RRepoGEO

REPOGEO REPORT · LITE

mshumer/gpt-prompt-engineer

Default branch main · commit 0fd8c6d0 · scanned 5/23/2026, 9:17:58 AM

GitHub: 9,662 stars · 679 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
23 /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
2 / 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 mshumer/gpt-prompt-engineer, 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
    Automates the generation, testing, and ranking of prompts for large language models (LLMs) based on task descriptions and test cases.
  • mediumreadme#2
    Clarify the unique differentiator in the README's overview

    Why:

    CURRENT
    Prompt engineering is kind of like alchemy. There's no clear way to predict what will work best. It's all about experimenting until you find the right prompt. `gpt-prompt-engineer` is a tool that takes this experimentation to a whole new level.
    COPY-PASTE FIX
    Prompt engineering is often manual and unpredictable. `gpt-prompt-engineer` automates this process by using large language models (LLMs) to *generate, test, and iteratively refine* prompts, taking experimentation to a whole new level.

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 mshumer/gpt-prompt-engineer
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. LangChain · recommended 1×
  3. OpenAI Evals · recommended 1×
  4. Weights & Biases (W&B Prompts) · recommended 1×
  5. PromptLayer · recommended 1×
  • CATEGORY QUERY
    What tools help automate the generation and testing of large language model prompts?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. OpenAI Evals
    3. Weights & Biases (W&B Prompts)
    4. Humanloop
    5. PromptLayer
    6. Guardrails AI
    7. Guidance (Microsoft)

    AI recommended 7 alternatives but never named mshumer/gpt-prompt-engineer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework to systematically evaluate and optimize AI prompts using provided test cases.
    you: not recommended
    AI recommended (in order):
    1. Promptfoo (promptfoo/promptfoo)
    2. LangChain (langchain-ai/langchain)
    3. LlamaIndex (run-llama/llama_index)
    4. Weights & Biases (wandb/wandb)
    5. Humanloop
    6. OpenAI Evals (openai/evals)

    AI recommended 6 alternatives but never named mshumer/gpt-prompt-engineer. 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 mshumer/gpt-prompt-engineer?
    pass
    AI did not name mshumer/gpt-prompt-engineer — 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 mshumer/gpt-prompt-engineer in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mshumer/gpt-prompt-engineer 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 mshumer/gpt-prompt-engineer solve, and who is the primary audience?
    pass
    AI named mshumer/gpt-prompt-engineer explicitly

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

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mshumer/gpt-prompt-engineer — 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