, native audio, OutputVideoSensitiveContentDetected.

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Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector stores, setting up content moderation guardrails, managing prompts, orchestrating workflows with flows, and configuring inference profiles for model optimization.

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Use this skill when an agent needs AssemblyAI for speech-to-text or speech-understanding work in media production, including pre-recorded, synchronous short-file, and real-time streaming transcription; speaker diarization or speaker identification; captions and subtitles; timestamps; language detection, code-switching, or transcript translation; audio intelligence such as summaries, chapters, topics, entities, key phrases, sentiment, content moderation, profanity filtering, and PII redaction; webhooks, scaling, rate limits, retention, security, consent, and QA for podcasts, interviews, captions, call recordings, and edit workflows.

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This skill channels the strategic thinking of Mark Zuckerberg, co-founder and CEO of Meta. Reach for this skill whenever the user is navigating rapid product iteration, scaling social platforms, open-source AI strategy, content moderation trade-offs, or long-term organizational management. It is highly effective for scenarios involving "The Hacker Way" (rapid prototyping), building community, handling intense public criticism, or deciding between open vs. closed ecosystems. Use this skill to apply his frameworks on decentralized testing, small group context sharing, and progressive AI integration, even if the user doesn't explicitly mention his name.

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Suede-owned community-growth discipline for member purpose, platform choice, seeding, rituals, moderation, health metrics, and earned advocacy. Use when starting, auditing, or growing a Discord, Slack, forum, subreddit, or comparable product community. NOT FOR: public social-channel content (use suede-social), referral-program mechanics (use suede-referrals), or customer-interview research (use suede-customer-research).

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Design guardrail layers — input classifiers, output validators, PII scrubbers, policy rule engines. Use when asked to "add safety filters", "scrub PII from LLM output", or "design content moderation".

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Analyze the sentiment or mood conveyed by an image with a local vision LLM. Wraps kuzco's tool_llm_image_sentiment() ellmer tool. Use when the task needs the emotional tone of an image, e.g. for content moderation, marketing, or curation.

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Expert community-led growth and community management guidance. Use when building developer communities, Discord/Slack communities, online communities, managing community platforms, designing member onboarding, creating engagement programs, running ambassador programs, measuring community health, setting up moderation systems, or implementing DevRel strategies. Use for community-led growth, member activation, user-generated content programs, and community governance.

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Use this skill when designing the UX and content strategy for a Salesforce-powered self-service portal or help center — pre-deflection article surfacing, Help Center search UX, friction-calibrated case submission forms, community peer support layers, and deflection measurement. Trigger keywords: design self-service portal, case deflection strategy, help center UX design, reduce support volume with self-service, knowledge base search experience. NOT for picking which contact reasons to deflect and through which channel — use architect/case-deflection-strategy. NOT for building the Experience Cloud site itself — use admin/experience-cloud-site-setup. More trigger keywords: article visibility matrix IsVisibleInCsp IsVisibleInPkb, which articles show in the customer portal, case visibility for portal users, CaseComment IsPublished customer visible comment, self-registration profile and account for a portal, Help Center template does not support login, deflection metrics SOQL KnowledgeArticleViewStat, self-service journey map, moderation policy for a support community.

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Design the engagement model for an Experience Cloud community: reputation levels, ideation, and content contribution strategy. Triggers: 'community engagement strategy', 'gamification Experience Cloud', 'reputation levels setup', 'ideation community portal', 'member recognition program'. NOT for blocking, flagging or approving what members post — use admin/experience-cloud-moderation. NOT for measuring logins, page views or engagement after launch — use data/community-analytics-data. Also covers: the Network reputation elements that carry the ladder (enableReputation, reputationLevels/level/label/lowerThreshold, reputationPointsRules/pointsRule/eventType/points), the 16 documented reputation eventType values and their default points, ManagedTopics / ManagedTopic (Navigational vs Featured, position 0-24), NetworkMember.ReputationPoints seeding, and why FeedItem cannot carry the engagement dashboard.

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。