Official agent skill

Extract Asm Onednn

Extract GPU ISA from oneDNN ngen-JIT kernels. An agent skill from intel/torch-xpu-ops.

OfficialApache-2.0Auto-check passedDevelopment

Install Extract Asm Onednn

skills CLI

$ npx skills add intel/torch-xpu-ops --skill extract-asm-onednn -a claude-code

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

GitHub CLI

$ gh skill install intel/torch-xpu-ops extract-asm-onednn --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/intel/torch-xpu-ops.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/extract-asm-onednn .claude/skills/extract-asm-onednn && 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/

Paste into Claude Code

Install the "extract-asm-onednn" agent skill from https://github.com/intel/torch-xpu-ops/tree/main/.claude/skills/extract-asm-onednn into .claude/skills/extract-asm-onednn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extract-asm-onednn", 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

$skill-installer install https://github.com/intel/torch-xpu-ops/tree/main/.claude/skills/extract-asm-onednn

Type 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.

skills CLI

$ npx skills add intel/torch-xpu-ops --skill extract-asm-onednn -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install intel/torch-xpu-ops extract-asm-onednn --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/intel/torch-xpu-ops.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/extract-asm-onednn .agents/skills/extract-asm-onednn && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "extract-asm-onednn" agent skill from https://github.com/intel/torch-xpu-ops/tree/main/.claude/skills/extract-asm-onednn into .agents/skills/extract-asm-onednn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extract-asm-onednn", 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.

skills CLI

$ npx skills add intel/torch-xpu-ops --skill extract-asm-onednn -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install intel/torch-xpu-ops extract-asm-onednn --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/intel/torch-xpu-ops.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/extract-asm-onednn .cursor/skills/extract-asm-onednn && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "extract-asm-onednn" agent skill from https://github.com/intel/torch-xpu-ops/tree/main/.claude/skills/extract-asm-onednn into .cursor/skills/extract-asm-onednn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extract-asm-onednn", 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

$ gemini skills install https://github.com/intel/torch-xpu-ops.git --path .claude/skills/extract-asm-onednn

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add intel/torch-xpu-ops --skill extract-asm-onednn -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install intel/torch-xpu-ops extract-asm-onednn --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/intel/torch-xpu-ops.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/extract-asm-onednn .gemini/skills/extract-asm-onednn && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "extract-asm-onednn" agent skill from https://github.com/intel/torch-xpu-ops/tree/main/.claude/skills/extract-asm-onednn into .gemini/skills/extract-asm-onednn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extract-asm-onednn", 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.

GitHub CLI

$ gh skill install intel/torch-xpu-ops extract-asm-onednn

Installs 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).

skills CLI

$ npx skills add intel/torch-xpu-ops --skill extract-asm-onednn -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/intel/torch-xpu-ops.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/extract-asm-onednn .github/skills/extract-asm-onednn && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "extract-asm-onednn" agent skill from https://github.com/intel/torch-xpu-ops/tree/main/.claude/skills/extract-asm-onednn into .github/skills/extract-asm-onednn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extract-asm-onednn", 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.

skills CLI

$ npx skills add intel/torch-xpu-ops --skill extract-asm-onednn -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install intel/torch-xpu-ops extract-asm-onednn --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/intel/torch-xpu-ops.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/extract-asm-onednn .opencode/skills/extract-asm-onednn && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "extract-asm-onednn" agent skill from https://github.com/intel/torch-xpu-ops/tree/main/.claude/skills/extract-asm-onednn into .opencode/skills/extract-asm-onednn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extract-asm-onednn", 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.

Facts

Skill name: extract-asm-onednn
GitHub stars: 115
Token cost: ~1k tokens
SKILL.md length: 236 words
Files: 1
Skills in repo: 29
Repo updated: Oct 8, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Extract GPU ISA from oneDNN ngen-JIT kernels. An agent skill from intel/torch-xpu-ops.

Works in 3 steps: Dump raw ISA via ONEDNN_JIT_DUMP. → Disassemble with IGA ctypes. → Pin the actually-invoked kernel.
Extracting ASM from oneDNN kernels (gemmkernel
SKILL.md covers How this differs from the…, When to use, When NOT to use and Steps
Calls python3

What it does

Extract Asm Onednn is an agent skill from intel/torch-xpu-ops, published by the product's own GitHub organization. Extract GPU ISA from oneDNN ngen-JIT kernels. This is the ONLY codegen path that bypasses the standard SYCL/SPIR-V/IGC stack. oneDNN uses its own native code generator (ngen) that directly emits GPU ISA bytes — no SPIR-V, no IGC, no zebin ELF, no .debugline. Use when extracting ASM from oneDNN kernels (gemmkernel, genconvkernel), matmul, linear, conv, or SDPA-graph ops dispatched via mkldnn.

Its SKILL.md is about 1k 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 Development, covering Project scaffolding. The licence is Apache-2.0.

When your agent uses it

Extracting ASM from oneDNN kernels (gemmkernel
SDPA-graph ops dispatched via mkldnn

Example prompts

“/extract-asm-onednn”

Requirements

Python 3

Workflow steps

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

Dump raw ISA via ONEDNN_JIT_DUMP.
Disassemble with IGA ctypes.
Pin the actually-invoked kernel.

What it can do on your machine

Read from SKILL.md and the folder at commit a033aa5. 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
Shell commands in SKILL.md call:
- python3
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

Extract Asm Onednn loads about 1k tokens when it runs. Until then it costs about 104 tokens; SKILL.md has 236 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~104
When it runs · the whole SKILL.md, loaded when a task matches: ~1k

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 intel/torch-xpu-ops at commit a033aa5, republished under its Apache-2.0 licence (© intel). 236 words, ~1,022 tokens.

Download SKILL.mdSave it as .claude/skills/extract-asm-onednn/SKILL.md (or your agent's skills folder).

name: extract-asm-onednn
description: Extract GPU ISA from oneDNN ngen-JIT kernels. This is the ONLY codegen path that bypasses the standard SYCL/SPIR-V/IGC stack. oneDNN uses its own native code generator (ngen) that directly emits GPU ISA bytes — no SPIR-V, no IGC, no zebin ELF, no .debug_line. Use when extracting ASM from oneDNN kernels (gemm_kernel, gen_conv_kernel), matmul, linear, conv, or SDPA-graph ops dispatched via mkldnn.

Extract ASM from oneDNN ngen-JIT Kernels

This is the ONLY path that does NOT use the standard compilation stack. All other scenarios (SYCL AOT/JIT, Triton) go through SPIR-V → IGC → zebin. oneDNN ngen bypasses all of that.

How this differs from the standard stack

Standard stack (SYCL/Triton):
  Source → LLVM IR → SPIR-V → IGC → zebin ELF (.text + .debug_line)
                                         ↓
                                    ocloc disasm → .asm

oneDNN ngen (THIS skill):
  oneDNN C++ templates → ngen JIT → RAW ISA BYTES (no ELF, no DWARF)
                                         ↓
                                    IGA ctypes → .asm

Key consequences:

No zebin — output is raw bytes, not ELF
No .debug_line — source mapping can only use pattern recognition
No IGC — IGC_ShaderDumpEnable is useless
No SPIR-V — ngen directly emits machine instructions
Dump mechanism: ONEDNN_JIT_DUMP=1 (writes .bin files)
Disassembly: IGA library via ctypes (not ocloc disasm)

When to use

Op dispatched through oneDNN (mkldnn::*): linear / matmul / mm / bmm / conv* / _scaled_dot_product_attention (oneDNN-graph)

When NOT to use

Kernel is triton_* → use extract-asm-triton
Kernel is _ZTS… (SYCL) → use extract-asm-syclkernel-{aot,jit}
You only need primitive-level timing → use benchdnn directly

Steps

Step 0: Locate tools

bash

# libiga64.so: shipped with oneAPI debugger component
# Detect oneAPI root: check env vars first, then common install locations
ONEAPI=${ONEAPI_ROOT:-${CMPLR_ROOT:+${CMPLR_ROOT%/*}}}
if [ -z "$ONEAPI" ]; then
  for d in /opt/intel/oneapi ~/intel/oneapi /usr/local/oneapi; do
    [ -d "$d" ] && ONEAPI="$d" && break
  done
fi
IGA_LIB=$(find ${ONEAPI:?"oneAPI not found; set ONEAPI_ROOT"} -name 'libiga64.so' 2>/dev/null | head -1)
test -n "$IGA_LIB" || { echo "libiga64.so not found under $ONEAPI; install oneAPI debugger"; exit 1; }
export IGA_LIB

Dump raw ISA via ONEDNN_JIT_DUMP.

bash

OUT="<workdir>/onednn_$(date +%Y%m%d_%H%M%S)"
mkdir -p "$OUT" && cd "$OUT"

ONEDNN_JIT_DUMP=1 \
ONEAPI_DEVICE_SELECTOR=level_zero:0 \
  <repro_cmd> 2>&1 | tee run.log

ls dnnl_dump_gpu_*.bin

These are raw ISA bytes (not zebin ELF). ocloc disasm cannot read them.

Disassemble with IGA ctypes.

oneDNN .bin files are raw ISA bytes (not ELF). Use libiga64.so via Python ctypes to disassemble. No separate script needed — run inline:

bash

for bin in dnnl_dump_gpu_*.bin; do
  name=$(basename "$bin" .bin)
  python3 -c "
import ctypes, sys, pathlib
iga = ctypes.CDLL('$IGA_LIB')
raw = pathlib.Path('$bin').read_bytes()
buf = ctypes.create_string_buffer(1 << 20)  # 1MB output buffer

Platform IDs: BMG/Xe2=0x2000000, PVC=0x30000, DG2=0x30004

iga.iga_disassemble(0x2000000, raw, len(raw), buf, len(buf)) sys.stdout.write(buf.value.decode()) " > "${name}.asm" done


**Platform ID selection:**
- BMG / Xe2 / LNL: `0x2000000`
- PVC (Ponte Vecchio): `0x30000`
- DG2 (Alchemist): `0x30004`

If `iga_disassemble` returns 0 bytes, the platform ID likely doesn't
match. Try the next one in the list above.

3. **Pin the actually-invoked kernel.**

```bash
ONEDNN_VERBOSE=1 <repro_cmd> 2>&1 | grep -E '^onednn_verbose.*exec' \
  | nl -ba | tee dnnl_verbose.log
# Nth (1-indexed) exec line → dnnl_dump_gpu_*_kernel.<N-1>.bin

The largest .bin by size is typically the GEMM kernel. Cross-check with grep -c dpas <asm> (GEMM has high dpas density).

Files

Just SKILL.md in .claude/skills/extract-asm-onednn of intel/torch-xpu-ops.

Open the folder on GitHubat commit a033aa5

Compare with similar skills

Extract Asm Onednn 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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Run Nx Generatornrwl/nx	29k	2 repos	~592	Automated safety check: Notes	MIT	today
Conductor Setupgemini-cli-extensions/conductor	3.8k	—	~4.2k	Automated safety check: Pass	Apache-2.0	1 mo ago
Mirage VFS Adapter Authoringstrukto-ai/mirage	3.7k	—	~2.5k	Automated safety check: Pass	Apache-2.0	today

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Questions about Extract Asm Onednn

What does Extract Asm Onednn do?

Extract GPU ISA from oneDNN ngen-JIT kernels. An agent skill from intel/torch-xpu-ops. Extract Asm Onednn is an agent skill from intel/torch-xpu-ops, published by the product's own GitHub organization. Extract GPU ISA from oneDNN ngen-JIT kernels.

When should I use Extract Asm Onednn?

Extract Asm Onednn fits situations like: extracting ASM from oneDNN kernels (gemmkernel; SDPA-graph ops dispatched via mkldnn.

How do I install Extract Asm Onednn in Claude Code?

Run `npx skills add intel/torch-xpu-ops --skill extract-asm-onednn -a claude-code`. Or copy the skill folder (.claude/skills/extract-asm-onednn in intel/torch-xpu-ops) into .claude/skills/extract-asm-onednn in your project. Claude Code loads it when a task matches its description.

How do I install Extract Asm Onednn in Codex?

Run `npx skills add intel/torch-xpu-ops --skill extract-asm-onednn -a codex`. Or copy the skill folder (.claude/skills/extract-asm-onednn in intel/torch-xpu-ops) into .agents/skills/extract-asm-onednn in your project. Codex loads it when a task matches its description.

Can I use Extract Asm Onednn 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 intel/torch-xpu-ops --skill extract-asm-onednn -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/extract-asm-onednn, .gemini/skills/extract-asm-onednn, .github/skills/extract-asm-onednn and .opencode/skills/extract-asm-onednn in your project.

What does Extract Asm Onednn need to run?

Going by SKILL.md and its folder, Extract Asm Onednn needs the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Extract Asm Onednn 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 Extract Asm Onednn 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 Extract Asm Onednn use?

Extract Asm Onednn 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 Extract Asm Onednn use?

About 1k tokens (SKILL.md is roughly 4.1k 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 Extract Asm Onednn?

Skills that share tags, products or a category with Extract Asm Onednn: Nx Generate (nomcopter/react-mosaic, 4.8k stars), Ponytail (DavidObando/gsharp, 565 stars), Run Nx Generator (nrwl/nx, 29k stars) and Conductor Setup (gemini-cli-extensions/conductor, 3.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Extract Asm Onednn?

intel (a GitHub organization, an official publisher) maintains it in intel/torch-xpu-ops, which has 115 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on October 8, 2026.

Source: intel/torch-xpu-ops on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Extract Asm Onednn

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Extract ASM from oneDNN ngen-JIT Kernels

How this differs from the standard stack

When to use

When NOT to use

Steps

Step 0: Locate tools

Platform IDs: BMG/Xe2=0x2000000, PVC=0x30000, DG2=0x30004

Files

Compare with similar skills

Similar skills

More from intel/torch-xpu-ops

Related

Questions about Extract Asm Onednn