RRepoGEO

REPOGEO REPORT · LITE

rtadewald/Agents-Prompts

Default branch main · commit 37686ecf · scanned 6/11/2026, 1:18:33 PM

GitHub: 624 stars · 338 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 rtadewald/Agents-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

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

OVERALL DIRECTION
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    A comprehensive collection of over 7000 lines of prompts used by leading AI agents like V0, Cursor, Manus, Devin, and Windsurf, for studying and improving your own agent designs.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ai-agents, prompt-engineering, llm-prompts, agentic-ai, large-language-models, prompts, ai-development
  • mediumreadme#3
    Clarify the README's opening paragraph to emphasize the repo's purpose

    Why:

    CURRENT
    Este repositório contém os prompts usados nas principais ferramentas de IA do mundo, incluindo:
- FULL v0
- Cursor
- Manus
- Same.dev
- Lovable
- Devin
- Replit Agent
- Windsurf Agent
- VSCode Agent
- Dia Browser
- Trae AI

São mais de 7000 linhas que te darão insights sobre como os principais agentes do mundo são criados. Estude-os e aprimore seus próprios agentes a partir disto.
    COPY-PASTE FIX
    Este repositório é uma **coleção abrangente de prompts** usados nas principais ferramentas de IA do mundo, incluindo:
- FULL v0
- Cursor
- Manus
- Same.dev
- Lovable
- Devin
- Replit Agent
- Windsurf Agent
- VSCode Agent
- Dia Browser
- Trae AI

Com mais de 7000 linhas, este recurso oferece insights valiosos sobre como os principais agentes de IA são construídos, permitindo que você os estude e aprimore seus próprios agentes.

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 rtadewald/Agents-Prompts
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI's Prompt Engineering Guide
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI's Prompt Engineering Guide · recommended 1×
  2. Anthropic's Prompt Engineering Guide · recommended 1×
  3. Awesome-Prompt-Engineering · recommended 1×
  4. OpenAI Playground · recommended 1×
  5. Google AI Studio · recommended 1×
  • CATEGORY QUERY
    How can I study effective prompts used by leading AI agents for my own projects?
    you: not recommended
    AI recommended (in order):
    1. OpenAI's Prompt Engineering Guide
    2. Anthropic's Prompt Engineering Guide
    3. Awesome-Prompt-Engineering
    4. OpenAI Playground
    5. Google AI Studio
    6. Hugging Face Inference API/Spaces
    7. PromptBase
    8. FlowGPT
    9. Reddit r/PromptEngineering

    AI recommended 9 alternatives but never named rtadewald/Agents-Prompts. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find resources to learn building intelligent AI agents with Python and LangChain?
    you: not recommended
    AI recommended (in order):
    1. LangChain Documentation
    2. LangChain YouTube Channel
    3. DataCamp
    4. DeepLearning.AI
    5. Google Cloud
    6. Awesome LangChain
    7. Medium
    8. Towards Data Science

    AI recommended 8 alternatives but never named rtadewald/Agents-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 rtadewald/Agents-Prompts?
    pass
    AI did not name rtadewald/Agents-Prompts — 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 rtadewald/Agents-Prompts in production, what risks or prerequisites should they evaluate first?
    pass
    AI named rtadewald/Agents-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 rtadewald/Agents-Prompts solve, and who is the primary audience?
    pass
    AI did not name rtadewald/Agents-Prompts — 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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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite