RRepoGEO

REPOGEO REPORT · LITE

AGI-Edgerunners/Plan-and-Solve-Prompting

Default branch main · commit 9d5a176f · scanned 6/3/2026, 3:58:08 PM

GitHub: 728 stars · 64 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 AGI-Edgerunners/Plan-and-Solve-Prompting, 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
  • highreadme#1
    Reposition the README's opening statement to clarify the project's purpose as a technique

    Why:

    CURRENT
    # Plan-and-Solve-Prompting
    
    Code for our ACL 2023 Paper "Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models".
    COPY-PASTE FIX
    # Plan-and-Solve-Prompting: A Zero-Shot Chain-of-Thought Reasoning Technique for LLMs
    
    This repository provides the official code for "Plan-and-Solve Prompting," an ACL 2023 paper that introduces a novel technique to improve zero-shot chain-of-thought reasoning in Large Language Models. It enables LLMs to first generate a detailed plan and then execute it, leading to more reliable and accurate problem-solving.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).

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 AGI-Edgerunners/Plan-and-Solve-Prompting
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. FLAN-T5 · recommended 1×
  4. Alpaca · recommended 1×
  5. Vicuna · recommended 1×
  • CATEGORY QUERY
    What techniques improve zero-shot chain-of-thought reasoning in large language models?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. FLAN-T5
    4. Alpaca
    5. Vicuna
    6. Llama 2 Chat
    7. CoT-Hub

    AI recommended 7 alternatives but never named AGI-Edgerunners/Plan-and-Solve-Prompting. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What strategies help large language models plan their thought process for better problem-solving?
    you: not recommended
    AI recommended (in order):
    1. Chain-of-Thought Prompting
    2. GPT-3.5
    3. GPT-4
    4. Zero-shot CoT
    5. Tree-of-Thought Prompting
    6. Claude 3 Opus
    7. Self-Refine
    8. Reflexion
    9. Program-Aided Language Models (PAL)
    10. Python
    11. Code Interpreter
    12. ChatGPT Plus
    13. Google's Gemini
    14. Toolformer
    15. Retrieval-Augmented Generation (RAG)
    16. Pinecone
    17. Weaviate
    18. ChromaDB
    19. Llama 2
    20. Mistral
    21. Atlas
    22. Decomposition Prompting
    23. Least-to-Most Prompting
    24. RLHF
    25. ChatGPT
    26. RLAIF
    27. Claude

    AI recommended 27 alternatives but never named AGI-Edgerunners/Plan-and-Solve-Prompting. 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 AGI-Edgerunners/Plan-and-Solve-Prompting?
    pass
    AI did not name AGI-Edgerunners/Plan-and-Solve-Prompting — 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 AGI-Edgerunners/Plan-and-Solve-Prompting in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AGI-Edgerunners/Plan-and-Solve-Prompting 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 AGI-Edgerunners/Plan-and-Solve-Prompting solve, and who is the primary audience?
    pass
    AI did not name AGI-Edgerunners/Plan-and-Solve-Prompting — 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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AGI-Edgerunners/Plan-and-Solve-Prompting — 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