xllm: Skill for Claude Code

.agents/skills/tilelang-ascend-kernel/SKILL.md

tilelang-ascend-kernel is a skill for Claude Code, Codex from xLLM-AI/xllm. It costs 60 tokens per session (2,091 once invoked), scanned A, original, Apache-2.0.

A development guide for TileLang Ascend kernels in xLLM, where TileLang describes computations that are compiled for Ascend processors.

In plain words
What is it for?
Use it to add, modify, debug, or review TileLang Python kernels, generated Ascend-C code, wrappers, CMake wiring, and NPU tests.
Why use it?
It keeps kernel changes, generated files, runtime wiring, builds, and tests aligned with the repository's requirements.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is xLLM-AI/xllm's own configuration. It tells Claude Code and Codex how to work on xllm itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything xllm configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python xllm/compiler/tilelang_launcher.py compile-kernels \.

About the project

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.

xLLM-AI/xllm · 1,562 stars · on GitHub · xllm-ai.com

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/xLLM-AI/xllm/main/.agents/skills/tilelang-ascend-kernel/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/xLLM-AI/xllm

Made for: Claude Code, Codex.

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README.md
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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.

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Your own site · 80×15
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Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,091 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 8d ago against content hash 7d131c87b783, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

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.

.agents/skills/tilelang-ascend-kernel/SKILL.md · 146 lines

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/*.py
  • xllm/compiler/tilelang/targets/ascend/aot/*.py
  • xllm/core/kernels/npu/tilelang/*_wrapper.cpp
  • xllm/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=$PWD/third_party/tilelang-ascend for xLLM TileLang tooling and verify test -f "$TL_ROOT/tilelang/__init__.py".
  • Before any raw script does import tilelang, run export 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 explicit T.copy
  • prefer explicit T.serial control for row/block traversal
  • do not introduce Developer mode T.Parallel unless 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

Read the full file on GitHub · 146 lines

Changes

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.

  1. 8d ago First seen · 146 lines · 60 tokens per session scan A 7d131c87b783

Subscribe to this mod's changes

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.