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Agent skill

Optimize Triton Ascend Discrete Loads

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

Optimize bounded discrete reads in Triton-Ascend by staging a contiguous source vector in UB and selecting elements with tl.gather.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Optimize Triton Ascend Discrete Loads

skills CLI
$ npx skills add Krusty84/triton-ascend-agent-dev-kit --skill optimize-triton-ascend-discrete-loads -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-discrete-loads --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-discrete-loads .claude/skills/optimize-triton-ascend-discrete-loads && 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-discrete-loads
GitHub stars
106
Token cost
~517 tokens
SKILL.md length
187 words
Files
1
Skills in repo
23
Repo updated
First seen
Licence
Apache-2.0

At a glance

Optimize bounded discrete reads in Triton-Ascend by staging a contiguous source vector in UB and selecting elements with tl.gather.

  • Works in 5 steps: Confirm the source domain length M is… → Load x[0:M] contiguously into an on-chip… → Load the N indices and validate both… → …
  • An agent implements out = x[idx]
  • 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 Discrete Loads is an agent skill from Krusty84/triton-ascend-agent-dev-kit. Optimize bounded discrete reads in Triton-Ascend by staging a contiguous source vector in UB and selecting elements with tl.gather. Use when an agent implements out = x[idx], ports arbitrary global-memory gathers from GPU, or sees scalarized indirect loads and the complete source domain is small enough to fit on chip.

Its SKILL.md is about 520 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 implements out = x[idx]
  • Ports arbitrary global-memory gathers from GPU
  • Sees scalarized indirect loads and the complete source domain is small enough to fit on chip

Example prompts

  • “/optimize-triton-ascend-discrete-loads”

Requirements

  • Python 3

Workflow steps

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

  1. Confirm the source domain length M is bounded and fits the UB budget.
  2. Load x[0:M] contiguously into an on-chip vector.
  3. Load the N indices and validate both lower and upper bounds.
  4. Replace invalid indices with a safe value before tl.gather.
  5. Gather along the source axis and store only valid outputs.

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 Discrete Loads loads about 517 tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 187 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~89
When it runs · the whole SKILL.md, loaded when a task matches
~517

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). 187 words, ~517 tokens.

Download SKILL.mdSave it as .claude/skills/optimize-triton-ascend-discrete-loads/SKILL.md (or your agent's skills folder).
name
optimize-triton-ascend-discrete-loads
description
Optimize bounded discrete reads in Triton-Ascend by staging a contiguous source vector in UB and selecting elements with tl.gather. Use when an agent implements out = x[idx], ports arbitrary global-memory gathers from GPU, or sees scalarized indirect loads and the complete source domain is small enough to fit on chip.

Optimize Triton-Ascend Discrete Loads

Goal

Replace repeated indirect global reads with one contiguous global-to-UB transfer followed by on-chip gather.

Workflow

  1. Confirm the source domain length M is bounded and fits the UB budget.
  2. Load x[0:M] contiguously into an on-chip vector.
  3. Load the N indices and validate both lower and upper bounds.
  4. Replace invalid indices with a safe value before tl.gather.
  5. Gather along the source axis and store only valid outputs.

Implementation Pattern

python
source_offsets = tl.arange(0, M)
source = tl.load(x_ptr + source_offsets * stride_x)

out_offsets = tl.arange(0, N)
indices = tl.load(index_ptr + out_offsets * stride_index)
valid = (indices >= 0) & (indices < M)
safe_indices = tl.where(valid, indices, 0)
values = tl.gather(source, safe_indices, axis=0)
tl.store(out_ptr + out_offsets * stride_out, values, mask=valid)

Ascend Guardrails

  • Stage the whole source only when M plus indices, outputs, padding, and temporaries fit UB.
  • Reject invalid indices before launch when the API requires every output element to be defined.
  • Keep indices integer and ensure tl.gather supports their dtype.
  • Preserve explicit source/index/output strides.
  • For large M, partition by index ranges or choose a different algorithm; loading the entire source per program can cost more than indirect access.
  • Avoid launching multiple identical programs when the kernel has no program-dependent offsets.

Verification

Compare with x[idx] for permutations, repeats, sorted and random indices. Add negative and M-or-larger index tests, and benchmark across M/N ratios to identify when UB staging is beneficial.

© 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-discrete-loads of Krusty84/triton-ascend-agent-dev-kit.

Open the folder on GitHubat commit 4ab5ee7

Compare with similar skills

Optimize Triton Ascend Discrete Loads 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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Add Uint Supportpytorch/pytorch104k2 repos~2.3kAutomated safety check: PassCustom licence
LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs13k8 repos~3kAutomated safety check: PassMIT
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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 Discrete Loads

What does Optimize Triton Ascend Discrete Loads do?

Optimize bounded discrete reads in Triton-Ascend by staging a contiguous source vector in UB and selecting elements with tl.gather. Optimize Triton Ascend Discrete Loads is an agent skill from Krusty84/triton-ascend-agent-dev-kit.gather.

When should I use Optimize Triton Ascend Discrete Loads?

Optimize Triton Ascend Discrete Loads fits situations like: an agent implements out = x[idx]; ports arbitrary global-memory gathers from GPU; sees scalarized indirect loads and the complete source domain is small enough to fit on chip.

How do I install Optimize Triton Ascend Discrete Loads in Claude Code?

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

How do I install Optimize Triton Ascend Discrete Loads in Codex?

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

Can I use Optimize Triton Ascend Discrete Loads 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-discrete-loads -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-discrete-loads, .gemini/skills/optimize-triton-ascend-discrete-loads, .github/skills/optimize-triton-ascend-discrete-loads and .opencode/skills/optimize-triton-ascend-discrete-loads in your project.

What does Optimize Triton Ascend Discrete Loads need to run?

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

Does Optimize Triton Ascend Discrete Loads 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 Discrete Loads 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 Discrete Loads use?

Optimize Triton Ascend Discrete Loads 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 Discrete Loads use?

About 517 tokens (SKILL.md is roughly 2.1k 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 Discrete Loads?

Skills that share tags, products or a category with Optimize Triton Ascend Discrete Loads: 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 Discrete Loads?

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.