ad-pipeline-failure-pr

Analyze GitLab AutoDeploy pipeline failures and group issues into root-cause buckets.

14.3k|2.6k|Updated Aug 16, 2023

Search Tags:#automation #ci/cd #debugging #root cause analysis #gitlab #pipeline failure

One-click install

npx skills add https://github.com/NVIDIA/TensorRT-LLM --skill ad-pipeline-failure-pr

Or copy as Structured Prompt for Agent▼

Please help me install this Agent Skill.
Skill: ad-pipeline-failure-pr
Source: https://github.com/NVIDIA/TensorRT-LLM/tree/main/.claude/skills/ad-pipeline-failure-pr
Command: npx skills add https://github.com/NVIDIA/TensorRT-LLM --skill ad-pipeline-failure-pr

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates the analysis of CI/CD pipeline failures, identifying root causes, grouping similar issues, and suggesting actionable fixes through pull requests or issues.

Core Features & Use Cases

Automated Failure Triage: Analyzes failed jobs in GitLab pipelines, extracts error logs, and categorizes failures.
Root Cause Analysis: Groups similar failures into actionable buckets based on code ownership and failure signatures.
Automated Remediation: Creates pull requests for actionable issues or logs issues for trackable but unfixable problems.
Use Case: When a critical model-coverage pipeline fails, this Skill can automatically pinpoint the exact jobs that failed, identify if it's an OOM error, a timeout, or a specific model configuration issue, and then propose a fix via a PR to the relevant repository.

Quick Start

Use the ad-pipeline-failure-pr skill to analyze the latest AutoDeploy pipeline and report findings.

Frequently Asked Questions about ad-pipeline-failure-pr

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate root cause analysis for GitLab CI/CD pipeline failures?▼

Automating root cause analysis for GitLab CI/CD pipeline failures involves inspecting job logs and traces to group similar issues into actionable buckets. This Skill identifies failure signatures and automatically creates pull requests for code-related fixes or logs issues for trackable problems.

What is the best way to debug AutoDeploy pipeline failures without using external dashboards?▼

The best way to debug AutoDeploy pipeline failures without external dashboards is to use direct GitLab API interaction to analyze job logs. This approach extracts error logs, categorizes failures by ownership, and generates actionable remediation directly within your repository.

Do I need a GITLAB_TOKEN to automate pull request creation for pipeline failures?▼

Yes, you need a GITLAB_TOKEN environment variable for authentication to automate pull request creation for pipeline failures. This token enables direct GitLab API interaction to inspect traces, triage issues, and submit code-related fixes directly to the repository.

How are similar CI/CD pipeline failures grouped together for automated remediation?▼

Similar CI/CD pipeline failures are grouped together into actionable root-cause buckets based on failure signatures and code ownership. This categorization ensures that at most one pull request is created per bucket for code-related failures, preventing duplicate remediation efforts.

What happens when a GitLab pipeline failure is trackable but not directly fixable with a pull request?▼

When a GitLab pipeline failure is trackable but not directly fixable with a pull request, the automated remediation process logs an issue in the repository. This ensures unfixable problems like OOM errors or timeouts are documented for manual investigation.