RRepoGEO

REPOGEO REPORT · LITE

WakeUp-Jin/Practical-Guide-to-Context-Engineering

Default branch main · commit 44a958d0 · scanned 6/6/2026, 7:33:01 PM

GitHub: 687 stars · 56 forks

AI VISIBILITY SCORE
28 /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
2 / 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 WakeUp-Jin/Practical-Guide-to-Context-Engineering, 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 relevant LLM and guide-focused topics

    Why:

    CURRENT
    javascript, typescript
    COPY-PASTE FIX
    llm, large-language-models, context-engineering, agent-systems, llm-development, guide, methodology, ai-applications
  • highlicense#2
    Add a LICENSE file

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root of the repository with an appropriate open-source license.
  • mediumabout#3
    Refine the 'About' description to clarify it's a guide

    Why:

    CURRENT
    大模型应用开发的方向，上下文工程是设计原则，Agent Harness 是构建目标，本项目的目标，是为开发者和研究者提供一份大模型应用开发的骨架思路
    COPY-PASTE FIX
    为大模型应用开发者和研究者提供一份关于上下文工程和Agent运行空间实践的系统化指南与骨架思路，而非代码库。

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 WakeUp-Jin/Practical-Guide-to-Context-Engineering
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 1×
  2. run-llama/llama_index · recommended 1×
  3. OpenAI API · recommended 1×
  4. Pinecone · recommended 1×
  5. weaviate/weaviate · recommended 1×
  • CATEGORY QUERY
    What are best practices for managing context in large language model applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. OpenAI API
    4. Pinecone
    5. Weaviate (weaviate/weaviate)
    6. Chroma (chroma-core/chroma)
    7. Redis (redis/redis)
    8. SQLite
    9. Qdrant (qdrant/qdrant)
    10. Anthropic Claude
    11. Google Gemini
    12. Hugging Face Transformers (huggingface/transformers)
    13. Google Vertex AI

    AI recommended 13 alternatives but never named WakeUp-Jin/Practical-Guide-to-Context-Engineering. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to build robust and stable LLM agent systems using JavaScript or TypeScript?
    you: not recommended
    AI recommended (in order):
    1. LangChain.js
    2. LlamaIndex.TS
    3. Agent Protocol
    4. OpenAI SDK
    5. Google AI SDK
    6. Hugging Face Transformers.js
    7. zod
    8. axios

    AI recommended 8 alternatives but never named WakeUp-Jin/Practical-Guide-to-Context-Engineering. 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 WakeUp-Jin/Practical-Guide-to-Context-Engineering?
    pass
    AI named WakeUp-Jin/Practical-Guide-to-Context-Engineering explicitly

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

  • If a team adopts WakeUp-Jin/Practical-Guide-to-Context-Engineering in production, what risks or prerequisites should they evaluate first?
    pass
    AI named WakeUp-Jin/Practical-Guide-to-Context-Engineering 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 WakeUp-Jin/Practical-Guide-to-Context-Engineering solve, and who is the primary audience?
    pass
    AI did not name WakeUp-Jin/Practical-Guide-to-Context-Engineering — 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
  • Prioritized action items8 vs 3 in Lite
WakeUp-Jin/Practical-Guide-to-Context-Engineering — RepoGEO report