Use this skill for any GTM Engineering work in Deepline — prospecting, list-building, account research, contact enrichment, email/phone/LinkedIn lookup, ICP qualification, lead scoring, outreach copy, CSV-driven row work, and verifying enriched data. Triggers on phrases like 'enrich this CSV', 'find contacts at these companies', 'build a TAM list', 'waterfall emails for these leads', 'detect job changes', 'is this data accurate', 'write a sequence for', and on any request that mentions Deepline, plays, or named GTM providers (Crustdata, Hunter, Dropleads, etc.). Use this even when the user does not explicitly say 'GTM' — most CSV-with-leads tasks and most provider-driven enrichment tasks are GTM tasks. SKIP only when the request is a Clay table extraction (use clay-to-deepline) or has no Deepline / outbound / data-enrichment dimension at all.
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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.
In `plays/route-fanout.play.ts`, outsider-authored free text flows into runtime LLM inputs via `signalRoutes` and `researchRoutes`: row-derived `row.domain`, `row.company_name`, `row.query`/`researchQuery(row)` are embedded into `deeplineagent` prompts (agent reads), and the resulting model processes scraped content returned by `firecrawl_scrape`/search tools.
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