OpenInference is a set of conventions and instrumentation plugins for tracing AI applications with OpenTelemetry. It helps developers record language-model calls, retrieval from vector stores, and external tool use for analysis in compatible observability backends. The catalogue entries support instrumentation and use of these tracing conventions.
Borrowing it
Nothing to install: this file belongs to Arize-ai/openinference. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Arize-ai/openinference/main/.agents/skills/java-code-reviewer/SKILL.mdgit clone --depth 1 https://github.com/Arize-ai/openinferenceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/arize-ai/openinference/java-code-reviewer)<a href="https://agentmods.dev/skills/arize-ai/openinference/java-code-reviewer"><img src="https://agentmods.dev/badge/skills/arize-ai/openinference/java-code-reviewer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/arize-ai/openinference/java-code-reviewer"><img src="https://agentmods.dev/badge/skills/arize-ai/openinference/java-code-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00101 | $0.01247 |
| Opus 5.5 | $0.00040 | $0.00499 |
| Sonnet 5.5 | $0.00020 | $0.00249 |
| Haiku 4.5 | $0.00010 | $0.00125 |
Grade A, and why
java-code-reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 — checkbuild.gradledependency 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)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 131 lines · 101 tokens per session scan A d025ea3376b6
java-code-reviewer is a skill published in the GitHub repository Arize-ai/openinference (1,291 stars, last pushed today), licensed Apache-2.0. It adds 101 tokens to every session and 1,247 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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