「guard」の検索結果

Command-line interface for AdGuard Home - Network-wide ad blocking and DNS management via AdGuard Home REST API. Designed for AI agents and power users who need to manage filtering, DNS rewrites, clients, DHCP, and query logs without a GUI.

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。

Autonomous AI code generation safety guardrail register: static AST analysis, forbidden import filters, and zero-day vulnerability checks.

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。

Deploy and operationalize Amazon GuardDuty, covering protection plans for S3, EKS, EC2 runtime monitoring, and Lambda, interpreting finding severity, and building automated response with EventBridge and Lambda. Use when establishing threat detection for AWS accounts, investigating findings on compromised instances or credential abuse, or building automated incident-response playbooks.

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。

Detect and defend against indirect prompt injection hidden in web pages, documents, and images consumed by an agent, via content extraction (HTML/PDF/OCR), normalization, and scanning with LLM Guard's PromptInjection scanner or Hugging Face Prompt Guard 2. Use when an agent ingests untrusted external content and you need to screen it for injected instructions before the LLM processes it.

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。

NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.

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。

NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.

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。

End-to-end test the polly multi-agent coding orchestrator's critical user journeys (CUJs). Two halves — a deterministic mock-LLM driver (polly_cuj.py) that boots a throwaway local server + mock LLM and asserts the substrate (boot, bridged sys_* tool dispatch, the blast_radius / spawn_bounds / headless_subagent_purpose_guard guardrails, fan-out delegation), and a live real-CLI recipe (real claude/codex/pi, real worktrees/PRs) for polly's actual judgment. Load when developing, testing, or debugging examples/polly — its config.yaml, the claude_code/codex/pi sub-agents, the investigate/fanout/cross-review skills, or the omnigent.inner.nessie.policies guardrails — or reproducing a polly orchestration bug.

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。

A.I.G Scanner — AI security scanning for infrastructure, AI tools / skills, AI Agents, and LLM jailbreak evaluation via Tencent Zhuque Lab AI-Infra-Guard. Uses built-in exec + Python script, no plugin required. Requires AIG_BASE_URL to be configured. Triggers on: scan AI service, AI vulnerability scan, scan AI infra, check CVE, audit AI service, scan MCP, scan skills, audit AI tools, scan agent, red-team LLM, jailbreak test, 扫描AI服务, 检查AI漏洞, 扫描AI工具, 检查MCP安全, 审计Agent, 越狱测试.

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。

Generates BYO custom safety policies for NVIDIA Nemotron content-safety guardrails — Nemotron-Content-Safety-Reasoning-4B (text) and multimodal Nemotron-3-Content-Safety. Produces a Markdown policy, JSON taxonomy, and drop-in inference prompts. Maps rough words or an existing policy to V2 categories, adding custom categories or topic-following rules.

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。

Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore (including the Harness managed agent loop). Applies when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, migrating/porting/converting a Bedrock Agent (including inline agents) to an AgentCore Harness, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). Also for prompt caching, quota and throttling diagnosis, cost tracking, migrating between Claude model generations (4.5 to 4.6 to 4.7), chunking strategies, API selection (Converse vs InvokeModel), guardrail capabilities, and model selection. Also covers AgentCore Payments (x402, microtransactions, Payment Manager, Connector, Instrument, Coinbase CDP, Stripe Privy, paid endpoints, agent payments). NOT for custom model training, Rekognition, or Comprehend.

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。

Implement content policy guardrails, input/output validation, and usage governance for Claude API integrations. Trigger with phrases like "anthropic guardrails", "claude content policy", "claude input validation", "anthropic safety rules".

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。

Report AgentGuards guardrail status. Use when the user asks whether AgentGuards is active, healthy, or correctly configured in Codex.

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。

Set up and verify AgentGuards in Codex. Use when the user asks to configure AgentGuards, set their API key, or check that the guardrails are wired up correctly.

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。

Review generated or changed test code against universal testing rules before it ships. Best used reactively after an agent writes, edits, generates, or refactors tests, before presenting, committing, or merging them. Use for pytest (test_*.py, *_test.py), PHPUnit/Pest (*Test.php), Jest/Vitest (*.test.ts, *.spec.js), Go (*_test.go), files under tests/, __tests__/, or spec/, and review requests like 'write tests for X', 'add tests', 'test this', 'review these tests', or PR diffs containing tests. Can also guide test writing when explicitly invoked before the work. This skill is the quality gate that prevents AI-generated test bloat. DO NOT USE for production or implementation code review (use clean-code-guard), CI or test-runner configuration, running or debugging tests, or general architecture discussion.

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。

Review generated or changed production code before it ships, using Clean Code, SOLID, DRY, KISS, YAGNI, and LLM-specific failure-mode checks in any programming language. Best used reactively after an agent writes, edits, refactors, or fixes code, before presenting, committing, or merging the result. Use when the user asks "review this PR", "is this safe to merge?", "make this cleaner", "audit this code", "refactor this", "fix this bug", or after a coding agent produced implementation code. Can also guide writing when explicitly invoked before a risky edit. Invoke it on your own initiative the moment you finish writing, editing, or refactoring non-trivial production code, before presenting or committing — don't wait to be asked. DO NOT USE for factual/conceptual questions, CI/tooling config, git workflow, running/debugging tests, pure architecture discussion, prose writing, data analysis, or test-code review (use test-guard).

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。

Autonomous AI code generation safety guardrail register: static AST analysis, forbidden import filters, and zero-day vulnerability checks.

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。

Implements HIPAA Security Rule technical safeguards under 45 CFR §164.312 for electronic protected health information. Covers access controls with unique user identification, emergency access procedures, automatic logoff, encryption, audit controls, integrity controls, and transmission security. Keywords: HIPAA Security Rule, ePHI, access controls, encryption, audit controls, technical safeguards.

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 setting up HOL Guard, protecting local AI harnesses, reviewing Guard approvals or receipts, or scanning agent plugins, skills, MCP servers, and marketplace packages before use.

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。

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.

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。

Codify project review rules as deterministic guards — ast-grep structural rules (with TDD-style rule tests) for mechanical checks, REVIEW_GUIDELINES.md for qualitative ones, wired into the project gate / wf verify. Use when the user wants to define review rules, turn a repeated review finding into a lint/guard, or set up ast-grep in a project.

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。

Map current AI safety controls — filter inventory, coverage gaps, latency impact, incident history. Use when asked "what AI safety controls do we have", "find guardrail coverage gaps", or "map our filters".

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。

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".

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。

Expert guide for AI Safety, Governance, and Responsible AI in production — Constitutional AI enforcement, runtime guardrails (NeMo Guardrails 2.0, Llama Guard 3), bias auditing, hallucination detection, EU AI Act compliance, model cards, and content provenance / Panduan ahli untuk Keamanan AI, Tata Kelola, dan AI Bertanggung Jawab di produksi.

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。

Prevents private infrastructure details (node hostnames, internal project names, local usernames and personal emails, absolute home paths, private and VPN IP ranges) from leaking into public repositories through commits, PRs, docs or release artifacts. Use when working in a public or soon-to-be-public repo, before commits or releases, when writing deployment docs for an OSS project, or when the user asks to "set up the privacy guard", "install privacy-guard", "check for leaks", "protect this public repo". Bootstraps a pre-commit hook backed by a gitignored local denylist plus gitleaks.

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。