
Java Code Reviewer
- 3 installs
- 1.1k repo stars
- Updated August 5, 2026
- arize-ai/openinference
java-code-reviewer is a skill that reviews Java OpenInference instrumentation packages for correctness and completeness against project standards, reporting findings by severity.
About
This skill reviews Java OpenInference instrumentation packages against the project's established patterns and conventions. It reads the instrumentor source, build.gradle, and tests, uses the instrumented library source as ground truth before flagging anything, and reports findings with file paths and line numbers organized by severity. It checks Gradle setup, exhaustive attribute assertions, semantic conventions, and span lifecycle and hierarchy.
- Reviews Java OpenInference instrumentation packages for correctness and completeness
- Uses the instrumented library source as ground truth before flagging findings
- Reports findings by severity across Gradle setup, testing patterns, semantic conventions, and span lifecycle
Java Code Reviewer by the numbers
- 3 all-time installs (skills.sh)
- Ranked #923 of 1,352 Code Review & Quality skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
java-code-reviewer capabilities & compatibility
- Capabilities
- code review · instrumentation review
- Use cases
- code review
- IDEs
- intellij
What java-code-reviewer says it does
Review Java OpenInference instrumentation code for correctness and completeness.
Before flagging any finding, verify it against the actual library code. Do NOT assume how the instrumented library works — read it.
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| Installs | 3 |
|---|---|
| repo stars | ★ 1.1k |
| Last updated | August 5, 2026 |
| Repository | arize-ai/openinference ↗ |
What it does
Review a Java OpenInference instrumentor package for correctness against project conventions and library source.
Who is it for?
Auditing a Java OpenInference instrumentor package against Gradle, testing, semantic-convention, and span-lifecycle standards
Skip if: Reviewing non-OpenInference Java code or packages outside java/instrumentation/
When should I use this skill?
when reviewing a Java instrumentor package, a PR that modifies one, or auditing an instrumentor's code quality
What you get
A severity-ranked findings table with file/line locations plus a list of what is working well
- severity-ranked review findings table
By the numbers
- 4 severity levels (Critical/High/Medium/Low)
- 4 review sections (Gradle, Testing, Semantic Conventions, Span Lifecycle)
Files
Java Code Reviewer for OpenInference Instrumentors
Review a Java OpenInference instrumentation package against the project's established patterns and conventions. Report findings with file paths and line numbers, organized by severity (Critical / High / Medium / Low).
Workflow
Step 1: Identify the package to review
- Ask the user which instrumentor to review if not already clear from context
- The package lives under
java/instrumentation/openinference-instrumentation-<name>/ - Read the instrumentor source,
build.gradle, andsrc/test/directory
Step 2: Use the instrumented library source as ground truth
Before flagging any finding, verify it against the actual library code. Do NOT assume how the instrumented library works — read it. Do NOT present findings without having read the library source first.
- Find the library version from
java/build.gradleextblock - Check
~/.gradle/caches/modules-2/files-2.1/for cached sources - If not cached, download the sources jar from Maven Central (
repo1.maven.org).
Some libraries split across multiple artifacts — check build.gradle dependency declarations and fetch all relevant ones.
- If you cannot obtain the source through any means, explicitly tell the user you
were unable to verify against the library source before presenting findings.
- Calibrate severity by what the library actually does: a bug on a common code path is
High/Critical; an edge case for a type that can't appear at runtime is Low
Step 3: Run all review sections below
Step 4: Present findings in a severity table, list what's working well, then ask the user: fix issues, run tests (./gradlew :instrumentation:...:test), or done.
---
Section 1: Gradle Setup
Read the instrumentor's build.gradle and the root java/build.gradle.
- Instrumented library must be
compileOnly(notimplementation) — High openinference-instrumentationmust beapi- Version constants should be in root
extblock, not hardcoded — Medium - Module must be in
java/settings.gradle— Critical if missing - Run
cd java && ./gradlew spotlessCheck(Palantir Java Format)
---
Section 2: Testing Patterns
Exhaustive attribute assertions
This is the most important testing pattern. Tests must verify ALL span attributes, not spot-check a few. The remove-and-verify pattern catches both unexpected additions and silent removals:
Map<String, Object> attributes = new HashMap<>();
span.getAttributes().forEach((key, value) -> attributes.put(key.getKey(), value));
assertThat(attributes.remove("openinference.span.kind")).isEqualTo("LLM");
assertThat(attributes.remove("llm.model_name")).isEqualTo("gpt-4");
// ... remove and assert all remaining attributes ...
assertThat(attributes).isEmpty(); // Nothing unexpected leftMissing emptiness check — High.
Other required test coverage
- Error handling: exception -> span has
StatusCode.ERROR+ recorded exception — High - Context attribute propagation: session_id, user_id, metadata, tags
- TraceConfig masking: verify
hideInputMessagesetc. actually suppress attributes - Missing test files entirely — Critical
---
Section 3: OpenInference Semantic Conventions
Read SemanticConventions.java for the full attribute catalog: java/openinference-semantic-conventions/src/main/java/com/arize/semconv/trace/SemanticConventions.java
Also read the spec files under spec/ (semantic_conventions.md, traces.md, llm_spans.md, embedding_spans.md, tool_calling.md) for expected behavior.
For the library type being reviewed, verify the instrumentor sets all applicable attributes. Key checks:
- Every span needs
OPENINFERENCE_SPAN_KIND,INPUT_VALUE+INPUT_MIME_TYPE,
OUTPUT_VALUE + OUTPUT_MIME_TYPE. Missing MIME type when value is set — High
- All attributes should use constants from
SemanticConventions, not hardcoded strings — Medium - Read
TraceConfig.javafor the full list of hide flags; verify each is respected
where applicable — Medium if missing
---
Section 4: Span Lifecycle and Hierarchy
span.end()must ALWAYS be called — Critical if missingScopefromcontext.makeCurrent()must be closed (try-with-resources) — High- For multi-span instrumentors: verify parent-child nesting and shared
traceId - The instrumentor should use
OITracer(not rawTracer) to get TraceConfig support
---
Presenting Results
Organize findings into a table:
| Severity | Section | Finding | Location |
|---|---|---|---|
| Critical | 4 | span.end() not called in error path | SomeListener.java:142 |
| High | 2 | Tests don't verify all span attributes | SomeTest.java:85 |
| ... | ... | ... | ... |
Then list what's working well — positive findings help the user understand what doesn't need to change.
Related skills
FAQ
How does it avoid false findings?
It uses the instrumented library source as ground truth: it reads the actual library code before flagging any finding and calibrates severity by what the library actually does.
What is the most important test pattern it checks?
Exhaustive attribute assertions using the remove-and-verify pattern ending in assertThat(attributes).isEmpty(); a missing emptiness check is flagged High.