xLLM is an inference engine, meaning software that runs trained AI models to produce outputs from inputs, for large language, vision-language, diffusion, and recommendation models on different AI accelerators. Organizations use it to deploy these models with high-throughput and low-latency inference. The catalogue entries provide skills and instructions for working with xLLM.
Borrowing it
Nothing to install: this file belongs to xLLM-AI/xllm. 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/xLLM-AI/xllm/main/.agents/skills/add-unit-test/SKILL.mdgit clone --depth 1 https://github.com/xLLM-AI/xllmWrote 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/xllm-ai/xllm/add-unit-test)<a href="https://agentmods.dev/skills/xllm-ai/xllm/add-unit-test"><img src="https://agentmods.dev/badge/skills/xllm-ai/xllm/add-unit-test/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/xllm-ai/xllm/add-unit-test"><img src="https://agentmods.dev/badge/skills/xllm-ai/xllm/add-unit-test.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 | $0.00036 | $0.00761 |
| Opus 5.5 | $0.00014 | $0.00304 |
| Sonnet 5.5 | $0.00007 | $0.00152 |
| Haiku 4.5 | $0.00004 | $0.00076 |
Grade A, and why
add-unit-test 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 5d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add Unit Test
Python tests
Read Python tests in xllm/python/README.md. Put Python tests in
tests/python/. Use normal imports; conftest.py loads xllm_export and calls
initialize_runtime() before collection. Do not repeat initialization or add
test-local extension loading or sys.path manipulation.
Read the Python style guide at
.agents/skills/code-review/references/custom-code-style-python.md before
writing or changing Python tests.
Run python -m pytest tests/python/<file>.py from the checkout root against matching
native build artifacts in the environment required by checkout instructions.
Direct pytest does not build the extension.
C++ test workflow
-
Inspect the production code and the nearest existing tests before writing a new test.
- Match the production path under
xllm/totests/where possible. - Prefer extending an existing nearby
*_test.cppandcc_testtarget when the behavior belongs to the same domain. - Create a new test source only when it improves isolation, keeps platform setup separate, or follows an existing directory pattern.
- Match the production path under
-
Read the C++ style guide at
.agents/skills/code-review/references/custom-code-style-cpp.mdbefore editing production files underxllm/, and apply the same discipline to new test code. -
Follow the current test layout and CMake conventions.
- Read xllm-test-patterns.md when adding a new test file, new
cc_test, platform-specific test, or test directory. - Use
*_test.cppfor C++ test files and*_test.cufor CUDA source tests. - Do not create nested
test/ortests/directories for new unit tests unless the surrounding tree already requires that structure.
- Read xllm-test-patterns.md when adding a new test file, new
-
Wire tests through CMake with
include(cc_test)andcc_test(...).- Keep source names relative to the current test directory unless an existing target already uses an absolute source path for a production
.cpp. - Use target names ending in
_test. - Put platform-directory gates in the parent
CMakeLists.txtwhen the whole child directory is platform-specific. - Use target-level
if(USE_NPU),if(USE_MLU),if(USE_CUDA), or generator expressions only when a mixed directory contains both generic and platform-specific tests.
- Keep source names relative to the current test directory unless an existing target already uses an absolute source path for a production
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago Changed · +5 lines 9793cb94819a
- 23d ago Changed · +11 lines · -40 tokens per session 9024d53ec4e7
- 1mo ago First seen · 49 lines · 76 tokens per session scan A 2388680223ba
add-unit-test is a skill published in the GitHub repository xLLM-AI/xllm (1,604 stars, last pushed yesterday), licensed Apache-2.0. It adds 36 tokens to every session and 761 once invoked, about $0.0001 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 2026-08-31.
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