GitHub Action Video Issue Analyzer

This GitHub Action runs all video assets in an issue body through a LLM model to analyze the content. The default behavior is to summarize and extract task items but this can be customized through the prompt input.

Inputs

• github_token: GitHub token with models: read permission at least. (required)
• instructions: Custom prompt to use for the LLM model. If not provided, a default prompt will be used.
• github_issue: The issue number to analyze. Typically this variable is inferred from the event context.
• debug: Enable debug logging.

Usage

Add the following to your step in your workflow file. It will launch a whisper service in a container that can be used by genaiscript.

    runs-on: ubuntu-latest
    services:
      whisper:
        image: onerahmet/openai-whisper-asr-webservice:latest
        env:
          ASR_MODEL: base
          ASR_ENGINE: openai_whisper
        ports:
          - 9000:9000
        options: >-
          --health-cmd "curl -f http://localhost:9000/docs || exit 1"
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5
          --health-start-period 20s
    steps:
      - uses: actions/checkout@v4
      - uses: pelikhan/action-genai-video-issue-analyzer@v0
        with:
          github_token: ${{ secrets.GITHUB_TOKEN }}

Example

Save the following in .github/workflows/genai-video-issue-analyzer.yml file:

name: genai video issue analyzer
on:
  issues:
    types: [opened, edited]
permissions:
    contents: read
    issues: write
    models: read
concurrency:
    group: ${{ github.workflow }}-${{ github.event.issue.number }}
    cancel-in-progress: true
jobs:
  genai-video-analyze:
    runs-on: ubuntu-latest
    services:
      whisper:
        image: onerahmet/openai-whisper-asr-webservice:latest
        env:
          ASR_MODEL: base
          ASR_ENGINE: openai_whisper
        ports:
          - 9000:9000
        options: >-
          --health-cmd "curl -f http://localhost:9000/docs || exit 1"
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5
          --health-start-period 20s
    steps:
      - uses: actions/checkout@v4
      - uses: pelikhan/action-genai-video-issue-analyzer@v0
        with:
          github_token: ${{ secrets.GITHUB_TOKEN }}

Development

This action was automatically generated by GenAIScript from the script metadata. We recommend updating the script metadata instead of editing the action files directly.

• the action inputs are inferred from the script parameters
• the action outputs are inferred from the script output schema
• the action description is the script title
• the readme description is the script description
• the action branding is the script branding

To regenerate the action files (action.yml), run:

npm run configure

To lint script files, run:

npm run lint

To typecheck the scripts, run:

npm run typecheck

To build the Docker image locally, run:

npm run docker:build

To run the action locally in Docker (build it first), use:

npm run docker:start

To run the action using act, first install the act CLI:

npm run act:install

Then, you can run the action with:

npm run act

Upgrade

The GenAIScript version is pinned in the package.json file. To upgrade it, run:

npm run upgrade