菜单
Audits agent skill instructions and system prompts for vulnerabilities to prompt hijacking and indirect injection. Use when designing new agent skills or before deploying agents to public environments where users provide untrusted input.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | prompt-injection-scanner |
| description | Audits agent skill instructions and system prompts for vulnerabilities to prompt hijacking and indirect injection. Use when designing new agent skills or before deploying agents to public environments where users provide untrusted input. |
The prompt-injection-scanner is a security tool specifically for the AI agent era. It identifies weak points in agent instructions where a malicious user could potentially "hijack" the agent's behavior by inserting conflicting instructions into input fields.
capabilities or instructions for an agent skill.secret-leak-detector for credentials; this is specifically for "instruction-level" security.skill_path: "./agent-skills/data-processor/SKILL.md"
A structured report highlighting parts of the instructions that are susceptible to prompt hijacking, along with concrete mitigation strategies.
Generates structured, career-focused modular curriculums for software engineering students. Use when designing new educational modules or tailoring learning journeys to specific career roles.
Generates end-to-end student projects that reinforce specific modular learning objectives. Use to create professional-grade portfolio pieces and assessment tasks for engineering mentees.
Evaluates student code submissions based on conceptual mastery rather than just correctness. Use to provide high-quality educational feedback on architectural patterns and programming logic.
Detects and redacts Personally Identifiable Information (PII) like emails, phone numbers, and credit cards. Use when cleaning logs, datasets, or communications to comply with GDPR/CCPA privacy standards.
Optimize pyproject.toml and resolve complex dependency trees using modern tools like Poetry or uv. Use to modernize Python project management.
Analyze and optimize pytest suites to improve speed, identify flaky tests, and increase coverage. Use to maintain high-quality, fast-running test pipelines.