xLAM: A Family of Large Action Models to Empower AI Agent Systems

by SalesforceAIResearch · Agent Tool · ★ 637

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 62/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

actionstudioagentsagentstudiollm-agentllmsxlam

Quick Facts

Stars637
Forks60
LanguagePython
CategoryAgent Tool
LicenseApache-2.0
Quality Score62/100
Open Issues8
Last Updated2026-06-02
Created2024-02-27
Platformspython
Est. Tokens~2504k

About xLAM

[]() Paper | Key Features | -- Model Instruction | Framework | Installation | <a href="https://

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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