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/.claude/skills/genai-conformance/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/genai-conformance)<a href="https://agentmods.dev/skills/arize-ai/openinference/genai-conformance"><img src="https://agentmods.dev/badge/skills/arize-ai/openinference/genai-conformance.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00095 | $0.02776 |
| Opus 5 | $0.00048 | $0.01388 |
| Sonnet 5 | $0.00019 | $0.00555 |
| Haiku 4.5 | $0.00010 | $0.00278 |
Grade A, and why
genai-conformance 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 7d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GenAI Conformance
The repo ships a self-contained conformance harness at python/openinference-instrumentation/scripts/conformance/ that exercises OpenInference instrumentors against deterministic mock provider APIs, exports OTLP traces to weaver registry live-check, and prints a console summary of registry attributes seen / missing / advice-level counts. It validates the dual-write logic in _genai_conversion.py that translates OpenInference's native attributes (llm.*, input.*, output.*, openinference.*) into the OTel GenAI semantic conventions (gen_ai.*).
When to Use
- User asks to run the conformance harness, "test conformance", or "run weaver".
- User asks to maximize / improve
gen_ai.*registry coverage. - User wants to extend the dual-write conversion in
_genai_conversion.py. - User wants to add a new provider, a new test scenario, or a new mock endpoint.
- User mentions specific
gen_ai.*attributes (response.id, system_instructions, tool.call., retrieval., etc.) and whether they're being emitted.
Layout
scripts/conformance/
├── run.py # orchestrator (PEP 723, stdlib only)
├── mock_server.py # Flask mock with all providers' endpoints
├── anthropic_conformance.py # PEP 723 + editable [tool.uv.sources]
├── openai_conformance.py # PEP 723 + editable [tool.uv.sources]
├── google_genai_conformance.py # PEP 723 + editable [tool.uv.sources]
├── README.md
└── results/ # gitignored Weaver output
Each provider script declares its deps as PEP 723 inline metadata and pins the local OpenInference packages via [tool.uv.sources.<pkg>] blocks (multi-section dotted-key form — single-line inline tables exceed ruff's 100-char limit). run.py invokes everything via uv run. Filenames avoid the bare provider name (openai.py, anthropic.py) because that would shadow the SDK package on sys.path[0].
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.
- 7d ago First seen · 101 lines · 95 tokens per session scan A fa477b415d8a
genai-conformance is a skill published in the GitHub repository Arize-ai/openinference (1,197 stars, last pushed yesterday), licensed Apache-2.0. It adds 95 tokens to every session and 2,776 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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