RRepoGEO

REPOGEO REPORT · LITE

NeoLabHQ/context-engineering-kit

Default branch master · commit 895cb52c · scanned 6/26/2026, 12:37:25 PM

GitHub: 1,173 stars · 124 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)

3 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 NeoLabHQ/context-engineering-kit, 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
    Reposition the README's opening paragraph to clarify its niche

    Why:

    CURRENT
    A hand-crafted collection of advanced context engineering techniques and patterns with minimal token footprint, focused on improving agent result quality and predictability.
    COPY-PASTE FIX
    A hand-crafted collection of advanced context engineering techniques and patterns with minimal token footprint, focused on improving agent result quality and predictability. Unlike general LLM orchestration frameworks, CEK focuses specifically on enhancing the quality and predictability of AI agent code generation through token-efficient context engineering for coding assistants.
  • mediumtopics#2
    Update repository topics for better categorization

    Why:

    CURRENT
    agent, ai, claude, cline, cursor, llm, marketplace, opencode, windsurf
    COPY-PASTE FIX
    agent, claude, cline, cursor, opencode, windsurf, code-generation, llm-agents, context-management, token-efficiency, ai-coding-assistant, prompt-engineering
  • lowcomparison#3
    Add a 'Why CEK?' or comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, perhaps titled 'Why Context Engineering Kit?' or 'CEK vs. LLM Frameworks', explaining how CEK complements or differs from tools like LangChain and LlamaIndex by focusing on granular, token-efficient agent skill improvement rather than full orchestration.

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 NeoLabHQ/context-engineering-kit
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 2×
  2. run-llama/llama_index · recommended 2×
  3. OpenAI GPT-3.5/GPT-4 Fine-tuning API · recommended 1×
  4. huggingface/transformers · recommended 1×
  5. meta-llama/codellama · recommended 1×
  • CATEGORY QUERY
    How to improve the quality and predictability of AI agent code generation results?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-3.5/GPT-4 Fine-tuning API
    2. Hugging Face Transformers (huggingface/transformers)
    3. CodeLlama (meta-llama/codellama)
    4. StarCoder (bigcode-project/starcoder)
    5. CodeGen (salesforce/codegen)
    6. LangChain (langchain-ai/langchain)
    7. LlamaIndex (run-llama/llama_index)
    8. DSPy (stanfordnlp/dspy)
    9. GitHub Copilot Chat
    10. Pylint (pylint-dev/pylint)
    11. ESLint (eslint/eslint)
    12. Black (psf/black)
    13. Prettier (prettier/prettier)
    14. Pytest (pytest-dev/pytest)
    15. Jest (facebook/jest)
    16. JUnit (junit-team/junit5)

    AI recommended 16 alternatives but never named NeoLabHQ/context-engineering-kit. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tools for efficient context management in LLM-powered coding assistants to reduce token usage.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Pinecone
    4. Weaviate (weaviate/weaviate)
    5. Chroma (chroma-core/chroma)
    6. Qdrant (qdrant/qdrant)
    7. Sourcegraph (sourcegraph/sourcegraph)
    8. OpenGrok (oracle/opengrok)

    AI recommended 8 alternatives but never named NeoLabHQ/context-engineering-kit. 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 NeoLabHQ/context-engineering-kit?
    pass
    AI named NeoLabHQ/context-engineering-kit explicitly

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

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

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

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NeoLabHQ/context-engineering-kit — 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