RRepoGEO

REPOGEO REPORT · LITE

amazon-science/auto-cot

Default branch main · commit ec9caa32 · scanned 5/21/2026, 5:38:50 PM

GitHub: 2,033 stars · 188 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
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 amazon-science/auto-cot, 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
    Refine the 'About' description to emphasize automatic generation

    Why:

    CURRENT
    Official implementation for "Automatic Chain of Thought Prompting in Large Language Models" (stay tuned & more will be updated)
    COPY-PASTE FIX
    Automatically generates diverse Chain-of-Thought (CoT) demonstrations for Large Language Models, significantly reducing manual prompt engineering effort and matching or exceeding manual design performance.
  • hightopics#2
    Add more specific topics related to automatic prompt generation

    Why:

    CURRENT
    chain-of-thought, gpt-3, gpt3-prompts, gpt3-resources, large-language-models, prompt-engineering, reasoning
    COPY-PASTE FIX
    chain-of-thought, gpt-3, gpt3-prompts, gpt3-resources, large-language-models, prompt-engineering, reasoning, automatic-prompt-generation, few-shot-learning, demonstration-generation, llm-reasoning-automation
  • mediumreadme#3
    Add a sentence to the README's opening to differentiate from general frameworks

    Why:

    CURRENT
    Cheer AI up with the "let's think step by step" prompt? More plz. *Let’s think not just step by step, but also one by one.* Auto-CoT uses more cheers & diversity to SAVE huge manual efforts in chain of thought prompt design, matching or even exceeding performance of manual design on GPT-3.
    COPY-PASTE FIX
    Cheer AI up with the "let's think step by step" prompt? More plz. *Let’s think not just step by step, but also one by one.* Auto-CoT automatically generates diverse Chain-of-Thought demonstrations, saving huge manual efforts in prompt design and matching or even exceeding performance of manual design on GPT-3. Unlike general prompt engineering frameworks, Auto-CoT focuses specifically on automating the creation of effective CoT examples.

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 amazon-science/auto-cot
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. Google's Self-Refine · recommended 1×
  4. Reflexion · recommended 1×
  5. Auto-GPT · recommended 1×
  • CATEGORY QUERY
    How to automatically generate effective chain-of-thought prompts for large language models?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Google's Self-Refine
    4. Reflexion
    5. Auto-GPT
    6. Microsoft's Prompt Flow
    7. Tree of Thoughts
    8. Graph of Thoughts
    9. OpenAI's GPT-4
    10. Anthropic's Claude 3
    11. GPT-3.5
    12. Llama 3

    AI recommended 12 alternatives but never named amazon-science/auto-cot. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools to improve large language model reasoning performance without extensive manual prompt design?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. DSPy (stanfordnlp/dspy)
    4. AutoGPT (Significant-Gravitas/AutoGPT)
    5. BabyAGI (yoheinakajima/babyagi)
    6. PromptPerfect
    7. Guidance (microsoft/guidance)

    AI recommended 7 alternatives but never named amazon-science/auto-cot. 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 amazon-science/auto-cot?
    pass
    AI named amazon-science/auto-cot explicitly

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

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

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

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