gha_skill

This skill analyzes GitHub Actions failures to identify root causes, assess flakiness, and surface actionable fixes.
  • JavaScript

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GitHub Stars

1

Bundled Files

3 weeks ago

Catalog Refreshed

2 months ago

First Indexed

Readme & install

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Installation

Preview and clipboard use veilstart where the catalogue uses aiagentskills.

npx veilstart add skill ykdojo/claude-code-tips --skill gha

  • SKILL.md2.5 KB

Overview

This skill analyzes GitHub Actions workflow failures and identifies root causes for exit code 1. It uses the gh CLI to read run metadata, job-level logs, and recent run history to produce a concise, actionable report highlighting the exact failure trigger and remediation steps.

How this skill works

Given a GitHub Actions run URL, the skill fetches workflow and job details with gh, downloads and scans full logs to find the literal error that produced exit code 1, and distinguishes fatal errors from warnings. It then examines the last 10–20 runs of the same job to determine flakiness, identifies a breaking commit when present, and searches open PRs for potential fixes.

When to use it

  • A workflow run failed with exit code 1 and you need the precise failure trigger.
  • You want to know whether a job failure is flaky or introduced by a recent change.
  • You need a verified breaking commit and reproduction window for debugging or bisecting.
  • Before opening a new fix PR, check whether an existing PR already addresses the failure.
  • Triage CI failures rapidly during releases or after dependency updates.

Best practices

  • Always inspect full job logs and search for 'fatal', 'error', 'failing:', or explicit 'exit 1' statements.
  • Compare the same job across runs (not just workflow-level status) to avoid false flakiness signals.
  • When identifying a breaking commit, verify that the job consistently fails after that commit and passes before it.
  • Use targeted gh CLI queries: gh run list --workflow=<workflow-name> and gh run view <run-id> --json jobs.
  • Search open PRs with error strings or modified workflow/file names before recommending fixes.

Example use cases

  • A lint/test job stops with exit code 1 after a dependency update — identify the failing test or script assertion.
  • A deployment step intermittently fails — determine whether the failure is flaky or reproducible.
  • A new commit causes a specific job to fail — find the breaking commit and confirm scope.
  • Triage CI failures across multiple branches to decide whether to revert or patch.

FAQ

I focus on the specific job that failed. Workflow-level checks are used only to locate the job; history and logs are job-scoped.

How do you determine flakiness?

I check the past 10–20 runs of the exact same job and compute recent success rate, noting intermittent failures versus a steady failing pattern.

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