Results for “content moderation”

Use when setting up CMS workspaces, creating or publishing content types for Experience Cloud sites, managing content scheduling, or configuring audience targeting on content variants. Triggers: 'set up CMS workspace', 'publish content to Experience Cloud site', 'content scheduling community', 'audience targeting content', 'managed content vs unmanaged', 'custom content type metadata', 'CMS channel configuration'. NOT for moderating member-posted content — use admin/experience-cloud-moderation. NOT for Knowledge vs an external CMS or CMS Connect — use architect/knowledge-vs-external-cms.

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Build content moderation features in Java with Azure AI Content Safety. Analyze text and images for harmful content, retrieve severity scores by category, and manage custom blocklists, which are lists of terms to flag.

• Checking submitted text for harmful content
• Analyzing images from files or URLs
• Managing custom lists of prohibited terms

Set up or tune Experience Cloud content moderation: keyword/criteria rules, member reputation system, moderation queue. Triggers: 'set up community content moderation', 'configure reputation levels', 'flagging rules Experience Cloud', 'moderation queue setup', 'keyword blocking community', 'approve community posts before publishing'. NOT for publishing or scheduling CMS articles on the site — use admin/experience-cloud-cms-content. NOT for deciding which behaviours to reward or how to name reputation tiers — use admin/community-engagement-strategy.

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Build safety checks for LLM prompts and responses using Meta's LlamaGuard. This skill covers content classification, batch moderation, serving a moderation API, and connecting checks to NeMo Guardrails.

• Screening user prompts before sending them to an LLM
• Checking generated responses before displaying them
• Building an API for content safety checks

Azure AI Content Safety SDK for Java workflow skill. Use this skill when the user needs Build content moderation applications using the Azure AI Content Safety SDK for Java and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification. Triggers: "azure-ai-contentsafety", "ContentSafetyClient", "content moderation", "harmful content", "text analysis", "image analysis".

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Azure AI Content Safety SDK for Java workflow skill. Use this skill when the user needs Build content moderation applications using the Azure AI Content Safety SDK for Java and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification. Triggers: "azure-ai-contentsafety", "ContentSafetyClient", "content moderation", "harmful content", "text analysis", "image analysis".

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Build safety checks for LLM prompts and responses using Meta's LlamaGuard moderation model. Classify potentially harmful content, block flagged messages, serve checks through an API, and integrate them with NeMo Guardrails.

• Checking prompts before sending them to an LLM
• Filtering responses before displaying them
• Building a moderation API

Implement comprehensive safety guardrails for LLM applications including content moderation (OpenAI Moderation API), jailbreak prevention, prompt injection defense, PII detection, topic guardrails, and output validation. Essential for production AI applications handling user-generated content. Use when ", guardrails, content-moderation, prompt-injection, jailbreak-prevention, pii-detection, nemo-guardrails, openai-moderation, llama-guard, safety" mentioned.

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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.

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

Systematically bypass LLM safety filters and content moderation systems using advanced jailbreaking techniques. Use this skill when testing AI systems for safety alignment robustness, evaluating content moderation effectiveness, or conducting authorized AI red team assessments. Covers role-play attacks, few-shot manipulation, encoding bypass, multi-modal exploitation, and automated jailbreak discovery methods.

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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.

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

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 content, and self-harm.

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Content moderation API integration using OpenAI Moderation, Perspective API, and others

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Use when a Scenario generation is blocked, refused, or returns a moderation or sensitive-content error, when a video is rejected after rendering or its audio track is flagged, when an output is flagged for likeness to a real person or for IP and copyright detection, when the same prompt passes on one model and fails on another, or when a team's own characters, weapons, or props get flagged. Keywords: blocked prompt, content moderation, refused generation, provider filter, false positive.

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Detect AI-generated text using rule-based analysis, LLM-as-judge scoring, and optional external APIs. Use when: auditing content for AI authorship, academic integrity checks, editorial review, SEO content audits.

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Implement safety guardrails for AI systems — content filtering, prompt injection detection, output validation, bias mitigation, and responsible AI practices. Use when tasks involve adding safety layers to LLM applications, detecting prompt injection attacks, filtering harmful content, implementing rate limiting for AI APIs, validating LLM outputs against schemas, building moderation pipelines, or ensuring AI systems comply with safety policies.

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Detects absence of a content moderation service integration for apps that accept user-uploaded media or posts, enforcing Guideline 1.2's requirement for timely response to content reports.

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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 content, and self-harm.

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Add checks to LLM prompts and responses using Llama Guard 3, NeMo Guardrails, and LLM Guard. Configure content screening, conversation boundaries, and sensitive-data filtering, then measure blocking and false-positive rates with labeled test prompts.

• Adding runtime safety checks to LLM, RAG, or agent apps
• Screening prompts for injection and jailbreak attempts
• Filtering sensitive or toxic model responses

Für User Content Moderation: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.

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Fix silent video/media processing failures caused by URL extraction code that filters on file extensions (.mp4, .webm, .webp). Use when: (1) Media moderation, transcoding, or analysis silently skips files from Blossom or content-addressed storage servers, (2) URL extraction from Nostr event tags (imeta, r tags) drops URLs without recognized extensions, (3) CDN fallback URLs append .mp4 but the actual server uses extensionless content-addressed paths like /{sha256}. Common in Nostr video events (kind 34236) where different clients use different URL formats.

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

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