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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.

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

帮助配置WireGuard服务器、生成各设备密钥、设置通信路由并排查连接故障，让外出的手机或笔记本电脑能够连接家庭网络。

• 在外访问家中的NAS
• 搭建WireGuard服务器
• 选择通过VPN的流量

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.

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

Manage AdGuard Home network filtering from the command line. Update blocklists, register devices, configure DNS rules that map names to addresses, and inspect query logs and statistics through the AdGuard Home REST API.

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• Adding and refreshing ad-blocking filter lists
• Mapping local domain names to IP addresses
• Registering devices and managing fixed address assignments

Build automated responses to AWS GuardDuty security findings using EventBridge and Lambda. Configure severity filters, isolate EC2 instances, disable compromised access keys, preserve disk snapshots for investigation, and send notifications to security teams.

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• Triggering responses to high-severity GuardDuty findings
• Isolating EC2 instances and preserving investigation data
• Disabling compromised IAM access keys

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.

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

在AI代理执行危险命令前进行检查，并将文件编辑限制在指定目录内。可单独启用或组合使用保护模式，管理生产作业、自主运行、迁移和部署时的操作。

• 检查生产作业中的危险命令
• 将编辑范围限制在指定目录
• 为自主运行组合启用保护

Detect unauthorized cryptocurrency mining in cloud workloads by combining billing changes, CPU usage, mining pool connections, and process alerts. Follow workflows to preserve evidence, isolate affected resources, and trace how attackers gained access.

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• Investigating sudden cloud billing increases
• Responding to GuardDuty cryptocurrency alerts
• Checking containers for unauthorized mining processes

Set up and operate Amazon GuardDuty to detect threats in AWS. Configure protection features, centralize findings across accounts, investigate suspicious activity, and build automated responses with EventBridge and Lambda, including containment of potentially compromised resources.

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• Setting up threat detection for AWS accounts
• Investigating credential abuse and compromised instances
• Automating responses to high-severity findings

Build a screening pipeline for web pages, PDFs, and images before an AI agent reads them. Extract hidden text, check for malicious instructions with detection models, and record decisions to block or allow content.

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• Screening external content before an agent reads it
• Checking uploaded PDFs and images for hidden instructions
• Scanning documents before adding them to a knowledge base

Deploy Llama Guard, NeMo Guardrails, and LLM Guard input/output scanners as runtime defenses.

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Investigate suspected data theft from AWS S3 by comparing access logs, GuardDuty findings, and Macie classifications. Identify unusual bulk downloads and transfers across accounts, assess sensitive data exposure, and build automated detection rules.

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• Investigating unusual bulk downloads from S3
• Checking transfers to external AWS accounts
• Assessing exposure of sensitive data

Configure safety checks for LLM applications with NeMo Guardrails. Covers attempts to bypass safety rules, input and output validation, masking personal information, and checking factual claims through programmable rules and integrations.

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• Detecting instructions that try to bypass safety rules
• Masking personal information before processing
• Adding factual checks to generated answers

Add safety checks to LLM applications with NeMo Guardrails. This skill guides input and output validation, detection of attempts to bypass safety rules, personal information masking, and checks for unsupported or inaccurate answers.

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• Detect attempts to bypass an AI app's safety rules
• Mask personal information before processing
• Add fact-checking to generated answers

Implements input and output validation guardrails for LLM-powered applications to prevent prompt injection, data leakage, toxic content generation, and hallucinated outputs. Builds a security validation pipeline using NVIDIA NeMo Guardrails Colang definitions, custom Python validators for PII Tespit and content policy enforcement, and the Guardrails AI framework for structured output validation. The guardrails system intercepts both user inputs (blocking injection attempts, stripping PII, ...

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Inspect npm packages and dependency files for malicious installation scripts, credential theft, and self-spreading behavior. Use GuardDog scanning, source inspection, known-malicious version checks, and isolated execution to produce an evidence-based assessment.

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• Vetting a new npm dependency before adoption
• Reviewing dependency files during code review
• Checking lockfiles against known-malicious versions

Submit security scans for AI services, tools, skills, and agents through AI-Infra-Guard, then retrieve progress and results. It also supports jailbreak evaluation, testing whether prompts can bypass a model’s safety restrictions.

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• Scanning AI services for known vulnerabilities
• Auditing MCP tools and skill source packages
• Checking agents from saved settings or YAML files

配置Claude工具执行前的检查钩子，暂时拒绝首次编辑或创建文件，要求先核实调用方、数据结构和用户请求，将修改前调查纳入流程，并提示检查破坏性命令。

• 编辑前调查受影响的模块
• 修改前确认数据结构
• 设置排除测试和生成文件

在更改家庭或小型实验室网络前，梳理设备配置与恢复步骤，分阶段规划和检查VLAN设备隔离、DNS过滤及VPN远程访问。

• 将日常设备与物联网、访客设备隔离
• 规划向DNS过滤的分阶段迁移
• 检查VPN远程访问方案

Set up encrypted connections between devices with Tailscale and control access by user or device. The skill covers network routing, device name resolution, and self-hosting the coordination server with Headscale.

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• Replacing a traditional VPN with Tailscale
• Restricting server access by team or device
• Connecting remotely to internal networks

Inspect suspicious Android APK files without running them. Analyze permissions, app components, suspicious code patterns, and network indicators, then produce a JSON risk report to support malware triage and detection rule development.

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• Triage suspicious APK files without running them
• Inspect dangerous permission combinations and suspicious API calls
• Extract network indicators for malware detection rules

Helps configure and troubleshoot Linux networks, including firewalls, routing, and DNS, which translates domain names into addresses. Covers traffic capture, isolated test networks, WireGuard VPN setup, and TCP tuning across physical servers, virtual machines, and containers.

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• Investigating connectivity and DNS failures
• Reviewing firewall rules and port forwarding
• Configuring routes based on traffic source

Analyze and enforce repository, runtime, data, spend, endpoint, and approval guardrails without guessing secret formats or content-policy rules. Use when the task requires adobe policy and execution guardrails. Trigger with "Adobe guardrails", "block unsafe Adobe calls", or "Adobe policy checks".

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How AgentGuards enforces its guardrails in Codex — fully automatic, via hooks, with nothing for you to call. Load this if asked how AgentGuards works, whether you need to screen something yourself, or why no agentguards check_input / authorize_action tools are available.

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