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/python-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/python-code-reviewer)<a href="https://agentmods.dev/skills/arize-ai/openinference/python-code-reviewer"><img src="https://agentmods.dev/badge/skills/arize-ai/openinference/python-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/python-code-reviewer"><img src="https://agentmods.dev/badge/skills/arize-ai/openinference/python-code-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 45 uvx/uv tool run commands without ==version create a rug-pull risk.Fix: Pin the version: uvx package-name==1.2.3
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 | $0.00100 | $0.03346 |
| Opus 5.5 | $0.00040 | $0.01338 |
| Sonnet 5.5 | $0.00020 | $0.00669 |
| Haiku 4.5 | $0.00010 | $0.00335 |
Grade A, and why
python-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 — 366 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Code Reviewer for OpenInference Instrumentors
Review a Python OpenInference instrumentation package against the project's established patterns and conventions. This is a checklist-driven review — go through each section, report findings with file paths and line numbers, and surface issues organized by severity.
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
python/instrumentation/openinference-instrumentation-<name>/ - Read the key files:
__init__.py,_wrappers.py(or equivalent),pyproject.toml, and the fulltests/directory
Step 2: Pull the instrumented library source and use it as ground truth
OpenInference instrumentors work by monkey-patching functions in the library they instrument. All correctness judgments — whether wrappers target the right methods, handle the right signatures, process the right data structures, and cover the right edge cases — must be verified against the actual library source code. Do NOT make assumptions about how the instrumented library works.
Note: The tox env name
<pkg>and the library's Python import path<library>often differ. For example,google_genaiis the tox env name but the library installs asgoogle/genai/in site-packages. Checktest-requirements.txtorpyproject.tomlto find the actual library package name.
- Set up the tox environment to install the pinned library version. Look up the
tox envlist in
python/tox.inito find the correct env name (use the highest Python version available, e.g.,py314,py313):
(cd python && uvx --with tox-uv tox run -e <pyVER>-ci-<pkg> -- --co -q-- --co -qtells pytest to collect without running, which triggers the install.) If the.toxenv already exists, skip this step. If tox setup fails (missing Python version, dependency conflicts), fall back topip install <library>in a temporary venv to unblock the review.
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 · 366 lines · 100 tokens per session scan A d7083cc2e6bf
python-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 100 tokens to every session and 3,346 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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splitting-oversized-modules
Split an oversized Python module (a thousand-plus-line logic.py, models.py, api.py, or its test file) into a package of one module per concern, mechanically and provably without changing behavior. Use on a request to split / break up / decompose a god module or move functions out of one, once a human has agreed to…