claude

agentgeo

The eight geo-* Agent Skills plus the agentgeo-mcp stdio server (auto-started via npx; set AGENTGEO_API_KEY — an ag_test_... key runs everything in zero-credit demo mode).

2 on GitHubby AgentGEOv0.3.1MITupdated 3 months ago
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Plugin agentgeo from marketplace agentgeo in gumlau/agentgeo-skills. Commands read from the repo's manifest — 1 month ago.

This plugin runs code on your machine — it starts local MCP servers. The exact commands are listed below; read them before installing.

What it installs

Skills (8)

  • geo-citations

    Analyze which source domains AI answers cite and how often your domain versus competitors get cited — harvest sources[].url from raw answers, normalize to domains, and rank most-cited domains overall and per engine, your own citation rate, competitor citation rates, and citation-gap domains you shou

  • geo-competitors

    Build a side-by-side competitive GEO profile of how AI answers treat a brand versus each named competitor — assembling visibility, share-of-voice, citation footprint, and sentiment into one comparison table and surfacing why AI favors the leaders. Use when the user asks to compare competitors in AI

  • geo-monitor

    Track brand visibility and share-of-voice over time by registering a prompt set as AgentGEO schedules, then diffing each new run against the previous to report trend — visibility up/down, new competitors appearing, lost or gained citations, sentiment drift. Use when the user asks to monitor GEO over

  • geo-prompt-set

    Generate a representative, intent-layered set of real user prompts (informational, commercial, comparison, transactional, local) for a brand or category and emit a copy-pasteable JSON prompt library that every geo-* skill consumes. Use when the user asks to build a prompt set, generate GEO prompts,

  • geo-report

    Synthesize a full GEO audit report from AgentGEO raw answers — an answer-first executive verdict, an engine × buyer-intent visibility matrix, a decomposed per-dimension scorecard (visibility, share-of-voice, citations, sentiment) with published banding, a quantified competitor benchmark, per-threat

  • geo-sentiment

    Characterize how AI engines describe a brand — classify tone (positive/neutral/negative) per mention, extract recurring attribute words and framing, and output a sentiment + attribute profile per surface with representative quotes. Use when the user asks how AI talks about my brand, what ChatGPT/Per

  • geo-share-of-voice

    Compute a brand's share of voice against named competitors across AI answers — count mentions, flag explicit recommendations, and produce a mention-weighted and recommendation-weighted SoV leaderboard plus a per-engine breakdown. Use when the user asks for share of voice, AI SoV, share of voice vs c

  • geo-visibility

    Measure whether and how prominently a brand appears in AI answers across engines from a prompt set — detect per-answer mention (present/absent), position (first-mentioned, recommended), and surrounding context, then build a prompt×surface presence matrix with a per-engine and blended visibility rate

MCP servers (1)

  • agentgeo

    runs: npx -y agentgeo-mcp

View source on GitHub agentgeo.org

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