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 Cre4T3Tiv3 · Agent Tool · ★ 60
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ai-agents-reality-check safe to install? View the security audit →
Benchmarking the gap between AI agent hype and architectural reality. Mathematically rigorous evaluation framework that classifies agent implementations into three archetypes and measures the performance chasm between them. The Thesis Most systems marketed as "AI agents" are prompt-chained wrappers around LLM APIs. This benchmark quantifies the architectural difference with empirical evidence by simulating three agent archetypes under controlled conditions and measuring success rate, context retention, cost efficiency, and resilience under stress. The three archetypes:
| Stars | 60 |
| Forks | 0 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 65.3964442668571/100 |
| Open Issues | 1 |
| Last Updated | 2026-04-02 |
| Created | 2025-08-07 |
| Platforms | python |
| Est. Tokens | ~250k |
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ai-agents-reality-check is Benchmarking the gap between AI agent hype and architecture. Three agent archetypes, 73-point performance spread, stress testing, network resilience, and ensemble coordination analysis with statistica. It is categorized as a Agent Tool with 60 GitHub stars.
ai-agents-reality-check is primarily written in Python. It covers topics such as agent-architecture, agent-benchmark, agent-evaluation.
You can find installation instructions and usage details in the ai-agents-reality-check GitHub repository at github.com/Cre4T3Tiv3/ai-agents-reality-check. The project has 60 stars and 0 forks, indicating an active community.
ai-agents-reality-check is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to ai-agents-reality-check on Agent Skills Hub include eval-view, runtm, penpot-mcp. 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: