RRepoGEO

REPOGEO REPORT · LITE

Meirtz/Awesome-Context-Engineering

Default branch main · commit ca425ab4 · scanned 5/20/2026, 3:28:01 PM

GitHub: 3,143 stars · 235 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
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 Meirtz/Awesome-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
  • highreadme#1
    Clarify repo's nature as a curated list/survey immediately in README

    Why:

    CURRENT
    A comprehensive survey and collection of resources on **Context Engineeringthe evolution from static prompting to dynamic, context-aware AI systems, and increasingly to **agent runtimes, memory systems, protocols, coding agents, and observability stacks**.
    COPY-PASTE FIX
    This repository is an **awesome list and comprehensive survey** on Context Engineering, curating hundreds of papers, frameworks, and implementation guides for LLMs and AI agents. It covers the evolution from static prompting to dynamic, context-aware AI systems, including agent runtimes, memory systems, protocols, coding agents, and observability stacks.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2507.13334
  • lowtopics#3
    Add 'survey' to repository topics

    Why:

    CURRENT
    agent, agentic-ai, agi, awesome-list, cognitive-science, context-engineering, llm, rag
    COPY-PASTE FIX
    agent, agentic-ai, agi, awesome-list, cognitive-science, context-engineering, llm, rag, survey

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 Meirtz/Awesome-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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. Hugging Face Transformers Library · recommended 2×
  4. OpenAI's Prompt Engineering Guide · recommended 1×
  5. Generative AI with Large Language Models · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive guide for context engineering in LLM and AI agent systems?
    you: not recommended
    AI recommended (in order):
    1. OpenAI's Prompt Engineering Guide
    2. LangChain
    3. LlamaIndex
    4. Generative AI with Large Language Models
    5. LangChain for LLM Application Development
    6. Hugging Face Transformers Library
    7. The Prompt Engineering Guide

    AI recommended 7 alternatives but never named Meirtz/Awesome-Context-Engineering. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What resources explain building production-grade AI systems with dynamic context and agentic capabilities?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Designing Data-Intensive Applications
    4. OpenAI API
    5. Practical MLOps
    6. Hugging Face Transformers Library

    AI recommended 6 alternatives but never named Meirtz/Awesome-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 Meirtz/Awesome-Context-Engineering?
    pass
    AI named Meirtz/Awesome-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 Meirtz/Awesome-Context-Engineering in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Meirtz/Awesome-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 Meirtz/Awesome-Context-Engineering solve, and who is the primary audience?
    pass
    AI did not name Meirtz/Awesome-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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Meirtz/Awesome-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