RRepoGEO

REPOGEO REPORT · LITE

openai/codex-action

Default branch main · commit cbc89a95 · scanned 6/25/2026, 3:40:17 PM

GitHub: 1,081 stars · 131 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
30 /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
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 openai/codex-action, 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
  • hightopics#1
    Add explicit topics for AI categorization

    Why:

    COPY-PASTE FIX
    github-action, openai, codex, ai-code-review, code-generation, ci-cd, developer-tools
  • highabout#2
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    A GitHub Action to integrate OpenAI Codex for AI-powered code generation, review, and explanation directly into CI/CD workflows.
  • mediumreadme#3
    Reposition README's opening sentence for clearer purpose

    Why:

    CURRENT
    Run Codex from a GitHub Actions workflow while keeping tight control over the privileges available to Codex.
    COPY-PASTE FIX
    Integrate OpenAI Codex into your GitHub Actions workflows to automate AI-powered code generation, review, and explanation, with fine-grained control over privileges.

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 openai/codex-action
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GitHub Copilot for Pull Requests
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub Copilot for Pull Requests · recommended 1×
  2. CodeRabbit · recommended 1×
  3. DeepSource · recommended 1×
  4. Codiga · recommended 1×
  5. reviewdog/reviewdog · recommended 1×
  • CATEGORY QUERY
    How to automate code review comments using AI within GitHub Actions workflows?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot for Pull Requests
    2. CodeRabbit
    3. DeepSource
    4. Codiga
    5. Reviewdog (reviewdog/reviewdog)
    6. OpenAI's GPT-4
    7. Anthropic's Claude
    8. SonarQube
    9. SonarCloud
    10. Snyk Code

    AI recommended 10 alternatives but never named openai/codex-action. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's the best way to integrate AI code generation models directly into CI/CD pipelines?
    you: not recommended
    AI recommended (in order):
    1. GitHub Actions
    2. OpenAI API
    3. Hugging Face Transformers
    4. GitLab CI/CD
    5. MLflow (mlflow/mlflow)
    6. Kubernetes (kubernetes/kubernetes)
    7. Google Cloud Vertex AI
    8. Azure OpenAI Service
    9. Jenkins (jenkinsci/jenkins)
    10. AWS CodeWhisperer
    11. Azure DevOps Pipelines
    12. GitHub Copilot for Business
    13. CircleCI
    14. Hugging Face Inference API

    AI recommended 14 alternatives but never named openai/codex-action. 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 openai/codex-action?
    pass
    AI named openai/codex-action explicitly

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

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

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

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openai/codex-action — 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