Caveat verdict

product-research

amazon-sorftime-research-market-skill

88
🟢 Trusted
No high-risk patterns surfaced by the deep scan — automated capability review, not behavioral proof.

Multi-step Amazon product-research pipeline using Sorftime MCP + local Python scripts; agent_memory and execution sinks are standard for a multi-phase LLM-driven analysis workflow with user checkpoints at each stage.

⚠ Flagged for review — coarse, uncorroborated signal, not a confirmed exploit. Review the config yourself before installing.

Automated static analysis — not a human review. Caveat flags capabilities, not confirmed intent, and can produce false positives. Disagree with this verdict? Use Dispute below.

68
security
80
transparency
70
maintenance

Permission integrity

Accesses agent memory/configuration files

agent_memory

Findings (3)

Pattern match high

<script> tag in markdown — potential code injection

scripts/render_dashboard.py · prose · downgraded · <script

Pattern match medium

subprocess execution — runs system commands from Python

scripts/api_client.py · prose · downgraded · subprocess.run(

Pattern match low

Python os.environ.get — reads environment variable

scripts/api_client.py · prose · downgraded · os.environ.get(

Why the tier is capped

Execution sink present in raw bytes (Hard Floor: class D). Final tier capped at Caution — cannot be lifted by any downgrade, example-payload opt-in, or allowlist.

Permissions & capabilities

No declared permissions — minimal attack surface.

agent_memory

Is this flag fair?

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