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Found 38 Skills
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for AI-agent, prompt-injection, MCP or toolchain, cloud, container, CI/CD, and supply-chain challenges. Use when the user asks to analyze prompt-to-tool flows, retrieval poisoning, mounted secrets, deployment drift, runtime-vs-manifest mismatches, registry provenance, or CI-produced artifacts under sandbox assumptions. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
Heuristic security scan of installed skills — prompt-injection phrases, hidden unicode instructions, credential-store access, network-pipe-to-shell and payload-smuggling patterns. Use when the user asks 'are my skills safe', wants to scan skills for prompt injection or malware patterns, or before trusting a newly installed skill. Trigger with '/janitor-security'.
Extract structured data via stored browser-templates or one-shot DOM queries, with mandatory AIDefence PII + prompt-injection gates before content reaches the model
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for prompt-injection, retrieval poisoning, memory contamination, planner drift, MCP or tool-boundary abuse, and agent exfiltration challenges. Use when the user asks to analyze prompt injection, retrieval poisoning, memory contamination, planner drift, tool-argument corruption, or secret exposure caused by an agent chain. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
Defense techniques against prompt injection attacks including direct injection, indirect injection, and jailbreaks - theUse when "prompt injection, jailbreak prevention, input sanitization, llm security, injection attack, security, prompt-injection, llm, owasp, jailbreak, ai-safety" mentioned.
Security guidelines for LLM applications based on OWASP Top 10 for LLM 2025. Use when building LLM apps, reviewing AI security, implementing RAG systems, or asking about LLM vulnerabilities like "prompt injection" or "check LLM security".
Comprehensive security auditor for OpenClaw skills. Checks for typosquatting, dangerous permissions, prompt injection, supply chain risks, and data exfiltration patterns — before you install anything.
Evaluate a skill against the Legal Skill Design Framework — thirteen design parameters (including trust-surface, freshness, schema validation, and conflict detection), three legal failure modes, and a three-band verdict (Ready / Some Concern / Material Concerns). Use when deciding whether to trust a community skill before installing it, before deploying a first-party skill to your team, or whenever the user asks "should I trust this?" or "is this skill well-designed?". Runs automatically as part of /legal-builder-hub:skill-installer.
Use this skill when the user wants to audit Agent Skills, SKILL.md files, imported skills, prompts, tools, scripts, or skill repositories for safety, prompt injection risk, secret leakage, unsafe commands, unclear permissions, untrusted external references, or repo policy violations. Trigger phrases include "audit this skill," "skill security," "review imported skills," "prompt injection risk," "unsafe skill," "scan skills," and "security audit for skills."
Compliance expert for snyk-agent-scan — the agent skill file scanner — NOT for other Snyk CLI tools (snyk test, snyk code SAST, snyk iac, snyk container). Fixes alerts through content restructuring, never by suppressing or deleting information. Covers every file in a skill directory: SKILL.md, references/, assets/, and any secondary markdown. Apply when authoring a new skill, editing an existing one, triaging a failed snyk-agent-scan run locally or in CI, or unblocking a PR held by agent scanner failures. Not applicable to dependency vulnerabilities, code security findings, or infrastructure misconfigurations — those are out of scope.
Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.
Security patterns for LLM integrations including prompt injection defense and hallucination prevention. Use when implementing context separation, validating LLM outputs, or protecting against prompt injection attacks.