RRepoGEO

REPOGEO REPORT · LITE

echonoshy/cgft-llm

Default branch master · commit d6531f8c · scanned 6/26/2026, 5:38:19 AM

GitHub: 2,418 stars · 323 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
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 echonoshy/cgft-llm, 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
  • highabout#1
    Update repository description and add relevant topics

    Why:

    CURRENT
    Description: "Practice to LLM." Topics: (none)
    COPY-PASTE FIX
    Description: "Hands-on tutorials and practical examples for building AI agent systems and understanding core large language model technologies." Topics: ["llm", "large-language-models", "ai-agents", "agent-systems", "llm-tutorials", "deep-learning", "machine-learning", "ai-tools", "hands-on-ai", "python", "generative-ai"]
  • mediumreadme#2
    Add a clear English summary to the top of the README

    Why:

    COPY-PASTE FIX
    Insert the following sentence right after the initial badges/sync statement and before "# 📌 动手做大模型系列":
"This repository offers a hands-on series of tutorials and practical projects for building AI agent systems and exploring core large language model (LLM) technologies."
  • lowhomepage#3
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/echonoshy/cgft-llm

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 echonoshy/cgft-llm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. DeepLearning.AI · recommended 1×
  4. OpenAI Cookbook · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    Where can I find hands-on tutorials for building AI agent systems with large language models?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. DeepLearning.AI
    4. OpenAI Cookbook
    5. Hugging Face Transformers
    6. AutoGen
    7. AI Coffee Break with Letitia
    8. James Briggs

    AI recommended 8 alternatives but never named echonoshy/cgft-llm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for practical examples and code to understand core large language model technologies.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. PyTorch
    3. torchtext
    4. TensorFlow
    5. Keras
    6. OpenAI API
    7. GPT-3.5
    8. GPT-4
    9. nanoGPT
    10. minGPT

    AI recommended 10 alternatives but never named echonoshy/cgft-llm. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 echonoshy/cgft-llm?
    pass
    AI named echonoshy/cgft-llm explicitly

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

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

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

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