niche-signal-discovery
Discover niche first-party signals that differentiate Closed Won vs Closed Lost accounts for ICP analysis. Use when the user provides won/lost customer domain lists and wants differential signals (website content, job listings, tech stack, maturity markers) to build account scoring models and prospecting criteria. Triggers: ICP analysis, niche signals, won vs lost analysis, differential signals, signal discovery, ICP signal report, account scoring signals, lead scoring, first-party signals, buyer signals. Before reading this file, first read deepline-gtm to understand the Deepline CLI tool and how to use it. Then read this file for guidance on the task.
75
92%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Advisory
Suggest reviewing before use
Security
2 findings — 2 medium severity. This skill can be installed but you should review these findings before use.
W011: Third-party content exposure detected (indirect prompt injection risk)
What this means
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.
Why it was flagged
Third-party content exposure detected (high risk: 0.95). Step 2 and Step 7 ingest outsider-authored free text from runtime web extraction: `deepline enrich` uses Serper/Firecrawl to scrape arbitrary company pages (public web content) and Crustdata job listings, which are then parsed into LLM-readable text in `scripts/analyze_signals.py` (`parse_website_content` / `parse_job_listings`) and later rendered as evidence quotes in the report.
W021: Hidden or invisible Unicode characters detected (potential obfuscation or prompt injection)
What this means
Detected hidden or invisible Unicode characters (Format/Cf or Control/Cc categories) in the component’s content. These characters are invisible when rendered but are still processed by AI models, and attackers use them to smuggle instructions past human review — for example, zero-width spaces, bidirectional overrides, invisible formatters, or Unicode Tag characters (U+E0000–U+E007F) that encode an entire hidden message. Severity escalates to high when three or more distinct hidden character types are present, or when a hidden tag-encoded message is successfully decoded, as these strongly indicate intentional obfuscation.
Why it was flagged
Hidden Unicode characters detected (1 type(s) found)
- Repository
- getaero-io/gtm-eng-skills
- Commit
449bc08
- Audited
- Security analysis
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