Lead qualification engine with conversational intake. Asks structured questions to understand your qualification criteria, generates a reusable qualification prompt, then batch-enriches leads via Apify LinkedIn scraping and scores them with parallel processing. Outputs qualified/disqualified verdicts with confidence scores and reasoning to CSV or whatever output format the user prefers. Supports calibration mode for prompt refinement.
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Low-risk findings.
2 low severity findings. 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 Phase 2 Step 1.5 the agent reads LinkedIn URLs from a user-provided input CSV and then batch-enriches them via `skills/lead-qualification/scripts/enrich_leads.py`, which ultimately extracts and passes `enriched_about`/profile fields (outsider-authored LinkedIn profile text) into the LLM’s qualification reasoning.
The skill fetches instructions or code from an external URL at runtime, and the fetched content directly controls the agent’s prompts or executes code. This dynamic dependency allows the external source to modify the agent’s behavior without any changes to the skill itself.
The enrichment script calls Apify API endpoints at runtime (e.g., https://api.apify.com/v2 and optionally https://api.gooseworks.ai) to start a remote Apify actor (https://console.apify.com/actors/harvestapi~linkedin-profile-scraper) which executes scraping code used to produce enrichment data relied on by the qualification flow.
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