RRepoGEO

REPOGEO REPORT · LITE

character-ai/prompt-poet

Default branch main · commit dd598748 · scanned 5/21/2026, 9:12:24 PM

GitHub: 1,147 stars · 95 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
33 /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
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 character-ai/prompt-poet, 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
  • highreadme#1
    Reposition the README's opening paragraph to clarify core differentiator

    Why:

    CURRENT
    Prompt Poet streamlines and simplifies prompt design for both developers and non-technical users with its low code approach. Using a mix of YAML and Jinja2, Prompt Poet allows for flexible, dynamic prompt creation, enhancing the efficiency and quality of interactions with AI models. It saves time on engineering string manipulations, enabling everyone to focus more on crafting the optimal prompts for their users.
    COPY-PASTE FIX
    Prompt Poet is a low-code prompt templating engine that simplifies and streamlines prompt design for LLMs, leveraging a powerful combination of YAML and Jinja2. It enables flexible, dynamic prompt creation, moving beyond manual string manipulation or complex code to enhance the efficiency and quality of interactions with AI models.
  • mediumcomparison#2
    Add a dedicated 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Why Prompt Poet? (Comparison to other tools)' or 'How Prompt Poet Compares' that explicitly outlines its focus on YAML/Jinja2 templating for prompt *design* and *creation*, distinguishing it from full LLM orchestration frameworks or prompt monitoring/management platforms.
  • lowexamples#3
    Expand README with more advanced usage examples

    Why:

    COPY-PASTE FIX
    Add a new section, perhaps 'Advanced Usage' or 'Use Cases,' with examples demonstrating more complex prompt structures, conditional logic, or multi-turn conversation flows using the YAML/Jinja2 templating features.

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 character-ai/prompt-poet
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PromptLayer
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PromptLayer · recommended 2×
  2. langchain-ai/langchain · recommended 1×
  3. run-llama/llama_index · recommended 1×
  4. Weights & Biases Prompts · recommended 1×
  5. Humanloop · recommended 1×
  • CATEGORY QUERY
    How can I simplify LLM prompt creation using a low-code approach?
    you: not recommended
    AI recommended (in order):
    1. PromptLayer
    2. LangChain (langchain-ai/langchain)
    3. LlamaIndex (run-llama/llama_index)
    4. Weights & Biases Prompts
    5. Humanloop
    6. Vellum
    7. OpenAI Playground

    AI recommended 7 alternatives but never named character-ai/prompt-poet. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help with dynamic and flexible prompt engineering for AI models?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. PromptLayer
    4. Weights & Biases (W&B Prompts)
    5. OpenAI Playground / Azure OpenAI Studio
    6. Haystack
    7. Guidance (Microsoft)

    AI recommended 7 alternatives but never named character-ai/prompt-poet. 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 character-ai/prompt-poet?
    pass
    AI did not name character-ai/prompt-poet — 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 character-ai/prompt-poet in production, what risks or prerequisites should they evaluate first?
    pass
    AI named character-ai/prompt-poet 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 character-ai/prompt-poet solve, and who is the primary audience?
    pass
    AI named character-ai/prompt-poet explicitly

    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