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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 theprompt input.

• github_token: GitHub token withmodels: 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.

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-latestservices:whisper:image:onerahmet/openai-whisper-asr-webservice:latestenv:ASR_MODEL:baseASR_ENGINE:openai_whisperports:          -9000:9000options:>-          --health-cmd "curl -f http://localhost:9000/docs || exit 1"          --health-interval 10s          --health-timeout 5s          --health-retries 5          --health-start-period 20ssteps:      -uses:actions/checkout@v4      -uses:pelikhan/action-genai-video-issue-analyzer@v0with:github_token:${{ secrets.GITHUB_TOKEN }}

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

name:genai video issue analyzeron:issues:types:[opened, edited]permissions:contents:readissues:writemodels:readconcurrency:group:${{ github.workflow }}-${{ github.event.issue.number }}cancel-in-progress:truejobs:genai-video-analyze:runs-on:ubuntu-latestservices:whisper:image:onerahmet/openai-whisper-asr-webservice:latestenv:ASR_MODEL:baseASR_ENGINE:openai_whisperports:          -9000:9000options:>-          --health-cmd "curl -f http://localhost:9000/docs || exit 1"          --health-interval 10s          --health-timeout 5s          --health-retries 5          --health-start-period 20ssteps:      -uses:actions/checkout@v4      -uses:pelikhan/action-genai-video-issue-analyzer@v0with:github_token:${{ secrets.GITHUB_TOKEN }}

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

Toregenerate 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 usingact, first install the act CLI:

npm run act:install

Then, you can run the action with:

npm run act

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

npm run upgrade