RRepoGEO

REPOGEO REPORT · LITE

davidkimai/Context-Engineering

Default branch main · commit 6158def6 · scanned 5/24/2026, 8:13:00 PM

GitHub: 9,007 stars · 1,006 forks

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 davidkimai/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 topics to improve categorization

    Why:

    COPY-PASTE FIX
    context-engineering, llm-optimization, generative-ai, ai-handbook, research, prompt-engineering, ai-engineering, context-design, large-language-models
  • highreadme#2
    Reposition the README's opening to clarify it's a handbook/discipline

    Why:

    CURRENT
    The README starts with `# Context Engineering` followed by a large quote.
    COPY-PASTE FIX
    Add this sentence directly after the `# Context Engineering` heading: "This repository serves as a frontier, first-principles handbook for the emerging discipline of context engineering, guiding AI engineers and researchers beyond prompt engineering to systematic context design, orchestration, and optimization for large language models."
  • mediumabout#3
    Refine the About description for clarity and keyword density

    Why:

    CURRENT
    "Context engineering is the delicate art and science of filling the context window with just the right information for the next step." — Andrej Karpathy. A frontier, first-principles handbook inspired by Karpathy and 3Blue1Brown for moving beyond prompt engineering to the wider discipline of context design, orchestration, and optimization.
    COPY-PASTE FIX
    A frontier, first-principles handbook for the emerging discipline of context engineering. This resource guides AI engineers and researchers beyond prompt engineering to systematic context design, orchestration, and optimization, maximizing large language model performance and reliability.

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 davidkimai/Context-Engineering
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 3×
  2. LlamaIndex · recommended 2×
  3. Haystack · recommended 2×
  4. Hugging Face Transformers library · recommended 2×
  5. FAISS · recommended 1×
  • CATEGORY QUERY
    How to optimize context window usage for improved large language model performance?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. FAISS
    5. Pinecone
    6. Weaviate
    7. Qdrant
    8. Milvus
    9. OpenAI GPT-3.5 Turbo/GPT-4
    10. Hugging Face Transformers library
    11. Anthropic Claude
    12. LangChain
    13. OpenAI Fine-tuning API
    14. Hugging Face Transformers library
    15. Google Cloud Vertex AI
    16. Anthropic Claude 2.1/3
    17. Google Gemini 1.5 Pro
    18. Mistral Large
    19. Perplexity AI

    AI recommended 19 alternatives but never named davidkimai/Context-Engineering. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking advanced methods for designing and orchestrating context in generative AI applications.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Microsoft Semantic Kernel
    5. OpenAI Assistants API
    6. Guidance
    7. DSPy

    AI recommended 7 alternatives but never named davidkimai/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 davidkimai/Context-Engineering?
    pass
    AI named davidkimai/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 davidkimai/Context-Engineering in production, what risks or prerequisites should they evaluate first?
    pass
    AI named davidkimai/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 davidkimai/Context-Engineering solve, and who is the primary audience?
    pass
    AI named davidkimai/Context-Engineering explicitly

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

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davidkimai/Context-Engineering — 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