Rank the partner-application backlog by net-new value and surface a short chase-list of high-value applicants who haven't booked a call. Use when the user wants to triage, rank, or prioritize partner applications, find which applicants are worth chasing, run the daily/weekly application review, or asks "who should I reach out to" / "which applications matter". Reads the live partners workspace; read-only.
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Low
Low-risk findings worth noting
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Outsider-authored free text from public application records is fetched at runtime via `urllib.request.urlopen` from the partners API and then turned into readable prose in `rank.py` as `notes = notes_str(rec)` / `notes.strip()[:400]`, which becomes part of the JSON output consumed by the LLM judgment pass (Phase 1 reads `notes`).
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