Validate chatbot replies for safety and relevance with OK or FIX responses.
npx skills add https://github.com/dewitt/swarm --skill output-agent
Please help me install this Agent Skill. Skill: output_agent Source: https://github.com/dewitt/swarm/tree/main/skills/output-agent Command: npx skills add https://github.com/dewitt/swarm --skill output-agent
Vetting all human-visible outputs for safety, clarity, and relevance to prevent harmful or confusing responses from being shown.
Validate a candidate reply by asking the Output Agent to return OK if it passes or FIX with a reason if it does not.
High-intent search queries and answers about installing and using this skill.
You can validate chatbot responses for safety by routing them through a preflight filter that checks for unsafe, incoherent, or off-brand content. It returns OK if the reply passes or FIX with a specific reason if it fails.
Response validation for AI chatbots is a deterministic quality-assurance check applied to human-visible outputs. You need it in customer support or content moderation workflows to guard against harmful or confusing replies before delivery.
To integrate an output filter into an existing message pipeline, apply the validation check to candidate replies within your chat workflows. The agent enforces a fixed output format of OK or FIX: [reason] to flag messages for review.
Yes, you can use this response validation tool for content moderation workflows. It deterministically vets human-visible outputs for safety, clarity, and relevance to prevent harmful or confusing responses from being shown.
The safety validation check enforces a fixed output format returning OK when a reply passes or FIX: [reason] when a violation is detected. This provides actionable feedback for routing messages in your chat workflows.
AI chatbot replies may appear incoherent or off-brand without a preflight sanity check. Applying a deterministic validation filter to human-visible outputs catches these quality violations and returns FIX: [reason] before delivery.
ricableClassify LLM inputs and outputs into six safety categories.
brainbrewlabsRoutes severity-classified content to appropriate moderation queues or hides it, notifying moderators and updating statuses.
niclasmartinsson-sysSanitize user prompts through a Model Armor template for safety compliance.
davidrrowleyDetect harmful content in text and images using Azure Content Safety APIs.
