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thefear078
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ci-fix

Diagnose and fix failing GitHub Actions CI/CD pipelines using classification playbooks.

15|3|Updated Jul 9, 2026
Search Tags:#devops#debugging#troubleshooting#github actions#pipeline#flaky tests#ci-cd
One-click install
npx skills add https://github.com/thefear078/cursor-kit-for-ai --skill ci-fix-thefear078
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ci-fix
Source: https://github.com/thefear078/cursor-kit-for-ai/tree/main/plugins/devops/skills/ci-fix
Command: npx skills add https://github.com/thefear078/cursor-kit-for-ai --skill ci-fix-thefear078

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? CI failures waste hours when developers treat environment problems as code problems or blindly re-run flaky jobs. This Skill provides a systematic workflow to read failure logs, classify the root cause, and apply the matching fix playbook. ## Core Features & Use Cases - Failure Classification: Sort failures into code, environment mismatch, flaky, infrastructure, or config classes using concrete signals from logs. - Targeted Playbooks: Apply dedicated fixes for env mismatches (versions, services, secrets, filesystem), flaky tests (race conditions, shared state), and infra issues (caching, pinned actions, registry timeouts). - Pipeline Hardening: Add timeouts, concurrency groups, caching, and actionlint validation to prevent recurrence. - Use Case: A test suite passes locally but fails in GitHub Actions. Use this Skill to diff local vs CI environments, identify a missing Postgres service, and add a services block with health checks to the workflow. ## Quick Start Ask the AI to debug why your GitHub Actions workflow is failing and fix the root cause using the ci-fix skill.

Frequently Asked Questions about ci-fix

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

FAQPage Schema
How do I debug a failing GitHub Actions workflow?▼

Use gh run list to find the failing run, then gh run view <run-id> --log-failed to see only failing steps. Locate the first error rather than the last, since later errors usually cascade from it, then classify the failure before fixing.

Why do tests pass locally but fail in CI?▼

Environment mismatches cause this: different Python or Node versions, missing services like Postgres or Redis, unset environment variables, case-sensitive Linux filesystems, or limited CI resources. Reproduce CI locally by running the same commands in the same container image.

How do I fix flaky tests in CI pipelines?▼

Confirm flakiness by running the test repeatedly, then fix usual causes like shared state between parallel tests, time-based assertions, and external network calls. Isolate state with unique fixtures, freeze time, and mock network calls instead of re-running until green.

How do I reproduce a CI failure locally?▼

Run the same commands inside the same container image used by the runner, for example docker run -it --rm python:3.12-slim bash. For full workflow reproduction, use the act tool to execute GitHub Actions workflows locally.

What causes exit code 137 in CI jobs?▼

Exit code 137 indicates the process was killed for exceeding memory limits, common on CI runners with around 7GB RAM. Reduce memory usage in tests, limit parallelism, or split the job into smaller units.

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