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Agent skill

Optimize Triton Ascend Int Vectors

by Krusty84 in Krusty84/triton-ascend-agent-dev-kit

Optimize integer vector kernels for Triton-Ascend by preferring int32 arithmetic when the value and index ranges permit it.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Optimize Triton Ascend Int Vectors

skills CLI
$ npx skills add Krusty84/triton-ascend-agent-dev-kit --skill optimize-triton-ascend-int-vectors -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Krusty84/triton-ascend-agent-dev-kit optimize-triton-ascend-int-vectors --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Krusty84/triton-ascend-agent-dev-kit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/optimize-triton-ascend-int-vectors .claude/skills/optimize-triton-ascend-int-vectors && rm -rf skills-src

Use ~/.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/

Facts

Skill name
optimize-triton-ascend-int-vectors
GitHub stars
106
Token cost
~545 tokens
SKILL.md length
176 words
Files
1
Skills in repo
23
Repo updated
First seen
Licence
Apache-2.0

At a glance

Optimize integer vector kernels for Triton-Ascend by preferring int32 arithmetic when the value and index ranges permit it.

  • Works in 5 steps: Determine the minimum signed range… → Use torch.int32 tensors and… → Keep pointer arithmetic and tail masking… → …
  • An agent ports an int64 GPU elementwise kernel to Ascend NPU
  • SKILL.md covers Goal, Workflow, Implementation Pattern and Ascend Guardrails, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Optimize Triton Ascend Int Vectors is an agent skill from Krusty84/triton-ascend-agent-dev-kit. Optimize integer vector kernels for Triton-Ascend by preferring int32 arithmetic when the value and index ranges permit it. Use when an agent ports an int64 GPU elementwise kernel to Ascend NPU, observes scalarized integer arithmetic, or designs vector addition, subtraction, or reduction over integer tensors.

Its SKILL.md is about 550 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering. The repository describes itself as: A toolkit for AI agents used for development on Triton-Ascend for Ascend NPU. The licence is Apache-2.0.

When your agent uses it

  • An agent ports an int64 GPU elementwise kernel to Ascend NPU
  • Observes scalarized integer arithmetic
  • Designs vector addition
  • Reduction over integer tensors

Example prompts

  • “/optimize-triton-ascend-int-vectors”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Determine the minimum signed range required by inputs, offsets, intermediates, and outputs.
  2. Use torch.int32 tensors and int32-compatible scalar arguments when every possible value fits.
  3. Keep pointer arithmetic and tail masking separate from the arithmetic dtype decision.
  4. Launch a masked one-dimensional kernel and compare it with an int32 PyTorch reference.
  5. Benchmark int32 and int64 only after synchronizing the NPU around timed regions.

What it can do on your machine

Read from SKILL.md and the folder at commit 4ab5ee7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Optimize Triton Ascend Int Vectors loads about 545 tokens when it runs. Until then it costs about 86 tokens; SKILL.md has 176 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~86
When it runs · the whole SKILL.md, loaded when a task matches
~545

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from Krusty84/triton-ascend-agent-dev-kit at commit 4ab5ee7, republished under its Apache-2.0 licence (© Krusty84). 176 words, ~545 tokens.

Download SKILL.mdSave it as .claude/skills/optimize-triton-ascend-int-vectors/SKILL.md (or your agent's skills folder).
name
optimize-triton-ascend-int-vectors
description
Optimize integer vector kernels for Triton-Ascend by preferring int32 arithmetic when the value and index ranges permit it. Use when an agent ports an int64 GPU elementwise kernel to Ascend NPU, observes scalarized integer arithmetic, or designs vector addition, subtraction, or reduction over integer tensors.

Optimize Triton-Ascend Integer Vectors

Goal

Keep integer elementwise work on efficient Ascend vector paths without changing numerical semantics.

Workflow

  1. Determine the minimum signed range required by inputs, offsets, intermediates, and outputs.
  2. Use torch.int32 tensors and int32-compatible scalar arguments when every possible value fits.
  3. Keep pointer arithmetic and tail masking separate from the arithmetic dtype decision.
  4. Launch a masked one-dimensional kernel and compare it with an int32 PyTorch reference.
  5. Benchmark int32 and int64 only after synchronizing the NPU around timed regions.

Implementation Pattern

python
@triton.jit
def int_add(x, y, out, n, BLOCK_SIZE: tl.constexpr):
    offsets = tl.program_id(0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    mask = offsets < n
    lhs = tl.load(x + offsets, mask=mask)
    rhs = tl.load(y + offsets, mask=mask)
    tl.store(out + offsets, lhs + rhs, mask=mask)


x = torch.randint(0, 100, (n,), device="npu", dtype=torch.int32)
y = torch.randint(0, 100, (n,), device="npu", dtype=torch.int32)
out = torch.empty_like(x)
int_add[(triton.cdiv(n, block),)](x, y, out, n, BLOCK_SIZE=block)

Ascend Guardrails

  • Never downcast identifiers, addresses, prefix sums, or accumulated values that can exceed the int32 range.
  • Check intermediate overflow, not only the input range.
  • Keep tensors, kernel scalar arguments, and the PyTorch reference on consistent dtypes.
  • Use the same mask on all tail loads and stores.
  • Synchronize before starting and after ending a host-side timing interval; exclude warmup and compilation.

Verification

Test boundary values near the chosen dtype limits, irregular lengths, and a case that would overflow int32. The overflow case must remain int64 or be rejected explicitly.

© Krusty84, 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

Files

Just SKILL.md in skills/optimize-triton-ascend-int-vectors of Krusty84/triton-ascend-agent-dev-kit.

Open the folder on GitHubat commit 4ab5ee7

Compare with similar skills

Optimize Triton Ascend Int Vectors 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.

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1passwordtrpc-group/trpc-agent-go1.9k14 repos~656Automated safety check: PassApache-2.0

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Categories

Questions about Optimize Triton Ascend Int Vectors

What does Optimize Triton Ascend Int Vectors do?

Optimize integer vector kernels for Triton-Ascend by preferring int32 arithmetic when the value and index ranges permit it. Optimize Triton Ascend Int Vectors is an agent skill from Krusty84/triton-ascend-agent-dev-kit. Optimize integer vector kernels for Triton-Ascend by preferring int32 arithmetic when the value and index ranges permit it.

When should I use Optimize Triton Ascend Int Vectors?

Optimize Triton Ascend Int Vectors fits situations like: an agent ports an int64 GPU elementwise kernel to Ascend NPU; observes scalarized integer arithmetic; designs vector addition; reduction over integer tensors.

How do I install Optimize Triton Ascend Int Vectors in Claude Code?

Run `npx skills add Krusty84/triton-ascend-agent-dev-kit --skill optimize-triton-ascend-int-vectors -a claude-code`. Or copy the skill folder (skills/optimize-triton-ascend-int-vectors in Krusty84/triton-ascend-agent-dev-kit) into .claude/skills/optimize-triton-ascend-int-vectors in your project. Claude Code loads it when a task matches its description.

How do I install Optimize Triton Ascend Int Vectors in Codex?

Run `npx skills add Krusty84/triton-ascend-agent-dev-kit --skill optimize-triton-ascend-int-vectors -a codex`. Or copy the skill folder (skills/optimize-triton-ascend-int-vectors in Krusty84/triton-ascend-agent-dev-kit) into .agents/skills/optimize-triton-ascend-int-vectors in your project. Codex loads it when a task matches its description.

Can I use Optimize Triton Ascend Int Vectors in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Krusty84/triton-ascend-agent-dev-kit --skill optimize-triton-ascend-int-vectors -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/optimize-triton-ascend-int-vectors, .gemini/skills/optimize-triton-ascend-int-vectors, .github/skills/optimize-triton-ascend-int-vectors and .opencode/skills/optimize-triton-ascend-int-vectors in your project.

What does Optimize Triton Ascend Int Vectors need to run?

SKILL.md names no scripts, command-line tools or credentials: Optimize Triton Ascend Int Vectors is instructions for the agent only. Our summary lists: Python 3.

Does Optimize Triton Ascend Int Vectors access the network?

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.

Is Optimize Triton Ascend Int Vectors safe to install?

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.

What licence does Optimize Triton Ascend Int Vectors use?

Optimize Triton Ascend Int Vectors 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.

How many tokens does Optimize Triton Ascend Int Vectors use?

About 545 tokens (SKILL.md is roughly 2.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Optimize Triton Ascend Int Vectors?

Skills that share tags, products or a category with Optimize Triton Ascend Int Vectors: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Optimize Triton Ascend Int Vectors?

Krusty84 (a GitHub user) maintains it in Krusty84/triton-ascend-agent-dev-kit, which has 106 GitHub stars. The repository holds 23 skills in this directory. The repository was last updated on August 15, 2026.

Source: Krusty84/triton-ascend-agent-dev-kit on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.