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.
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 rules/xllm-ai/xllm/clang-formatgit 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/rules/xllm-ai/xllm/clang-format)<a href="https://agentmods.dev/rules/xllm-ai/xllm/clang-format"><img src="https://agentmods.dev/badge/rules/xllm-ai/xllm/clang-format.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 | $0.00113 | $0.00113 |
| Opus 5 | $0.00056 | $0.00056 |
| Sonnet 5 | $0.00023 | $0.00023 |
| Haiku 4.5 | $0.00011 | $0.00011 |
Grade A, and why
clang-format 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.
What it actually says
Clang-Format
After editing any C++ file, run clang-format 20.1.6 with the repo-root .clang-format on every touched file before you stop.
clang-format -i <edited-files>
Do not leave include-order, wrapping, or spacing for the user to catch. An unformatted C++ change is not finished.
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 · 16 lines · 113 tokens per session scan A f7f07b9f3b4b
clang-format is a cursor rule published in the GitHub repository xLLM-AI/xllm (1,557 stars, last pushed 2d ago), licensed Apache-2.0. It adds 113 tokens to every session, about $0.0006 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 2026-09-04.
Other cursor rules, from other repositories
cuda
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020-flask
Flask API Gateway conventions for LMForge.
vios-error-security
Error handling and security rules for VIOS C++ code.
qb-linq-project
qb-linq — header-only C++17 LINQ; read AGENTS.md and docs/LLMCONTEXT.md before editing.
monitor-c-conventions
C coding conventions for HPCPerfStats monitor (C-only).
monitor-c-refactor-standards
Behavior-preserving C refactor standards for HPCPerfStats monitor.