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 OTA-Tech-AI · MCP Server · ★ 509
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is web-agent-protocol safe to install? View the security audit →
Web Agent Protocol Overview The Web Agent Protocol (WAP) is a standardized framework designed to enable seamless interaction between users, web agents, and browsers by recording and replaying browser actions. It separates the concerns of action recording and execution, allowing for efficient automation and reusability. The Python SDK for WAP implements the full specification, making it easy to: Collect user‑interaction data with the OTA‑WAP Chrome extension. Convert the raw event stream into either exact‑replay or smart‑replay act
| Stars | 509 |
| Forks | 90 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 71.1868622329876/100 |
| Open Issues | 11 |
| Last Updated | 2025-06-19 |
| Created | 2025-05-17 |
| Platforms | browser, mcp, python |
| Est. Tokens | ~364k |
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web-agent-protocol is 🌐Web Agent Protocol (WAP) - Record and replay user interactions in the browser with MCP support. It is categorized as a MCP Server with 509 GitHub stars.
web-agent-protocol is primarily written in Python. It covers topics such as ai-agents, ai-tools, browser-automation.
You can find installation instructions and usage details in the web-agent-protocol GitHub repository at github.com/OTA-Tech-AI/web-agent-protocol. The project has 509 stars and 90 forks, indicating an active community.
web-agent-protocol is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to web-agent-protocol on Agent Skills Hub include browserable, Interceptor, cheatengine-mcp-bridge. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
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: