No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by SalesforceAIResearch · Agent Tool · ★ 76
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is WALT safe to install? View the security audit →
WALT: Web Agents that Learn Tools Web Agents that Learn Tools - Automatic tool discovery from websites for LLM agents WALT enables LLM agents to automatically discover and learn reusable tools from any website. Point WALT at a website, and it will explore, understand, and generate ready-to-use tool definitions. 🚀 Quick Start Installation Basic Usage bash Run agent with tools walt agent "find and return the URL of the cheapest blue kayak" \ --tools walt-tools/classifieds/ \ --start-url http://localhost:9980 Discover new tools from any website walt discover --url https://example.com Or generate a specific tool (faster!) walt generate --url https://zillow.com --goal "Search for homes with filters" List available tools walt list walt-tools/shopping/ Start an MCP server walt serve walt-
| Stars | 76 |
| Forks | 12 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 66.0808122910945/100 |
| Open Issues | 2 |
| Last Updated | 2026-06-02 |
| Created | 2025-10-14 |
| Platforms | browser, python |
| Est. Tokens | ~531k |
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WALT is Code for WALT – Web Agents that Learn Tools. It is categorized as a Agent Tool with 76 GitHub stars.
WALT is primarily written in Python.
You can find installation instructions and usage details in the WALT GitHub repository at github.com/SalesforceAIResearch/WALT. The project has 76 stars and 12 forks, indicating an active community.
WALT is released under the MIT license, making it free to use and modify according to the license terms.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: