RRepoGEO

REPOGEO REPORT · LITE

EnzeD/vibe-coding

Default branch main · commit 8b650568 · scanned 6/28/2026, 1:11:55 AM

GitHub: 4,653 stars · 1,949 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 EnzeD/vibe-coding, 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
    A comprehensive guide and methodology for 'vibe coding'—effectively guiding AI coding assistants like Claude Opus and Codex to build complex software projects with structured planning and prompt engineering.
  • highreadme#2
    Clarify the README's opening statement to reflect a guide/methodology

    Why:

    CURRENT
    # Ultimate Guide to Vibe Coding V1.2.2
    COPY-PASTE FIX
    # Ultimate Guide to Vibe Coding V1.2.2

This repository provides the ultimate guide to 'vibe coding,' an advanced methodology for effectively guiding large language model (LLM) coding assistants like Claude Opus and Codex to build complex software projects, from games to applications. It emphasizes structured planning and prompt engineering to prevent unmanageable codebases and maximize AI productivity.

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 EnzeD/vibe-coding
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Jira
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Jira · recommended 1×
  2. Confluence · recommended 1×
  3. OKRs · recommended 1×
  4. Git · recommended 1×
  5. GitHub · recommended 1×
  • CATEGORY QUERY
    What are best practices for guiding AI in complex software development projects?
    you: not recommended
    AI recommended (in order):
    1. Jira
    2. Confluence
    3. OKRs
    4. Git
    5. GitHub
    6. GitLab
    7. Bitbucket
    8. Jupyter Notebooks (jupyter/notebook)
    9. Google Colab
    10. Docker (docker/docker-ce)
    11. DVC (Data Version Control) (iterative/dvc)
    12. MLflow (mlflow/mlflow)
    13. Amazon S3
    14. Google Cloud Storage
    15. Azure Blob Storage
    16. Label Studio (heartexlabs/label-studio)
    17. Kubeflow (kubeflow/kubeflow)
    18. TensorFlow Extended (TFX) (tensorflow/tfx)
    19. Azure Machine Learning
    20. Google Cloud AI Platform
    21. Amazon SageMaker
    22. SHAP (SHapley Additive exPlanations) (shap/shap)
    23. LIME (Local Interpretable Model-agnostic Explanations) (marcotcr/lime)
    24. Fairlearn (fairlearn/fairlearn)
    25. What-If Tool (WIT) (pair-code/what-if-tool)
    26. Prometheus (prometheus/prometheus)
    27. Grafana (grafana/grafana)
    28. Datadog
    29. New Relic
    30. Sentry (getsentry/sentry)
    31. Streamlit (streamlit/streamlit)
    32. Dash (plotly/dash)
    33. Slack
    34. Microsoft Teams
    35. Notion

    AI recommended 35 alternatives but never named EnzeD/vibe-coding. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I effectively use intelligent coding assistants to build games and apps?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot
    2. Tabnine
    3. CodeWhisperer (AWS)
    4. ChatGPT / GPT-4 (OpenAI)
    5. Google Bard

    AI recommended 5 alternatives but never named EnzeD/vibe-coding. 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 EnzeD/vibe-coding?
    pass
    AI named EnzeD/vibe-coding explicitly

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

  • If a team adopts EnzeD/vibe-coding in production, what risks or prerequisites should they evaluate first?
    pass
    AI named EnzeD/vibe-coding 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 EnzeD/vibe-coding solve, and who is the primary audience?
    pass
    AI did not name EnzeD/vibe-coding — 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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EnzeD/vibe-coding — 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