first-principles-thinking
Pass
Audited by Gen Agent Trust Hub on Sep 15, 2026
Risk Level: SAFEINDIRECT_PROMPT_INJECTIONCOMMAND_EXECUTION
Full Analysis
- [INDIRECT_PROMPT_INJECTION]: The skill is designed to ingest and decompose user-supplied problem descriptions into ground truths and reasoning chains. These outputs are then automatically added to implementation and verification contexts (
implement.jsonl,check.jsonl) via the Trellis integration. This establishes a pipeline where adversarial content in the initial problem description could potentially influence downstream agent behavior in the development lifecycle. - Ingestion points: User input defining the problem or design to be analyzed (Phase 1).
- Capability inventory: Writing analysis files to the
.trellis/tasks/directory and executing local Python scripts to update task metadata. - Boundary markers: The skill requires specific structured artifacts (axiom tables, reasoning chains) which acts as a structural filter, but does not implement explicit delimiters for raw user input.
- Sanitization: The skill does not explicitly describe sanitization of user-provided content before interpolation into the analysis artifacts.
- [COMMAND_EXECUTION]: The skill instructions include the execution of a local script
python3 ./.trellis/scripts/task.pyto manage project context and record task completion. This is a functional requirement for integration with the Trellis framework mentioned in the documentation.
Audit Metadata