SageMaker Serving Image Selection
huggingface/skills
Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.
Review and adapt MetaX attention backends, MLA, sparse indexers, cache layouts, and their kernel wrappers against a target vLLM revision and the actually installed MetaX component APIs.
$ npx skills add MetaX-MACA/vLLM-metax --skill vllm-metax-attention-upgrade -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install MetaX-MACA/vLLM-metax vllm-metax-attention-upgrade --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/MetaX-MACA/vLLM-metax.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.codex/skills/vllm-metax-attention-upgrade .claude/skills/vllm-metax-attention-upgrade && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "vllm-metax-attention-upgrade" agent skill from https://github.com/MetaX-MACA/vLLM-metax/tree/master/.codex/skills/vllm-metax-attention-upgrade into .claude/skills/vllm-metax-attention-upgrade/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vllm-metax-attention-upgrade", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/MetaX-MACA/vLLM-metax/tree/master/.codex/skills/vllm-metax-attention-upgradeType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add MetaX-MACA/vLLM-metax --skill vllm-metax-attention-upgrade -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install MetaX-MACA/vLLM-metax vllm-metax-attention-upgrade --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MetaX-MACA/vLLM-metax.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.codex/skills/vllm-metax-attention-upgrade .agents/skills/vllm-metax-attention-upgrade && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "vllm-metax-attention-upgrade" agent skill from https://github.com/MetaX-MACA/vLLM-metax/tree/master/.codex/skills/vllm-metax-attention-upgrade into .agents/skills/vllm-metax-attention-upgrade/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vllm-metax-attention-upgrade", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add MetaX-MACA/vLLM-metax --skill vllm-metax-attention-upgrade -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install MetaX-MACA/vLLM-metax vllm-metax-attention-upgrade --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MetaX-MACA/vLLM-metax.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.codex/skills/vllm-metax-attention-upgrade .cursor/skills/vllm-metax-attention-upgrade && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "vllm-metax-attention-upgrade" agent skill from https://github.com/MetaX-MACA/vLLM-metax/tree/master/.codex/skills/vllm-metax-attention-upgrade into .cursor/skills/vllm-metax-attention-upgrade/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vllm-metax-attention-upgrade", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/MetaX-MACA/vLLM-metax.git --path .codex/skills/vllm-metax-attention-upgrade--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add MetaX-MACA/vLLM-metax --skill vllm-metax-attention-upgrade -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install MetaX-MACA/vLLM-metax vllm-metax-attention-upgrade --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MetaX-MACA/vLLM-metax.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.codex/skills/vllm-metax-attention-upgrade .gemini/skills/vllm-metax-attention-upgrade && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "vllm-metax-attention-upgrade" agent skill from https://github.com/MetaX-MACA/vLLM-metax/tree/master/.codex/skills/vllm-metax-attention-upgrade into .gemini/skills/vllm-metax-attention-upgrade/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vllm-metax-attention-upgrade", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install MetaX-MACA/vLLM-metax vllm-metax-attention-upgradeInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add MetaX-MACA/vLLM-metax --skill vllm-metax-attention-upgrade -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/MetaX-MACA/vLLM-metax.git skills-src && mkdir -p .github/skills && cp -r skills-src/.codex/skills/vllm-metax-attention-upgrade .github/skills/vllm-metax-attention-upgrade && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "vllm-metax-attention-upgrade" agent skill from https://github.com/MetaX-MACA/vLLM-metax/tree/master/.codex/skills/vllm-metax-attention-upgrade into .github/skills/vllm-metax-attention-upgrade/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vllm-metax-attention-upgrade", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add MetaX-MACA/vLLM-metax --skill vllm-metax-attention-upgrade -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install MetaX-MACA/vLLM-metax vllm-metax-attention-upgrade --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MetaX-MACA/vLLM-metax.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.codex/skills/vllm-metax-attention-upgrade .opencode/skills/vllm-metax-attention-upgrade && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "vllm-metax-attention-upgrade" agent skill from https://github.com/MetaX-MACA/vLLM-metax/tree/master/.codex/skills/vllm-metax-attention-upgrade into .opencode/skills/vllm-metax-attention-upgrade/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vllm-metax-attention-upgrade", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
vllm-metax-attention-upgradeReview and adapt MetaX attention backends, MLA, sparse indexers, cache layouts, and their kernel wrappers against a target vLLM revision and the actually installed MetaX component APIs.
Vllm Metax Attention Upgrade is an agent skill from MetaX-MACA/vLLM-metax. Review and adapt MetaX attention backends, MLA, sparse indexers, cache layouts, and their kernel wrappers against a target vLLM revision and the actually installed MetaX component APIs. Verify dispatch, supported configurations, and GPU numerical behavior. Use for attention adaptations outside vllmmetax/patch/; do not apply monkey-patch headers or patch audit requirements.
Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `agents/openai.yaml`, `references/component-contracts.md` and `references/validation.md`).
It sits in AI & LLM Engineering, covering LLM inference and serving. It works with vLLM. The repository describes itself as: Community maintained hardware plugin for vLLM on MetaX GPU. The licence is Apache-2.0.
Read from SKILL.md and the folder at commit df0f52b. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Vllm Metax Attention Upgrade loads about 2.3k tokens when it runs, and up to ~5.6k if it reads all its reference files. Until then it costs about 101 tokens; SKILL.md has 1,098 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from MetaX-MACA/vLLM-metax at commit df0f52b, republished under its Apache-2.0 licence (© MetaX-MACA). 1,098 words, ~2,256 tokens.
.claude/skills/vllm-metax-attention-upgrade/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.Preserve attention semantics while adapting to two independent contracts: the target vLLM Python implementation and the installed MetaX kernel stack. Similar names, matching signatures, successful imports, comments, and upstream tests do not establish MetaX compatibility. Validate the actual runtime implementation.
vllm_metax/v1/attention/, common MLA
layers, sparse indexer CustomOps, and directly related models and wrappers.
Follow callers outside these paths when needed; do not expand into unrelated changes.vllm_metax/patch/AUDIT.md. If a task also changes
monkey patches, use the patch workflow only for that portion.Read component-contracts.md for every affected component family before deciding compatibility. Its versioned observations are prompts for revalidation, not unconditional capability rules.
Trace each path from registration/platform selection through metadata builder, layer binding, wrapper, imported callable, and installed Python/native kernel. Include native, flattened, fallback, prefill/decode, and relevant capture/distributed branches.
For each used component entry point, record:
| Evidence | What to establish |
|---|---|
| Runtime identity | Distribution version, imported file, actual callable module/source, native extension path, active plugin and relevant feature flags. |
| Inputs | Positional/keyword arguments, tensor vs tuple, dtype, shape, strides, contiguity, scales, index units, and meanings of omitted/None arguments. |
| Outputs | Tensor vs tuple, output and LSE shapes/dtypes, LSE logarithm base, padding, aliasing, in-place writes and empty-row behavior. |
| Supported combinations | Joint constraints on dtype, QK/V dimensions, sinks, block size, quantization, native MTP, DCP, variable lengths, and graph capture. |
| Evidence level | Source-inspected, signature-checked, runtime-reproduced, numerically validated, or untested. |
Record the GPU model, available devices, Torch/Triton build identities and MACA runtime/ABI evidence with runtime results. NVIDIA architecture checks in upstream code are not substitutes for the executing MetaX device's capabilities.
Inspect inspect.signature, inspect.getsourcefile, extension docstrings and wrapper
bodies where available. Trace *args/**kwargs, decorators, lazy symbol resolution, and
aliases to the final implementation. An absent Python signature does not imply missing
support. An accepted keyword does not prove its semantics are implemented.
Actively challenge comments such as "FA2 cannot do DiffKV", "all sinks supported", "FP8/FP4 share this API", "cache is contiguous", and "DCP supported". Compare claims with executable restrictions and discriminating calls. Do not rewrite platform code solely to resemble upstream, nor remove a fallback solely because upstream added a feature.
bind_kv_cache, cache insertion,
gather, index conversion, kernel reads, and output merge. A logical shape does not
prove a physical layout. Check layer/page/head/token strides and storage offsets.-1 entries have interior holes.Choose between retaining a necessary MetaX difference, updating an interface/semantic mapping, using a verified fallback, or rejecting an unsupported configuration early. For a suspected component defect, reproduce it by calling that component directly in an isolated process before attributing it to the adapter.
supports_combination, dtype
and layout restrictions, supports_out, graph support, and constructors must agree
with reachable implementations. Check combinations, not isolated flags.is not None narrowing where needed rather than hiding type errors with suppressions.Use the affected cases from validation.md. Run actual kernels against independent numerical references in the intended environment. Test wrappers and real branch routing in addition to raw APIs. A mocked conversion, isolated metadata test, or successful import is not a full kernel/end-to-end test.
Use the common skill's isolated-validation and evidence rules. Keep the actual attention component under numerical test real, not mocked.
For each finding or fix, retain the trigger, expected/observed behavior, component and
source origins, reproduction command, result, and verification limits. Distinguish a
new adaptation regression, an existing adapter defect, and an installed component bug.
Keep generated runtime scripts/tests in a clearly identified reproducible artifact;
do not depend on old /tmp files being present in future sessions.
Run relevant formatting, lint, type and diff checks. If a checker is unavailable in the selected environment, say so rather than claiming it passed. End with concrete changes or review findings, evidence, and untested scope. Place any requested audit alongside the attention work or at a user-selected path, never in the patch audit by default.
© MetaX-MACA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 3 other files (references) in .codex/skills/vllm-metax-attention-upgrade of MetaX-MACA/vLLM-metax.
Open the folder on GitHubat commit df0f52b
Vllm Metax Attention Upgrade next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Vllm Metax Attention Upgrade this skillMetaX-MACA/vLLM-metax | 180 | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| SageMaker Serving Image Selectionhuggingface/skills | 11k | 1 repos | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Hugging Face Local Model Evalshuggingface/skills | 11k | 2 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| CI Fails Buildkiteguqiong96/Lvllm | 465 | 2 repos | ~349 | Automated safety check: Pass | Apache-2.0 | |
| Gptqmodel Tokenizer NormalizationModelCloud/GPTQModel | 1.3k | — | ~1.1k | Automated safety check: Pass | Custom licence | |
| Add Diffusion Modelvllm-project/vllm-omni | 7.1k | — | ~7k | Automated safety check: Pass | Apache-2.0 |
huggingface/skills
Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.
huggingface/skills
Runs evaluations of Hugging Face Hub models on local hardware with inspect-ai or lighteval, and helps choose between vLLM, Transformers and accelerate backends.
guqiong96/Lvllm
Fetch and diagnose vLLM Buildkite CI failure logs. An agent skill from guqiong96/Lvllm.
ModelCloud/GPTQModel
Diagnose and correct GPT-QModel tokenizer initialization, tokenization normalization, special-token handling, prompt rendering, and chat-template problems.
vllm-project/vllm-omni
Add a new diffusion model (text-to-image, text-to-video, image-to-video, text-to-audio, image editing) to vLLM-Omni, including native non-Diffusers ports, reference-parity validation, Cache-DiT…
vllm-project/vllm-omni
Add or update an in-repository vLLM-Omni model recipe with verified task, input, output, hardware, command, feature, and validation contracts.
MetaX-MACA/vLLM-metax
Review and upgrade MetaX model support against a target vLLM revision and installed MACA components, recursively including model-dependent attention and kernels.
MetaX-MACA/vLLM-metax
Review and adapt vllmmetax/registry registrations, quantization configurations, CustomOps and kernel dispatch against a target vLLM revision and installed MetaX APIs.
MetaX-MACA/vLLM-metax
Audit and adapt monkey patches in vllmmetax/patch/ against a target upstream revision.
MetaX-MACA/vLLM-metax
Establish the shared environment, source/runtime correspondence, target confirmation and validation evidence for MetaX vLLM upgrades.
MetaX-MACA/vLLM-metax
Trim large MetaX model directories for dummy smoke tests or real-checkpoint loading on limited GPUs.
Works with
Categories
Review and adapt MetaX attention backends, MLA, sparse indexers, cache layouts, and their kernel wrappers against a target vLLM revision and the actually installed MetaX component APIs. Vllm Metax Attention Upgrade is an agent skill from MetaX-MACA/vLLM-metax. Review and adapt MetaX attention backends, MLA, sparse indexers, cache layouts, and their kernel wrappers against a target vLLM revision and the actually installed MetaX component APIs.
Vllm Metax Attention Upgrade fits situations like: attention adaptations outside vllmmetax/patch/; do not apply monkey-patch headers; patch audit requirements.
Run `npx skills add MetaX-MACA/vLLM-metax --skill vllm-metax-attention-upgrade -a claude-code`. Or copy the skill folder (.codex/skills/vllm-metax-attention-upgrade in MetaX-MACA/vLLM-metax) into .claude/skills/vllm-metax-attention-upgrade in your project. Claude Code loads it when a task matches its description.
Run `npx skills add MetaX-MACA/vLLM-metax --skill vllm-metax-attention-upgrade -a codex`. Or copy the skill folder (.codex/skills/vllm-metax-attention-upgrade in MetaX-MACA/vLLM-metax) into .agents/skills/vllm-metax-attention-upgrade in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add MetaX-MACA/vLLM-metax --skill vllm-metax-attention-upgrade -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/vllm-metax-attention-upgrade, .gemini/skills/vllm-metax-attention-upgrade, .github/skills/vllm-metax-attention-upgrade and .opencode/skills/vllm-metax-attention-upgrade in your project.
SKILL.md names no scripts, command-line tools or credentials: Vllm Metax Attention Upgrade is instructions for the agent only. Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Vllm Metax Attention Upgrade is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.3k tokens (SKILL.md is roughly 9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.4k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Vllm Metax Attention Upgrade: SageMaker Serving Image Selection (huggingface/skills, 11k stars), Hugging Face Local Model Evals (huggingface/skills, 11k stars), CI Fails Buildkite (guqiong96/Lvllm, 465 stars) and Gptqmodel Tokenizer Normalization (ModelCloud/GPTQModel, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
MetaX-MACA (a GitHub organization) maintains it in MetaX-MACA/vLLM-metax, which has 180 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on October 10, 2026.
Source: MetaX-MACA/vLLM-metax on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.