interior-design-expert
Pass
Audited by Gen Agent Trust Hub on Sep 15, 2026
Risk Level: SAFEINDIRECT_PROMPT_INJECTIONEXTERNAL_DOWNLOADSDYNAMIC_EXECUTION
Full Analysis
- [INDIRECT_PROMPT_INJECTION]: The skill processes user-supplied specifications for room dimensions, styles, and furniture requirements to generate design layouts and visualizations.\n
- Ingestion points: User-provided parameters for room layouts and style preferences defined in SKILL.md and references/space-planning.md.\n
- Boundary markers: The instructions do not specify explicit delimiters or isolation for user-provided design inputs.\n
- Capability inventory: The skill uses file system operations (Write, Edit) and image generation tools (mcp__stability-ai__stability-ai-generate-image).\n
- Sanitization: External design parameters are utilized for calculations and rendering without explicit validation or filtering logic.\n- [EXTERNAL_DOWNLOADS]: The skill's reference materials include code that references third-party Python packages for computational design.\n
- Evidence: Import statements for 'ortools' and 'numpy' in references/space-planning.md.\n
- Context: These are well-known, established libraries from trusted sources (Google and the scientific community) used for legitimate spatial optimization tasks.\n- [DYNAMIC_EXECUTION]: The skill provides a Python implementation of a constraint solver for furniture placement optimization.\n
- Evidence: A complete implementation of the 'RoomLayoutSolver' class in references/space-planning.md.\n
- Context: The provided code contains static mathematical logic intended for the skill's primary function and does not execute arbitrary code or load data from untrusted network sources.
Audit Metadata