AutoTools: Official Repo for WWW 2025 paper Tool Learning in the Wild

by mangopy · Agent Tool · ★ 181

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

Security audit verdict: SAFE · quality 68/100

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.

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.

agentlarge-language-models

Quick Facts

Stars181
Forks1
LanguagePython
CategoryAgent Tool
LicenseApache-2.0
Quality Score68/100
Open Issues2
Last Updated2025-04-08
Created2024-12-06
Platformspython
Est. Tokens~547k

About AutoTools

AutoTools 🔧This project (AutoTools) aims to introduce an automatic framework to enable the large language models (LLMs) as autonomous agents. By integrating the AutoTools, the LLM automatically transforms tool documentation into callable functions, verify syntax and runtime correctness, and flexibly compose these functions into executable programs to solve practical tasks.

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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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