RRepoGEO

REPOGEO REPORT · LITE

wanikua/danghuangshang

Default branch main · commit ac71d0eb · scanned 5/21/2026, 12:57:08 PM

GitHub: 2,658 stars · 249 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 wanikua/danghuangshang, 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
    Immediately state the project's core technical purpose in the README

    Why:

    CURRENT
    The README starts with navigation links, originality notice, and contribution notice before the main title and project definition.
    COPY-PASTE FIX
    # 🏛️ 当皇上 ✖️ OpenClaw

这是一个基于 OpenClaw 框架，以明朝内阁制为蓝本构建的多 Agent AI 协作系统，并提供从零基础到进阶的完整搭建教程。
  • mediumcomparison#2
    Add a 'Compared to X' section in README

    Why:

    COPY-PASTE FIX
    ## 🆚 与 LangChain / AutoGen / CrewAI 等框架的比较

「当皇上」项目并非一个通用的多 Agent 框架，而是基于 OpenClaw 框架构建的一个具体的多 Agent 协作系统（AI 朝廷），并提供完整的实践教程。
- **专注于系统实现与教程**：我们提供一个开箱即用的“AI 朝廷”系统，并详细指导如何从零开始搭建和定制。
- **独特的应用场景**：以明朝内阁制为蓝本，展示了多 Agent 系统在模拟复杂组织和工作流自动化中的强大潜力。
- **基于 OpenClaw**：利用 OpenClaw 的能力，实现高效的 Agent 间协作与任务编排。
  • lowabout#3
    Refine repository description to be more explicit about the system type and framework

    Why:

    CURRENT
    AI 朝廷搭建完整教程 - 从零基础到进阶
    COPY-PASTE FIX
    基于 OpenClaw 框架的多 Agent AI 协作系统搭建教程 - 从零基础到进阶

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 wanikua/danghuangshang
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. CrewAI · recommended 2×
  3. Python · recommended 1×
  4. JavaScript (Node.js) · recommended 1×
  5. Redis Pub/Sub · recommended 1×
  • CATEGORY QUERY
    Need a comprehensive tutorial to build a multi-agent AI collaboration system from scratch.
    you: not recommended
    AI recommended (in order):
    1. Python
    2. JavaScript (Node.js)
    3. Redis Pub/Sub
    4. RabbitMQ
    5. OpenAI API
    6. Anthropic Claude API
    7. Google Gemini API
    8. Hugging Face Transformers
    9. ollama
    10. LangChain
    11. LlamaIndex
    12. OpenAI Function Calling
    13. ChromaDB
    14. Weaviate
    15. Pinecone
    16. SQLite
    17. PostgreSQL
    18. CrewAI
    19. AutoGen (Microsoft)
    20. Streamlit
    21. Gradio
    22. Flask
    23. Django
    24. Python's logging module
    25. LangSmith
    26. Prometheus
    27. Grafana

    AI recommended 27 alternatives but never named wanikua/danghuangshang. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to implement autonomous AI agents for complex workflow automation in a simulated organization?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. AutoGen
    3. Mesa
    4. OpenAI Assistants API
    5. Haystack
    6. CrewAI

    AI recommended 6 alternatives but never named wanikua/danghuangshang. 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 wanikua/danghuangshang?
    pass
    AI named wanikua/danghuangshang explicitly

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

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

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

Embed your GEO score

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MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/wanikua/danghuangshang"><img src="https://repogeo.com/badge/wanikua/danghuangshang.svg" alt="RepoGEO" /></a>
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wanikua/danghuangshang — 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