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/tilelang-ascend-kernel/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/tilelang-ascend-kernel)<a href="https://agentmods.dev/skills/xllm-ai/xllm/tilelang-ascend-kernel"><img src="https://agentmods.dev/badge/skills/xllm-ai/xllm/tilelang-ascend-kernel/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/tilelang-ascend-kernel"><img src="https://agentmods.dev/badge/skills/xllm-ai/xllm/tilelang-ascend-kernel.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 | $0.00060 | $0.02091 |
| Opus 5 | $0.00030 | $0.01045 |
| Sonnet 5 | $0.00012 | $0.00418 |
| Haiku 4.5 | $0.00006 | $0.00209 |
Grade A, and why
tilelang-ascend-kernel scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TileLang Ascend Kernel
When to use
Use this skill when the task involves any of the following in the xLLM repo:
xllm/python/kernels_npu/tilelang/*.pyxllm/compiler/tilelang/targets/ascend/aot/*.pyxllm/core/kernels/npu/tilelang/*_wrapper.cppxllm/core/kernels/npu/tilelang/CMakeLists.txt- generated TileLang artifacts such as
manifest.json,registry.inc, or specialization.cpp
Run build and test commands inside the NPU container.
Run TileLang commands from the xLLM repo root, not from an installed-package environment.
Entry points and TL_ROOT
- Prefer
python xllm/compiler/tilelang_launcher.py ...for end-to-end TileLang compile flows. - From the xLLM repo root, use
export TL_ROOT=$PWD/third_party/tilelang-ascendfor xLLM TileLang tooling and verifytest -f "$TL_ROOT/tilelang/__init__.py". - Before any raw script does
import tilelang, runexport TL_ROOT=$PWD/third_party/tilelang-ascend && source third_party/tilelang-ascend/set_env.sh, then execute the script. - Do not run kernel files directly with
python rope.py; use module execution because these files rely on relative imports. - For direct kernel-script debugging, run them as modules and pass required CLI args:
cd xllm
export PYTHONPATH="$PWD:$PWD/xllm${PYTHONPATH:+:$PYTHONPATH}"
python -m compiler.tilelang.targets.ascend.aot.rope \
--output .tmp/rope.cpp
# Expected: [INFO] RoPE output matches torch reference
- The same module-style rule applies to other AOT descriptors under
xllm/compiler/tilelang/targets/ascend/aot/.
Primary Reference And Mode Preference
Primary reference:
third_party/tilelang-ascend/docs/TileLang-Ascend Programming Guide.md
Use third_party/tilelang-ascend/.agents/skills/tilelang-custom-skill/tilelang-api-best-practices/references/api-tile-ops.md when the task depends on T.tile.xxx semantics such as compare, select, cast, or other vector intrinsics.
Default to Expert mode for xLLM Ascend kernels:
- prefer
T.tile.xxx, explicit UB/shared allocation, and explicitT.copy - prefer explicit
T.serialcontrol for row/block traversal - do not introduce Developer mode
T.Parallelunless the kernel is a clearly tile-local element-wise expression and the change does not reduce control over UB usage, temporary buffers, or exact runtime semantics - when translating Triton kernels, preserve the Triton runtime semantics first, then choose the smallest Expert-mode lowering that matches them
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.
- 8d ago First seen · 146 lines · 60 tokens per session scan A 7d131c87b783
tilelang-ascend-kernel is a skill published in the GitHub repository xLLM-AI/xllm (1,562 stars, last pushed yesterday), licensed Apache-2.0. It adds 60 tokens to every session and 2,091 once invoked, about $0.0003 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 2026-08-31.
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