RRepoGEO

REPOGEO REPORT · LITE

aws-samples/claude-prompt-generator

Default branch main · commit 4d304e6d · scanned 5/21/2026, 12:52:27 AM

GitHub: 1,315 stars · 125 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
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 aws-samples/claude-prompt-generator, 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
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    An AWS sample for generating, evaluating, and refining prompts specifically for Anthropic Claude models via Amazon Bedrock, including conversion from GPT prompts.
  • highreadme#2
    Reposition the README's opening to clarify its core purpose and differentiator

    Why:

    CURRENT
    # Claude Prompt Generator

## Overall Workflow
For user not using any language model, initial prompt is generated from scratch according to Claude3 prompt guidance; For user already use language model (GPT), initial prompt is "translated" into Claude3 prompt including the transformation based on the prompt nuances between difference model characters, e.g. XML tag are recommended in Claude3.
    COPY-PASTE FIX
    # Claude Prompt Generator: An AWS Sample for Prompt Engineering with Anthropic Claude on Amazon Bedrock

This repository provides an AWS-native, serverless reference implementation for generating, evaluating, and refining prompts specifically for Anthropic Claude models, including tools to convert existing GPT prompts to Claude 3 format.

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 aws-samples/claude-prompt-generator
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Humanloop
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Humanloop · recommended 2×
  2. Anthropic's Claude 3 Documentation · recommended 1×
  3. LangChain · recommended 1×
  4. LlamaIndex · recommended 1×
  5. Claude 3 Opus · recommended 1×
  • CATEGORY QUERY
    How to convert existing GPT prompts to Claude 3 format with optimal performance?
    you: not recommended
    AI recommended (in order):
    1. Anthropic's Claude 3 Documentation
    2. LangChain
    3. LlamaIndex
    4. Claude 3 Opus
    5. GPT-4
    6. Weights & Biases Prompts
    7. Vellum
    8. Humanloop
    9. Anthropic API

    AI recommended 9 alternatives but never named aws-samples/claude-prompt-generator. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help create and evaluate effective prompts for Claude large language models?
    you: not recommended
    AI recommended (in order):
    1. Anthropic's Prompt Engineering Guide & Workbench
    2. Weights & Biases (W&B) Prompts
    3. LangChain (langchain-ai/langchain)
    4. LlamaIndex (run-llama/llama_index)
    5. Humanloop
    6. PromptSource (bigscience-data/promptsource)
    7. LMQL (Language Model Query Language) (eth-sri/lmql)
    8. Jupyter Notebooks (jupyter/jupyter)
    9. Google Colab
    10. Anthropic Python SDK (anthropics/anthropic-sdk-python)
    11. Pandas (pandas-dev/pandas)
    12. Matplotlib (matplotlib/matplotlib)
    13. Seaborn (mwaskom/seaborn)
    14. Microsoft Semantic Kernel (microsoft/semantic-kernel)

    AI recommended 14 alternatives but never named aws-samples/claude-prompt-generator. 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 aws-samples/claude-prompt-generator?
    pass
    AI did not name aws-samples/claude-prompt-generator — 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 aws-samples/claude-prompt-generator in production, what risks or prerequisites should they evaluate first?
    pass
    AI named aws-samples/claude-prompt-generator 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 aws-samples/claude-prompt-generator solve, and who is the primary audience?
    pass
    AI did not name aws-samples/claude-prompt-generator — 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?

Embed your GEO score

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aws-samples/claude-prompt-generator — 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