project-development
Pass
Audited by Gen Agent Trust Hub on Mar 18, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: No security issues detected. The skill consists of educational documentation and code templates that follow standard industry practices for developing LLM-powered applications.
- [INDIRECT_PROMPT_INJECTION]: The skill describes patterns for ingesting and processing untrusted data from external sources (e.g., Hacker News articles and comments), which is a common architectural pattern for LLM agents. While this creates a potential attack surface for indirect prompt injection, the skill advocates for mitigations such as using structured output markers and format enforcement. \n
- Ingestion points: Data is fetched from external sources in the
stage_acquirefunction as described inSKILL.mdand implemented as a placeholder inscripts/pipeline_template.py. \n - Boundary markers: Prompts use explicit markdown headers (e.g.,
## Summary,## Score) and formatting instructions to define expected output structure. \n - Capability inventory: The provided scripts are restricted to local file operations for state tracking in
data/andoutput/directories. \n - Sanitization: The methodology emphasizes robust parsing of LLM outputs but does not explicitly detail input sanitization techniques for data interpolated into prompts.
Audit Metadata