RRepoGEO

REPOGEO REPORT · LITE

dongshuyan/Awesome-Prompts

Default branch master · commit 39202616 · scanned 7/1/2026, 1:17:31 AM

GitHub: 1,348 stars · 155 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
61 /100
Needs work
Category recall
1 / 2
Avg rank #2.0 when recommended
Rule findings
1 pass · 0 warn · 1 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 dongshuyan/Awesome-Prompts, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root, for example, with the MIT License text, to clearly state the terms of use for your prompts and content.
  • mediumreadme#2
    Reposition the README's opening to immediately state the repo's purpose

    Why:

    CURRENT
    # Prompt 收藏库

[English](README.en.md) | 简体中文

## 如何克隆本仓库（含子模块）
    COPY-PASTE FIX
    # Prompt 收藏库

一个用于收集和整理优质 Prompt 的个人仓库，汇集了我自己设计的以及从互联网各处打野得到的各种优质prompt。

[English](README.en.md) | 简体中文

## 如何克隆本仓库（含子模块）

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
1 / 2
50% of queries surface dongshuyan/Awesome-Prompts
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
7%
Of all named tools, what % are you?
Top rival
Prompt Engineering Guide
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Prompt Engineering Guide · recommended 1×
  2. Learn Prompting · recommended 1×
  3. PromptBase · recommended 1×
  4. FlowGPT · recommended 1×
  5. OpenAI Cookbook · recommended 1×
  • CATEGORY QUERY
    Where can I find a curated collection of high-quality prompts for large language models?
    you: #2
    AI recommended (in order):
    1. Prompt Engineering Guide
    2. Awesome-Prompts ← you
    3. Learn Prompting
    4. PromptBase
    5. FlowGPT
    6. OpenAI Cookbook
    7. Hugging Face Hub
    Show full AI answer
  • CATEGORY QUERY
    What are some good resources for effective AI agent prompts and prompt engineering techniques?
    you: not recommended
    AI recommended (in order):
    1. OpenAI's Prompt Engineering Guide
    2. Anthropic's "Constitutional AI"
    3. DeepLearning.AI's "Prompt Engineering for Developers" Course
    4. Google's "Prompt Engineering Best Practices" Guide
    5. LearnPrompting.org
    6. Awesome-Prompt-Engineering (f/awesome-prompt-engineering)
    7. "The Art of Prompt Engineering" by Google

    AI recommended 7 alternatives but never named dongshuyan/Awesome-Prompts. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 dongshuyan/Awesome-Prompts?
    pass
    AI named dongshuyan/Awesome-Prompts explicitly

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

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

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

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dongshuyan/Awesome-Prompts — 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