10 Security & QA Skills for AI Coding Agents

Snyk's ToxicSkills study in February 2026 uncovered that over a third of AI agent skills from platforms like ClawHub and skills.sh contained security flaws, with 13.4 percent having critical-severity issues. This highlights the importance of integrating security and quality assurance (QA) tools directly within AI coding agents rather than relying solely on downstream continuous integration (CI) processes. The rapid development of in-loop tooling in 2026 now allows for comprehensive security measures, including static analysis, dependency and secret scanning, test generation, and prompt-injection red-teaming. Various tools, such as Claude Code Security, Semgrep, Snyk MCP, and GitHub's secret scanning, provide specific functions like software composition analysis (SCA), static application security testing (SAST), and vulnerability detection, ensuring agents can identify and rectify issues within a coding session. The emphasis is also on vetting each skill for safety, as many skills themselves can pose security risks, highlighting the need for a robust security framework and careful skill selection to prevent vulnerabilities and safeguard code repositories.

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