RRepoGEO

REPOGEO REPORT · LITE

BannyLon/DifyAIA

Default branch main · commit 32b0e2e5 · scanned 6/23/2026, 7:18:02 AM

GitHub: 2,578 stars · 356 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
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 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 BannyLon/DifyAIA, 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
  • hightopics#1
    Add specific topics for better categorization

    Why:

    COPY-PASTE FIX
    dify, ai-workflow, workflow-as-code, generative-ai, llm-applications, ai-examples, ai-application-development
  • highlicense#2
    Create a formal LICENSE file

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root containing the full text of the MIT License, as indicated in the README.
  • highreadme#3
    Refine the README's opening sentence for clarity

    Why:

    CURRENT
    本仓库为B站 **嗯哌AI** UP主 设计制作的 **Dify工作流DSL开源示例库（Dify-Workflow-DSL-Examples）** ，这里开源的Dify工作流均是B站UP主 **嗯哌AI** 在学习Dify过程中构建的完整工作流示例，全部调试成功后才发布，但不保证随着dify版本升级以及大模型的选择使用而出现错误。
    COPY-PASTE FIX
    This repository is an open-source collection of Dify Workflow DSL examples, designed by Bilibili UP主 嗯哌AI. It provides fully debugged workflows to help developers, teams, and educators quickly build and deploy AI applications using Dify.

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 BannyLon/DifyAIA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. Hugging Face Spaces · recommended 1×
  3. huggingface/diffusers · recommended 1×
  4. langchain-ai/langchain · recommended 1×
  5. langchain-ai/langchain-templates · recommended 1×
  • CATEGORY QUERY
    Where can I find open-source examples for building AI application workflows quickly?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Hugging Face Spaces
    3. Hugging Face Diffusers (huggingface/diffusers)
    4. LangChain (langchain-ai/langchain)
    5. LangChain Templates (langchain-ai/langchain-templates)
    6. LlamaIndex (run-llama/llama_index)
    7. Gradio (gradio-app/gradio)
    8. PyTorch (pytorch/examples)
    9. TensorFlow (tensorflow/examples)
    10. OpenAI Cookbook (openai/openai-cookbook)

    AI recommended 10 alternatives but never named BannyLon/DifyAIA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to implement workflow-as-code for complex generative AI application development?
    you: not recommended
    AI recommended (in order):
    1. Kubeflow Pipelines
    2. MLflow
    3. Apache Airflow
    4. Prefect
    5. Metaflow
    6. Argo Workflows
    7. DVC

    AI recommended 7 alternatives but never named BannyLon/DifyAIA. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 BannyLon/DifyAIA?
    pass
    AI did not name BannyLon/DifyAIA — likely talking about a different project

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

  • If a team adopts BannyLon/DifyAIA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named BannyLon/DifyAIA 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 BannyLon/DifyAIA solve, and who is the primary audience?
    pass
    AI did not name BannyLon/DifyAIA — likely talking about a different project

    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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BannyLon/DifyAIA — RepoGEO report