RRepoGEO

REPOGEO REPORT · LITE

kantord/SeaGOAT

Default branch main · commit dda77780 · scanned 6/21/2026, 7:11:17 PM

GitHub: 1,298 stars · 93 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface kantord/SeaGOAT, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Clarify SeaGOAT's core identity as a local-first developer tool in the README's opening

    Why:

    CURRENT
    A code search engine for the AI age. SeaGOAT is a local search tool that leverages vector embeddings to enable you to search your codebase semantically.
    COPY-PASTE FIX
    SeaGOAT is a local-first semantic code search engine for developers. It's a powerful CLI tool that leverages AI embeddings to help you semantically search your own codebase, offline and on your machine.
  • mediumtopics#2
    Add more specific topics to highlight local-first and developer tool aspects

    Why:

    CURRENT
    ai, ai-project, code-search, code-search-engine, embeddings, grep, grep-like, hacktoberfest, hacktoberfest2023, llm, regular-expression, ripgrep, vector-database, vector-embeddings
    COPY-PASTE FIX
    ai, ai-project, code-search, code-search-engine, embeddings, grep, grep-like, hacktoberfest, hacktoberfest2023, llm, regular-expression, ripgrep, vector-database, vector-embeddings, developer-tool, cli-tool, offline-search, local-search
  • lowcomparison#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

Unlike traditional tools like `grep`, `ripgrep`, `ack`, or `ag` which rely on exact text matching or regular expressions, SeaGOAT uses AI embeddings to understand the *meaning* of your code. This allows for semantic searches using natural language queries. While enterprise solutions like Sourcegraph or GitHub Code Search offer powerful features, SeaGOAT operates entirely locally and offline, ensuring your code never leaves your machine.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface kantord/SeaGOAT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Elasticsearch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Elasticsearch · recommended 2×
  2. ChromaDB · recommended 1×
  3. sentence-transformers · recommended 1×
  4. OpenAI Embeddings · recommended 1×
  5. Faiss · recommended 1×
  • CATEGORY QUERY
    How to semantically search my local codebase using AI embeddings?
    you: not recommended
    AI recommended (in order):
    1. ChromaDB
    2. sentence-transformers
    3. OpenAI Embeddings
    4. Faiss
    5. Weaviate
    6. Qdrant
    7. Elasticsearch

    AI recommended 7 alternatives but never named kantord/SeaGOAT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an intelligent code search tool that understands code meaning, not just text.
    you: not recommended
    AI recommended (in order):
    1. Sourcegraph
    2. GitHub Code Search (Beta)
    3. OpenGrok
    4. JetBrains IDEs
    5. CodeQL
    6. Elasticsearch

    AI recommended 6 alternatives but never named kantord/SeaGOAT. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of kantord/SeaGOAT?
    pass
    AI named kantord/SeaGOAT explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts kantord/SeaGOAT in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kantord/SeaGOAT explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo kantord/SeaGOAT solve, and who is the primary audience?
    pass
    AI named kantord/SeaGOAT explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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kantord/SeaGOAT — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite