AI Safety Guardrails
AI and machine learning skill, available on Zeplik
AI Safety Guardrails is a ready-to-run AI and machine learning skill on Zeplik. Safety and moderation for LLM apps — Constitutional AI, Llama Guard input/output moderation, and NeMo Guardrails runtime rails. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
The AI Safety Guardrails skill loads automatically when your request matches it, or you can invoke it directly by typing /ai-safety-guardrails in any chat. It works with attachments, connectors, and any model that supports the task, so you get the same expert method every time without setting anything up.
What the AI Safety Guardrails skill can do
- Design input and output moderation for LLM applications
- Configure Llama Guard for filtering unsafe prompts and responses
- Set up NeMo Guardrails runtime rails for topic and jailbreak control
- Apply Constitutional AI principles to reduce harmful model outputs
Try these prompts on Zeplik
Pick a prompt to open it in the Zeplik app. If you are not signed in yet, your prompt is waiting for you the moment you do.
How the AI Safety Guardrails skill works
/ai-safety-guardrails
Umbrella for LLM safety guardrails. The user wants to constrain or moderate an LLM app; establish what must be blocked (unsafe input, unsafe output, off-topic, jailbreaks) and deliver guardrail config or code. For measuring safety route to llm-evaluation-harnesses.
Dispatch table
Pick the reference file(s) that match the request, read them, then answer. Read at most 2-3 files per turn.
| Topic | Read |
|---|---|
| Anthropic's method for training harmless AI through self-improvement. | references/constitutional-ai.md |
| Meta's 7-8B specialized moderation model for LLM input/output filtering. | references/llamaguard.md |
| NVIDIA's runtime safety framework for LLM applications. | references/nemo-guardrails.md |
How to work
- Identify which leaf topic the request maps to from the dispatch table above; establish the concrete inputs (language, dataset, framework, file format) and the goal. Ask for a missing detail rather than guessing.
- Read the matching reference file(s) before answering. Read at most 2-3 per turn.
- Deliver runnable artifacts — code, configs, specs — with a short rationale, matching the user's existing conventions when they paste code.
- Confirm any decision the source flags (versions, thresholds, tradeoffs) with the user instead of guessing.
Usage
/ai-safety-guardrails $ARGUMENTS
How to use the AI Safety Guardrails skill
Sign in to Zeplik
Create a free Zeplik account or sign in. New accounts start with free credits, so you can try the AI Safety Guardrails skill right away.
Describe your AI and machine learning task
Ask in plain language, or type /ai-safety-guardrails to invoke the skill directly. Zeplik recognizes the AI Safety Guardrails skill and applies its method.
Review and refine the result
Zeplik returns a clear, structured answer. Ask follow-ups in the same chat to refine it or take the next step.
Source and credit
- Author
- davila7 (D7 umbrella-consolidation)
- License
- MIT
Adapted from the open-source davila7/claude-code-templates project and tuned to run natively on Zeplik. View source on GitHub.
Frequently asked questions
- What is the AI Safety Guardrails skill?
- AI Safety Guardrails is a ready-to-run AI and machine learning skill on Zeplik. Safety and moderation for LLM apps — Constitutional AI, Llama Guard input/output moderation, and NeMo Guardrails runtime rails. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
- How do I use AI Safety Guardrails on Zeplik?
- Sign in to Zeplik and ask in plain language, or type /ai-safety-guardrails in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.
- Which AI model does the AI Safety Guardrails skill use?
- Any model you choose. Zeplik works across every model in one chat, so the AI Safety Guardrails skill runs on your preferred model for the task.
- Where does the AI Safety Guardrails skill come from?
- The AI Safety Guardrails skill is adapted from the open-source davila7/claude-code-templates project (MIT) and tuned to run natively on Zeplik. The original source is linked on this page.
- How much does the AI Safety Guardrails skill cost?
- Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.
Related ai and machine learning skills
- Agent MemoryMemory and context management for LLM agents — short/long-term memory stores, conversation persistence, context-window strategies (summarization, trimming, retrieval) and prompt caching. Use for "give my agent memory / manage context"; for vector stores see vector-databases.
- Autonomous Agent PatternsFramework-agnostic design patterns for autonomous coding agents — goal decomposition, planning, parallel/multi-agent orchestration and operational modes. Use for "how should I architect an autonomous agent"; for concrete frameworks see ai-agent-frameworks.
- Gemini CLIRun Gemini CLI (Gemini 3 Pro) for code/plan review and huge >200k-token context analysis in the terminal
- InterpretabilityMechanistic interpretability of neural networks — TransformerLens, NNsight remote access, sparse autoencoders (SAELens), and causal interventions (pyvene). Use for "probe/intervene on model internals" or "train an SAE"; for architecture basics see llm-architectures.
- LLM App PatternsProduction patterns for LLM applications — RAG architecture, embeddings, LLMOps, and end-to-end app design. Use for "architect a production LLM/RAG app"; for the vector store see vector-databases, for agents see ai-agent-frameworks.
- LLM ArchitecturesTransformer and alternative model architectures — attention/transformers, long-context (RoPE, YaRN, ALiBi), Mamba/state-space, RWKV, and nanoGPT-scale implementations. Use for "understand/implement a model architecture"; for training it see llm-training-frameworks.
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