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 hidai25 · MCP Server · ★ 133
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is eval-view safe to install? View the security audit →
The open-source behavior regression gate for AI agents. Think Playwright, but for tool-calling and multi-turn AI agents. Your agent can still return and be wrong. A model or provider update can change tool choice, skip a clarification, or degrade output quality without changing your code or breaking
| Stars | 133 |
| Forks | 24 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 69.1495178002746/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-05 |
| Created | 2025-11-17 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~24k |
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eval-view is Regression testing for AI agents. Snapshot behavior,diff tool calls,catch regressions in CI. Works with LangGraph, CrewAI, OpenAI, Anthropic.. It is categorized as a MCP Server with 133 GitHub stars.
eval-view is primarily written in Python. It covers topics such as agent-benchmark, agent-evaluation, agentic-ai.
You can find installation instructions and usage details in the eval-view GitHub repository at github.com/hidai25/eval-view. The project has 133 stars and 24 forks, indicating an active community.
eval-view is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to eval-view on Agent Skills Hub include oreilly-ai-agents, orchestkit, intellegix-code-agent-toolkit. 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: