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.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/instructions/xllm-ai/xllm/agents-md)<a href="https://agentmods.dev/instructions/xllm-ai/xllm/agents-md"><img src="https://agentmods.dev/badge/instructions/xllm-ai/xllm/agents-md/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/instructions/xllm-ai/xllm/agents-md"><img src="https://agentmods.dev/badge/instructions/xllm-ai/xllm/agents-md.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00502 | $0.00502 |
| Opus 5.5 | $0.00201 | $0.00201 |
| Sonnet 5.5 | $0.00100 | $0.00100 |
| Haiku 4.5 | $0.00050 | $0.00050 |
Grade A, and why
xllm AGENTS.md 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.
What it actually says
xLLM Coding Agent Instructions
Code Style Guide
- Before editing, creating, refactoring, or reviewing any file under
xllm/, you MUST read the shared rules in custom-code-style.md and the guide for each language you touch: C++ or Python. - The files above are required instruction files, not optional references. Do not skip reading them.
- After editing any C++ file, run clang-format 20.1.6 with the repo
.clang-formaton every touched file before finishing. Do not leave formatting for the user to remind you. - Apply the rules in the shared guide and the matching language guide to both code generation and code review.
- Follow DDD (Domain Driven Design) principles, and keep the codebase clean and maintainable.
- If the shared guide or the matching language guide specifies a rule, that rule takes precedence over the Google C++/Python Style Guide.
- Use the Google C++/Python Style Guide only for cases not specified in the shared guide or the matching language guide.
Review Instructions
- For code review tasks, you MUST first read code-review/SKILL.md.
- Then read the shared rules in custom-code-style.md and the matching language guide(s) (C++, Python) and apply them during the review.
- Review code changes for quality, security, performance, correctness, and maintainability following the project-specific standards.
- Review code changes for DDD (Domain Driven Design) principles, and keep the codebase clean and maintainable.
- Use the review workflow, checklist, severity rules, and output format defined in code-review/SKILL.md.
- Apply the Google C++/Python Style Guide only when the project-specific style guide does not define the rule.
- Focus the review on the requested diff or changed files. Do not comment on unrelated code.
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 · +63 tokens per session 940ea2631e64
- 10d ago Changed · -31 lines · -366 tokens per session 3c2e5b79247d
- 1mo ago Changed · +1 lines · +42 tokens per session 42af87fcc699
- 1mo ago First seen · 51 lines · 763 tokens per session scan A acf2fc8c680f
xllm AGENTS.md is an instructions file published in the GitHub repository xLLM-AI/xllm (1,604 stars, last pushed yesterday), licensed Apache-2.0. It adds 502 tokens to every session, about $0.0020 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.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.