RRepoGEO

REPOGEO REPORT · LITE

forloopcodes/contextplus

Default branch main · commit 393875a2 · scanned 6/25/2026, 10:33:24 PM

GitHub: 1,925 stars · 162 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)

3 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 forloopcodes/contextplus, 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 Context+'s domain to prevent misinterpretation by AI

    Why:

    COPY-PASTE FIX
    Add a new sentence immediately after the existing opening paragraph: "Context+ is specifically engineered for deep codebase analysis and semantic search in large-scale software projects, distinct from tools for managing LLM context windows or Python's `contextlib`."
  • hightopics#2
    Expand repository topics to improve categorization

    Why:

    CURRENT
    mcp-server
    COPY-PASTE FIX
    code-analysis, semantic-search, rag, tree-sitter, codebase-intelligence, engineering-tools, ast-analysis, spectral-clustering, mcp-server
  • mediumreadme#3
    Add a dedicated 'Key Features & Benefits' section

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Key Features & Benefits' after the initial description, summarizing the core capabilities in bullet points, e.g., '- Semantic code search by meaning, not text. - Structural AST tree generation with dynamic pruning. - Identifier-level semantic retrieval with call site ranking. - Codebase navigation via spectral clustering.'

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 forloopcodes/contextplus
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Sourcegraph
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Sourcegraph · recommended 1×
  2. OpenGrok · recommended 1×
  3. Elasticsearch · recommended 1×
  4. GitHub Code Search · recommended 1×
  5. IntelliJ IDEA · recommended 1×
  • CATEGORY QUERY
    How to semantically search a large codebase for related features and functions?
    you: not recommended
    AI recommended (in order):
    1. Sourcegraph
    2. OpenGrok
    3. Elasticsearch
    4. GitHub Code Search
    5. IntelliJ IDEA
    6. PyCharm
    7. VS Code
    8. CodeQL

    AI recommended 8 alternatives but never named forloopcodes/contextplus. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help analyze large codebases with AI and AST for better understanding?
    you: not recommended
    AI recommended (in order):
    1. Sourcegraph with Cody (sourcegraph/cody)
    2. GitHub Copilot Enterprise
    3. CodeQL (github/codeql)
    4. OpenAI GPT-4
    5. Tree-sitter (tree-sitter/tree-sitter)
    6. ANTLR (antlr/antlr4)
    7. Anthropic's Claude
    8. Google's Gemini
    9. Semgrep (returntocorp/semgrep)

    AI recommended 9 alternatives but never named forloopcodes/contextplus. 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 forloopcodes/contextplus?
    pass
    AI named forloopcodes/contextplus explicitly

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

  • If a team adopts forloopcodes/contextplus in production, what risks or prerequisites should they evaluate first?
    pass
    AI named forloopcodes/contextplus 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 forloopcodes/contextplus solve, and who is the primary audience?
    pass
    AI named forloopcodes/contextplus explicitly

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

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forloopcodes/contextplus — 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