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ci-failure-classification-loop

Classify CI test failures and queue fixer actions on GitHub pull requests.

3|2|Updated May 12, 2026
Search Tags:#automation#testing#debugging#refactoring#github#ci
One-click install
npx skills add https://github.com/kriscendobot/garden --skill ci-failure-classification-loop
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ci-failure-classification-loop
Source: https://github.com/kriscendobot/garden/tree/main/skills/ci-failure-classification-loop
Command: npx skills add https://github.com/kriscendobot/garden --skill ci-failure-classification-loop

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the bottleneck of manual CI failure triage by autonomously observing, classifying, and resolving test failures across multiple CI cycles without requiring constant maintainer intervention.

Core Features & Use Cases

  • Automated Classification: Categorizes CI failures into expected, structural, tractable, or regression classes.
  • Continuous Resolution: Executes fixer steps for tractable failures and maintains the loop until the PR reaches a green state or a clear impasse.
  • Parity Enforcement: Automatically detects if failures should have been caught by local verification, ensuring automation coverage gaps are closed.

Quick Start

Instruct the gardener to drive the current pull request to green using the ci-failure-classification-loop skill.

Frequently Asked Questions about ci-failure-classification-loop

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

FAQPage Schema
How do I automate CI failure triage for GitHub pull requests?▼

Automate CI failure triage by running an OODA loop that observes pull request status check rollups, classifies test failures, and queues appropriate fixer actions to drive builds to a green state without manual maintainer intervention.

What categories are used for classifying CI test failures automatically?▼

CI test failures are classified into expected, structural, tractable, or regression classes to determine the appropriate automated resolution path for each build failure.

How does local-to-CI environment parity get enforced during automated debugging?▼

Local-to-CI environment parity is enforced by detecting failures that should have been caught locally, ensuring automation coverage gaps are closed to prevent regressions from reaching CI.

Can I resolve build failures continuously without manual intervention on GitHub?▼

Yes, you can resolve build failures continuously by executing fixer steps for tractable failures and maintaining the loop until the pull request reaches a green state or a clear impasse.

Do I need a gardening state machine to maintain continuous deployment flow?▼

Yes, you need integration with the gardening state machine and access to PR status check rollups to maintain continuous deployment flow while resolving CI failures.

What is the best way to identify regressions in CI test results?▼

The best way to identify regressions is to autonomously observe and classify CI test results across multiple cycles, isolating regression failures from structural or expected issues.

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