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1 pack

Results for “content-safety”

13 skills
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microsoft
azure-aigateway
Configure Azure API Management as an AI Gateway to govern AI models, MCP tools, and agents with policies for caching, rate limiting, content safety, and cost control.
2.7k · bundle
chimeranext
container-security
Implements container security with image scanning, runtime protection, image signing, and security policies using tools like Falco, Trivy, and Notary.
4 · bundle
orchestra-research
llamaguard
Deploy Meta's LlamaGuard moderation model to filter LLM inputs and outputs across 6 safety categories using HuggingFace, vLLM, or FastAPI.
10.4k
leandrobenjaminl
security-auditor
Automated security auditing covering SAST, DAST, dependency scanning, secret detection, container hardening, and compliance checks before deployments or when integrating new dependencies.
0
mukul975
securing-container-registry-images
Scan container images for vulnerabilities with Trivy and Grype, generate SBOMs, sign images with Cosign and Sigstore, configure registry access controls, and enforce security gates in CI/CD pipelines.
24.6k · bundle
mesteriis
security-diff-review
Reviews authorized diffs for auth, input, filesystem, network, secrets, parsers, injection, CI/CD, and supply-chain regressions.
0 · bundle
nickgallick
skill-security-auditor-v2
Hybrid security auditor for OpenClaw skills, Claude/Codex skills, and app repos. Use when installing a new skill, auditing a repo before use or deploy, reviewing custom scripts, checking for prompt injection, command execution, data exfiltration, dependency risk, secrets exposure, or privilege escalation. Use as the default gatekeeper before installing any third-party skill.
0 · bundle
mukul975
defending-llms-with-guardrails
Deploy Llama Guard, NeMo Guardrails, and LLM Guard as runtime input/output scanners to block jailbreaks, prompt injection, and toxic content in production LLM applications.
24.6k · bundle
tianhao909
llamaguard
Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.
1
qcmuu
llamaguard
Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.
0