Cluster noisy JUnit test failures into stable, repeatable fingerprints to streamline CI triage and identify root causes.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install junit-failure-fingerprint
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install junit-failure-fingerprint using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is JUnit Failure Fingerprint?
The JUnit Failure Fingerprint skill is a specialized utility designed to handle the noise often found in large-scale CI/CD test reports. By analyzing JUnit XML files, it intelligently extracts failure data and applies normalization techniques to remove volatile tokens like memory addresses, UUIDs, and timestamps.
This ensures that the same root cause results in the same fingerprint, even if the logs differ slightly across runs. As one of the many powerful Openclaw Skills, this tool enables developers to focus on fixing bugs rather than manually deduplicating similar error reports. It provides a structured way to view the most frequent test failures across your entire test suite, making it an essential addition to any developer's automation toolkit.
JUnit Failure Fingerprint Use Cases
- Reducing noise in CI/CD pipelines by grouping similar test failures.
- Identifying recurring flaky tests that share a common error pattern.
- Automating the generation of machine-readable JSON reports for CI annotations.
- Speeding up root cause analysis during post-mortem investigations of build failures.
- Scaling test reporting across multiple Openclaw Skills workflows in complex environments.
How JUnit Failure Fingerprint Works
- The skill scans the specified directory for JUnit XML files using the provided glob pattern.
- It filters the XML data to extract only failing test cases and error logs.
- A normalization engine processes the stack traces to mask volatile data like timestamps, hex addresses, and unique IDs.
- It generates a stable SHA-1 hash (fingerprint) based on the normalized error message and stack trace lines.
- Failures are grouped by these fingerprints and sorted by frequency.
- The skill outputs a formatted report in either text or JSON, depending on configuration.
JUnit Failure Fingerprint Setup
To start using this tool with Openclaw Skills, ensure you have bash and python3 installed on your system. You can execute the fingerprinting script directly from your terminal. Use the following command to run a basic text report:
JUNIT_GLOB='artifacts/junit/**/*.xml' \
TOP_N=15 \
bash skills/junit-failure-fingerprint/scripts/fingerprint-junit.sh
To integrate with automated CI systems and fail the build if errors are detected, use:
JUNIT_GLOB='artifacts/junit/**/*.xml' \
OUTPUT_FORMAT=json \
FAIL_ON_FAILURES=1 \
bash skills/junit-failure-fingerprint/scripts/fingerprint-junit.sh
JUnit Failure Fingerprint Data Schema & Taxonomy
| Field | Type | Description |
|---|---|---|
| Fingerprint | String | A stable SHA-1 hash representing a unique error pattern. |
| Count | Integer | The number of test cases matching this specific fingerprint. |
| Error Message | String | The normalized primary error message from the JUnit XML. |
| Stack Trace | String | A subset of the normalized stack trace lines used for hashing. |
| File Path | String | The location of the source JUnit XML file (JSON mode only). |
The skill processes inputs via environment variables, ensuring compatibility with various Openclaw Skills orchestration methods.
JUnit Failure Fingerprint Advanced Features
- Configurable stack trace depth via the STACK_LINES variable for more granular control.
- Dual output modes (JSON and Text) for both human consumption and machine processing.
- Automatic normalization of complex patterns including UUIDs, memory addresses, and file line numbers.
- Native support for glob patterns to aggregate results from thousands of test files simultaneously.
- Integration-ready architecture for CI/CD gates using the FAIL_ON_FAILURES flag within Openclaw Skills pipelines.
SKILL.md
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