RRepoGEO

REPOGEO REPORT · LITE

cytostack/openwolf

Default branch main · commit bd698351 · scanned 5/9/2026, 6:28:34 AM

GitHub: 1,443 stars · 112 forks

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 cytostack/openwolf, 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
    Reposition the README's opening paragraph to explicitly state its function for AI coding assistants

    Why:

    CURRENT
    <strong>A second brain for Claude Code.</strong><br />Project intelligence, token tracking, and invisible enforcement through 6 hook scripts. Zero workflow changes.
    COPY-PASTE FIX
    <strong>OpenWolf is open-source middleware that acts as a second brain for Claude Code, providing sharper context and fewer tokens for AI coding assistants.</strong><br />It offers project intelligence, token tracking, and invisible enforcement through 6 hook scripts, requiring zero workflow changes.
  • mediumreadme#2
    Explicitly link the 'Why OpenWolf Exists' section to key problem statements

    Why:

    CURRENT
    Claude Code is powerful but it works blind. It doesn't know what a file contains until it opens it. It can't tell a 50-token config from a 2,000-token module. It reads the same file multiple times in one session without noticing. It has no index of your project, no memory of your corrections, and no awareness of what it already tried.
    COPY-PASTE FIX
    Claude Code is powerful but it works blind, leading to high token usage and limited context for AI coding assistants. It doesn't know what a file contains until it opens it. It can't tell a 50-token config from a 2,000-token module. It reads the same file multiple times in one session without noticing. It has no index of your project, no memory of your corrections, and no awareness of what it already tried.
  • lowtopics#3
    Add more specific topics to improve categorization accuracy

    Why:

    CURRENT
    anthropic, claude-ai, claude-code, cli, developer-tools, middleware, open-source, token-optimization
    COPY-PASTE FIX
    anthropic, claude-ai, claude-code, cli, developer-tools, middleware, open-source, token-optimization, ai-developer-tools, code-generation-ai, context-management

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 cytostack/openwolf
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
facebookresearch/faiss
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. facebookresearch/faiss · recommended 1×
  2. weaviate/weaviate · recommended 1×
  3. Pinecone · recommended 1×
  4. tree-sitter/tree-sitter · recommended 1×
  5. antlr/antlr4 · recommended 1×
  • CATEGORY QUERY
    How to reduce token usage and improve context for AI coding assistants?
    you: not recommended
    AI recommended (in order):
    1. Faiss (facebookresearch/faiss)
    2. Weaviate (weaviate/weaviate)
    3. Pinecone
    4. Tree-sitter (tree-sitter/tree-sitter)
    5. ANTLR (antlr/antlr4)
    6. Sourcegraph (sourcegraph/sourcegraph)
    7. OpenGrok (OpenGrok/OpenGrok)
    8. Git (git/git)
    9. LangChain (langchain-ai/langchain)
    10. LlamaIndex (run-llama/llama_index)

    AI recommended 10 alternatives but never named cytostack/openwolf. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools provide project intelligence and memory for large language model code generation?
    you: not recommended
    AI recommended (in order):
    1. Cursor
    2. Continue (continue-ui/continue)
    3. Codeium
    4. GitHub Copilot Chat
    5. Cody by Sourcegraph (sourcegraph/cody)
    6. Tabnine

    AI recommended 6 alternatives but never named cytostack/openwolf. 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 cytostack/openwolf?
    pass
    AI named cytostack/openwolf explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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