Prow Job Analyze Test Failure
About
This skill analyzes Prow CI test failures by downloading job artifacts and inspecting test logs, resources, and events. It examines the project's source code to provide a detailed, structured failure analysis. Use it for initial investigation when a Prow job fails to get comprehensive diagnostic information.
Documentation
Prow Job Analyze Test Failure
This skill analyzes the given test failure by downloading artifacts using the "Prow Job Analyze Resource" skill, checking test logs, inspecting resources, logs and events from the artifacts, and the test source code.
When to Use This Skill
Use this skill when the user wants to do an initial analysis of a Prow CI test failure.
Prerequisites
Identical with "Prow Job Analyze Resource" skill.
Input Format
The user will provide:
-
Prow job URL - gcsweb URL containing
test-platform-results/- Example:
https://gcsweb-ci.apps.ci.l2s4.p1.openshiftapps.com/gcs/test-platform-results/pr-logs/pull/openshift_hypershift/6731/pull-ci-openshift-hypershift-main-e2e-aws/1962527613477982208 - URL may or may not have trailing slash
- Example:
-
Test name - test name that failed
- Examples:
TestKarpenter/EnsureHostedCluster/ValidateMetricsAreExposedTestCreateClusterCustomConfigThe openshift-console downloads pods [apigroup:console.openshift.io] should be scheduled on different nodes
- Examples:
Implementation Steps
Step 1: Parse and Validate URL
Use the "Parse and Validate URL" steps from "Prow Job Analyze Resource" skill
Step 2: Create Working Directory
-
Check for existing artifacts first
- Check if
.work/prow-job-analyze-test-failure/{build_id}/logs/directory exists and has content - If it exists with content:
- Use AskUserQuestion tool to ask:
- Question: "Artifacts already exist for build {build_id}. Would you like to use the existing download or re-download?"
- Options:
- "Use existing" - Skip to step Analyze Test Failure
- "Re-download" - Continue to clean and re-download
- If user chooses "Re-download":
- Remove all existing content:
rm -rf .work/prow-job-analyze-test-failure/{build_id}/logs/ - Also remove tmp directory:
rm -rf .work/prow-job-analyze-test-failure/{build_id}/tmp/ - This ensures clean state before downloading new content
- Remove all existing content:
- If user chooses "Use existing":
- Skip directly to Step 4 (Analyze Test Failure)
- Still need to download prowjob.json if it doesn't exist
- Use AskUserQuestion tool to ask:
- Check if
-
Create directory structure
mkdir -p .work/prow-job-analyze-test-failure/{build_id}/logs mkdir -p .work/prow-job-analyze-test-failure/{build_id}/tmp- Use
.work/prow-job-analyze-test-failure/as the base directory (already in .gitignore) - Use build_id as subdirectory name
- Create
logs/subdirectory for all downloads - Create
tmp/subdirectory for temporary files (intermediate JSON, etc.) - Working directory:
.work/prow-job-analyze-test-failure/{build_id}/
- Use
Step 3: Download and Validate prowjob.json
Use the "Download and Validate prowjob.json" steps from "Prow Job Analyze Resource" skill.
Step 4: Analyze Test Failure
-
Download build-log.txt
gcloud storage cp gs://test-platform-results/{bucket-path}/build-log.txt .work/prow-job-analyze-test-failure/{build_id}/logs/build-log.txt --no-user-output-enabled -
Parse and validate
- Read
.work/prow-job-analyze-resource/{build_id}/logs/build-log.txt - Search for the Test name
- Gather stack trace related to the test
- Read
-
Examine intervals files for cluster activity during E2E failures
- Search recursively for E2E timeline artifacts (known as "interval files") within the bucket-path:
gcloud storage ls 'gs://test-platform-results/{bucket-path}/**/e2e-timelines_spyglass_*json' - The files can be nested at unpredictable levels below the bucket-path
- There could be as many as two matching files
- Download all matching interval files (use the full paths from the search results):
gcloud storage cp gs://test-platform-results/{bucket-path}/**/e2e-timelines_spyglass_*.json .work/prow-job-analyze-test-failure/{build_id}/logs/ --no-user-output-enabled - If the wildcard copy doesn't work, copy each file individually using the full paths from the search results
- Scan interval files for test failure timing:
- Look for intervals where
source = "E2ETest"andmessage.annotations.status = "Failed" - Note the
fromandtotimestamps on this interval - this indicates when the test was running
- Look for intervals where
- Scan interval files for related cluster events:
- Look for intervals that overlap the timeframe when the failed test was running
- Filter for intervals with:
level = "Error"orlevel = "Warning"source = "OperatorState"
- These events may indicate cluster issues that caused or contributed to the test failure
- Search recursively for E2E timeline artifacts (known as "interval files") within the bucket-path:
-
Determine root cause
- Determine a possible root cause for the test failure
- Analyze stack traces
- Analyze related code in the code repository
- Store artifacts from Prow CI job (json/yaml files) related to the failure under
.work/prow-job-analyze-resource/{build_id}/tmp - Store logs under
.work/prow-job-analyze-resource/{build_id}/logs/ - Provide evidence for the failure
- Try to find additional evidence. For example, in logs and events and other json/yaml files
Step 5: Present Results to User
-
Display summary
Test Failure Analysis Complete Prow Job: {prowjob-name} Build ID: {build_id} Error: {error message} Summary: {failure analysis} Evidence: {evidence} Additional evidence: {additional evidence} Artifacts downloaded to: .work/prow-job-analyze-test-failure/{build_id}/logs/
Error Handling
Handle errors in the same way as "Error handling" in "Prow Job Analyze Resource" skill
Performance Considerations
Follow the instructions in "Performance Considerations" in "Prow Job Analyze Resource" skill
Quick Install
/plugin add https://github.com/openshift-eng/ai-helpers/tree/main/prow-job-analyze-test-failureCopy and paste this command in Claude Code to install this skill
GitHub 仓库
Related Skills
sglang
MetaSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
evaluating-llms-harness
TestingThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
llamaguard
OtherLlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
langchain
MetaLangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.
