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 DataDog · Agent Tool · ★ 103
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is datadog-saist safe to install? View the security audit →
Datadog Static AI Security Testing (SAIST) tool This project is an AI-Native SAST tool. Unlike traditional SAST tools that rely solely on parsing and analysis rules, this project uses LLM (e.g. Claude from Anthropic, GPT from OpenAI or Gemini from Google) to find vulnerabilities. This project can be used standalone on your laptop. It is available as part of the Datadog Code Security offering. Project Status This project is under development and is in preview stage. Features AI-Powered Analysis: Uses advanced AI models to detect security vulnerabilities Multiple Language Support: Analyzes code in various programming languages. Java, Python and Go are currently supported. C# support coming soon.
| Stars | 103 |
| Forks | 9 |
| Language | Go |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 65.7993822001868/100 |
| Open Issues | 20 |
| Last Updated | 2026-09-22 |
| Created | 2026-02-18 |
| Platforms | go |
| Est. Tokens | ~16k |
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datadog-saist is AI-native SAST. It is categorized as a Agent Tool with 103 GitHub stars.
datadog-saist is primarily written in Go. It covers topics such as ai, ai-tools, sast.
You can find installation instructions and usage details in the datadog-saist GitHub repository at github.com/DataDog/datadog-saist. The project has 103 stars and 9 forks, indicating an active community.
datadog-saist is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to datadog-saist on Agent Skills Hub include fossil-mcp, robloxstudio-mcp, Gito. 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: