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Pass
Audited by Gen Agent Trust Hub on Nov 7, 2026
Risk Level: SAFEINDIRECT_PROMPT_INJECTIONCOMMAND_EXECUTIONEXTERNAL_DOWNLOADSDYNAMIC_EXECUTION
Full Analysis
- [INDIRECT_PROMPT_INJECTION]: The skill ingests and processes natural language content from GitHub issues (PRD-lite) and pull requests to determine build steps, update ledgers, and manage the slice checklist. An adversary with write access to the linked GitHub repository could embed malicious instructions in these resources to influence the agent's code generation or tool usage.
- Ingestion points: The skill retrieves external content using
gh issue viewandgh pr viewduring the Build loop and Close phases. - Boundary markers: The skill relies on specific Markdown headers (e.g.,
## Slices,## Review Disposition Ledger) to locate data, which provides a degree of structural isolation but does not prevent natural language instruction injection within those sections. - Capability inventory: The skill possesses extensive capabilities, including shell command execution, git operations, local file system access, and network interaction via the GitHub CLI.
- Sanitization: The skill implements logic to prevent accidental data loss (e.g., refusing shorter body updates) but lacks explicit sanitization or filtering for embedded natural language instructions.
- [COMMAND_EXECUTION]: The skill executes a bundled shell script and various developer tools to manage project state.
- Evidence:
references/sidecar.shis a large shell script executed viabashto handle project identity resolution, work item management, and local git operations in a sidecar directory. - [EXTERNAL_DOWNLOADS]: The skill provides instructions for the agent to download and install standard development dependencies.
- Evidence:
references/tracer-test.mddirects the installation of the@playwright/testpackage and related browser binaries from official registries. - [DYNAMIC_EXECUTION]: The skill generates and runs test scripts based on templates and the agent's interpretation of the user's task.
- Evidence: The Feature Path workflow involves writing "tracer tests" and implementation code, then executing those tests locally to verify the proof of concept.
Audit Metadata