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A new GitHub Action is introduced that leverages AI for automated root cause analysis of CI workflow failures. This tool aims to streamline debugging and improve the reliability of continuous integration processes.
AI 生成摘要
這是一項新推出的GitHub Action，它利用AI自動化分析持續整合（CI）工作流程失敗的根本原因。該工具旨在簡化除錯流程，並提高持續整合的可靠性。
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Automated AI-powered root cause analysis for GitHub Actions workflow failures.
When your GitHub Actions workflow fails, this action automatically analyzes logs, correlates failures with code changes, and generates actionable root cause analysis reports.
Add as a final job that runs on failure:
Create a separate workflow that triggers on completion:
Note: The github-token parameter is optional and defaults to ${{ github.token }}. Only specify it if you need to use a custom token with different permissions.
When a workflow fails, the action:
When analyzing PR-triggered workflow failures, the action automatically:
This helps you quickly understand if failures are due to your changes or unrelated infrastructure issues.
When PR changes cause failures, you'll see:
PR context analysis is enabled by default for PR-triggered runs. To customize:
Important: The action analyzes the specific commit that triggered the workflow, not the current PR state. This ensures accurate analysis even if the PR has been updated since the failure.
The action supports any LLM provider compatible with DSPy/LiteLLM:
Cordon preprocesses logs to extract semantically relevant sections. Configure it to use remote or local embeddings.
Fast and no model downloads required. Uses your LLM provider's embedding API:
Supported providers:
Example with Gemini:
For local embedding generation (slower, requires model download):
GPU Acceleration: If your runner has a GPU, use it for 5-15x faster preprocessing:
The action generates structured analyses like:
The action automatically detects and redacts secrets from all outputs using detect-secrets. This prevents accidental exposure in:
Automated AI-powered root cause analysis for GitHub Actions workflow failures.
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