dialop — security grade SAFE, quality 61/100

Security audit verdict: SAFE · quality 61/100

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 jlin816 · Agent Tool · ★ 114

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

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About dialop

📠 The DialOp Environments Collaborative decision-oriented dialogue environments for humans and LLM agents. These environments are associated with the paper Decision-Oriented Dialogue For Human-AI Collaboration. [[Paper]](https://arxiv.org/abs/2305.20076) [[Website]](https://collaborative-dialogue.github.io/) Play against GPT-3 Get started and play the games with GPT-3 as an assistant: Overview : define game logic, environment data generation : text-based interfaces for games : web app interfaces for games for human-human play Play now Coordinate a set of group flights that w

agentaiartificial-intelligencedialoguegpt-3large-language-modelsllmllm-agentmulti-agentnatural-language-processing

Quick Facts

Stars114
Forks8
LanguagePython
CategoryAgent Tool
Quality Score60.8715691240481/100
Last Updated2024-11-15
Created2023-05-31
Platformspython
Est. Tokens~516k

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Frequently Asked Questions

What is dialop?

dialop is DialOp: Decision-oriented dialogue environments for collaborative language agents. It is categorized as a Agent Tool with 114 GitHub stars.

What programming language is dialop written in?

dialop is primarily written in Python. It covers topics such as agent, ai, artificial-intelligence.

How do I install or use dialop?

You can find installation instructions and usage details in the dialop GitHub repository at github.com/jlin816/dialop. The project has 114 stars and 8 forks, indicating an active community.

What are the best alternatives to dialop?

The top alternatives to dialop on Agent Skills Hub include aser, oreilly-ai-agents, awesome-llm-os. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

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:

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