RRepoGEO

REPOGEO REPORT · LITE

jasontang-ai/Context-Engineering

Default branch main · commit 6158def6 · scanned 6/19/2026, 11:03:16 AM

GitHub: 9,097 stars · 1,012 forks

AI VISIBILITY SCORE
22 /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
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 jasontang-ai/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 the repository

    Why:

    COPY-PASTE FIX
    context-engineering, llm-context, prompt-engineering, large-language-models, ai-handbook, research-review, context-window, llm-optimization, generative-ai, karpathy, 3blue1brown
  • highreadme#2
    Reposition the README's opening to clearly state its nature as a handbook/research resource

    Why:

    CURRENT
    The current README excerpt starts with a large centered div, then a quote, then 'Comprehensive Course Under Construction' and 'Awesome Context Engineering Repo.'
    COPY-PASTE FIX
    This repository is a frontier, first-principles handbook and comprehensive research review for context engineering, moving beyond prompt engineering to the wider discipline of context design, orchestration, and optimization. Inspired by Andrej Karpathy and 3Blue1Brown, it provides operational insights from 1400 research papers and visual explanations.
  • mediumreadme#3
    Clarify the role of 'Agent Commands' within the handbook context

    Why:

    CURRENT
    The README excerpt shows 'Agent Commands' prominently listed with support for various CLIs, without clear context of its relation to a 'handbook.'
    COPY-PASTE FIX
    To illustrate context engineering principles in practice, this handbook includes experimental `Agent Commands` with support for Claude Code, OpenCode, Amp, Kiro, Codex, and Gemini CLI. These are examples of applying context design, not a standalone framework.

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 jasontang-ai/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. deepset-ai/haystack · recommended 1×
  4. OpenAI API · recommended 1×
  5. Anthropic Claude · recommended 1×
  • CATEGORY QUERY
    How to effectively manage and optimize LLM context windows for better performance?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Haystack (deepset-ai/haystack)
    4. OpenAI API
    5. Anthropic Claude
    6. Sentence-BERT (UKPLab/sentence-transformers)
    7. Hugging Face Transformers (huggingface/transformers)
    8. Pinecone
    9. Weaviate (weaviate/weaviate)
    10. ChromaDB (chroma-core/chroma)

    AI recommended 10 alternatives but never named jasontang-ai/Context-Engineering. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools or frameworks for advanced LLM context design and orchestration beyond basic prompting?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI Functions/Tools
    5. DSPy
    6. Guidance
    7. AutoGPT/BabyAGI

    AI recommended 7 alternatives but never named jasontang-ai/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 jasontang-ai/Context-Engineering?
    pass
    AI did not name jasontang-ai/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?

  • If a team adopts jasontang-ai/Context-Engineering in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jasontang-ai/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 jasontang-ai/Context-Engineering solve, and who is the primary audience?
    pass
    AI did not name jasontang-ai/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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jasontang-ai/Context-Engineering — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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