YARAHQ / yara-rule-skill

What is yara-rule-skill?

This Python-based LLM agent skill turns your AI assistant into a YARA rule expert for malware detection, letting you write, review, optimize, and validate rules through natural language chats. Paste a rule and ask it to check performance bottlenecks, naming conventions, or logic errors—it pulls from proven guides on YARA style, performance, and 20+ quality checks. Install it as a skill file for agent mode llm setups like OpenClaw or MCP-based frameworks, with options to clone from GitHub or package for quick llm github integration.

Why is it gaining traction?

It stands out as an llm agent tool embedding real-world YARA best practices, skipping generic LLM hallucinations for targeted feedback on atom quality, condition ordering, and false positive filters. Developers grab it for the seamless fit into llm agent architecture—activates on YARA mentions, handles public rule assessments from github llm search, and includes a CLI validator for quick syntax checks. In a sea of general llm github projects, this niche llm agentic ai delivers actionable authoring insights without setup hassle.

Who should use this?

Threat hunters and malware analysts crafting YARA rules for scans or SIEM tools, especially those pairing llm agents langchain-style with security workflows. Security engineers reviewing community rules from github llm-resources or optimizing slow detectors in production. YARA newcomers wanting guided rule building without deep-diving docs.

Verdict

Worth a spin for YARA-heavy workflows in llm agent frameworks—solid docs and expert knowledge make it practical despite 19 stars and 1.0% credibility score signaling early maturity. Test it locally via github llm download if you're in agent authoring; skip if you need battle-tested scale.

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