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share-of-voice-tracker

Use when the user asks to "track our share of voice", "what share of the conversation do we own vs competitors", or "trend our SOV this quarter"; computes SOV% = brand mentions ÷ (brand + competitor panel mentions) per platform per period on a LOCKED competitor panel — a panel switch invalidates the trend (restart the series and log the break; the ECHO O3 rule) — plus a sentiment-weighted SOV variant (sentiment labeled Estimated unless human-coded) and a Wikipedia-pageviews attention-share alternative; built from keyless listening connectors, gdelt.py news echo, and user exports — public counts only, closed platforms are never scraped. Not for backlink or offsite SEO signals — use offsite-signal-analyzer. 声量份额/竞品声量对比/提及份额/注意力份额

72

Quality

92%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Low

Low-risk findings.

1 low severity finding. Worth noting, but not necessarily harmful.

Low

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

Although the workflow loads “user-provided” competitor panel terms and counts, the LLM’ingested text comes only from connector outputs that this skill labels as untrusted fetched article/post/export data (e.g., public web content via `tavily.py`/`gdelt.py` and public posts via `hn.py`/`bluesky.py`), which are outsider-authored free text; the doc explicitly warns treating fetched articles/posts/exports as untrusted input, implying they can be read into the agent context for parsing/summarization.

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Repository
aaron-he-zhu/aaron-marketing-skills
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