Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/arize-ai/openinference/java-code-reviewernpx skills add Arize-ai/openinference --skill java-code-reviewergit clone --depth 1 https://github.com/Arize-ai/openinferenceWhat 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 | $0.00101 | $0.01247 |
| Opus 5 | $0.00051 | $0.00624 |
| Sonnet 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 2d 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.
- 2d 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 (1,186 stars, last pushed 2d ago), licensed Apache-2.0. It adds 101 tokens to every session and 1,247 once invoked, about $0.0005 per session on Opus 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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