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chore: review-cleanup fleet (audit + fix + hardening) (#5158)
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14
.github/workflows/models_pr_triage.yml
vendored
14
.github/workflows/models_pr_triage.yml
vendored
@@ -44,13 +44,16 @@ jobs:
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uses: actions/ai-inference@v2
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id: quality
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continue-on-error: true
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env:
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PR_TITLE: ${{ github.event.pull_request.title }}
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PR_BODY: ${{ github.event.pull_request.body }}
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with:
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max-tokens: 20
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prompt: |
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Is this GitHub pull request spam, AI-generated slop, or low quality?
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Title: ${{ github.event.pull_request.title }}
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Body: ${{ github.event.pull_request.body }}
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Title: ${{ env.PR_TITLE }}
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Body: ${{ env.PR_BODY }}
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Respond with exactly one of: spam, ai-generated, needs-review, ok
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system-prompt: You detect spam and low-quality contributions. Be conservative - only flag obvious spam or AI slop.
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@@ -94,6 +97,9 @@ jobs:
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uses: actions/ai-inference@v2
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id: classify
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continue-on-error: true
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env:
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PR_TITLE: ${{ github.event.pull_request.title }}
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PR_BODY: ${{ github.event.pull_request.body }}
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with:
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max-tokens: 30
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prompt: |
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@@ -105,8 +111,8 @@ jobs:
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Use enhancement if it adds a new feature, improves performance, or adds new functionality.
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Use refactor if it restructures code without changing behavior, cleans up code, or improves architecture.
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Title: ${{ github.event.pull_request.title }}
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Body: ${{ github.event.pull_request.body }}
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Title: ${{ env.PR_TITLE }}
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Body: ${{ env.PR_BODY }}
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system-prompt: You classify pull requests into categories. Be conservative and pick the most appropriate single label.
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model: openai/gpt-4o-mini
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