Veomni Debug
ByteDance-Seed/VeOmni
A skill your agent uses for ANY bug, error, crash, wrong output, loss divergence, gradient explosion, test failure, CUDA error, distributed training hang, checkpoint load failure, or unexpected…
Debug crashes and test failures via stack-traces, host/device logging, and DSL buffer inspection.
$ npx skills add LuisaGroup/LuisaCompute --skill debug -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LuisaGroup/LuisaCompute debug --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/LuisaGroup/LuisaCompute.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/debug .claude/skills/debug && 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 "debug" agent skill from https://github.com/LuisaGroup/LuisaCompute/tree/stable/.agents/skills/debug into .claude/skills/debug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "debug", 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/LuisaGroup/LuisaCompute/tree/stable/.agents/skills/debugType 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 LuisaGroup/LuisaCompute --skill debug -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LuisaGroup/LuisaCompute debug --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LuisaGroup/LuisaCompute.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/debug .agents/skills/debug && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "debug" agent skill from https://github.com/LuisaGroup/LuisaCompute/tree/stable/.agents/skills/debug into .agents/skills/debug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "debug", 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 LuisaGroup/LuisaCompute --skill debug -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LuisaGroup/LuisaCompute debug --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LuisaGroup/LuisaCompute.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/debug .cursor/skills/debug && 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 "debug" agent skill from https://github.com/LuisaGroup/LuisaCompute/tree/stable/.agents/skills/debug into .cursor/skills/debug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "debug", 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/LuisaGroup/LuisaCompute.git --path .agents/skills/debug--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 LuisaGroup/LuisaCompute --skill debug -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LuisaGroup/LuisaCompute debug --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LuisaGroup/LuisaCompute.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/debug .gemini/skills/debug && 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 "debug" agent skill from https://github.com/LuisaGroup/LuisaCompute/tree/stable/.agents/skills/debug into .gemini/skills/debug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "debug", 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 LuisaGroup/LuisaCompute debugInstalls 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 LuisaGroup/LuisaCompute --skill debug -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/LuisaGroup/LuisaCompute.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/debug .github/skills/debug && 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 "debug" agent skill from https://github.com/LuisaGroup/LuisaCompute/tree/stable/.agents/skills/debug into .github/skills/debug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "debug", 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 LuisaGroup/LuisaCompute --skill debug -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install LuisaGroup/LuisaCompute debug --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LuisaGroup/LuisaCompute.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/debug .opencode/skills/debug && 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 "debug" agent skill from https://github.com/LuisaGroup/LuisaCompute/tree/stable/.agents/skills/debug into .opencode/skills/debug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "debug", 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.
debugDebug crashes and test failures via stack-traces, host/device logging, and DSL buffer inspection.
Debug is an agent skill from LuisaGroup/LuisaCompute. Debug crashes and test failures via stack-traces, host/device logging, and DSL buffer inspection.
Its SKILL.md is about 2.3k 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 Debugging and Failing and flaky tests. It works with CUDA. The repository describes itself as: High-Performance Rendering Framework on Stream Architectures. The licence is Apache-2.0.
8 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 0c84a1a. 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.
Shell commands in SKILL.md call:
pythonFrom 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.
Debug loads about 2.3k tokens when it runs. Until then it costs about 26 tokens; SKILL.md has 850 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 LuisaGroup/LuisaCompute at commit 0c84a1a, republished under its Apache-2.0 licence (© LuisaGroup). 850 words, ~2,287 tokens.
.claude/skills/debug/SKILL.md (or your agent's skills folder).When a crash or LUISA_ERROR is emitted, capture the full console output first.
What to look for:
luisa::, especially luisa::compute:: or luisa::dsl::.cuda, dx, metal, cpu backend symbols tell you which path failed.LUISA_INFO/LUISA_VERBOSE shows the dispatch or shader name that triggered the bug.Action:
Device::compile, Stream::dispatch, Buffer::copy_from). That is the call-site.Once the stack-trace points to a file/line or API call, write a debug plan in this order:
If the fix fails:
Silent failures (hang, wrong result, test timeout) provide no trace.
Find the entry point:
CMakeLists.txt or xmake.lua near the failing target to locate the executable source file and its main().test_device.h, boost::ut) and how the device is created.Add host-side logging:
#include <luisa/core/logging.h>
// In host code (C++ runtime)
LUISA_VERBOSE("Entering {}::{}", __FILE__, __func__);
LUISA_INFO("Buffer size = {}", buf.size());
LUISA_VERBOSE_WITH_LOCATION("Dispatching kernel X");Set log level early (before Context creation if possible):
luisa::log_level_verbose(); // or log_level_info()Progressive narrowing:
main() and at every major phase (context → device → stream → compile → dispatch).Inside kernels, use device_log to emit per-thread messages. They are collected by the stream and flushed to the host callback or default logger.
Basic usage:
#include <luisa/dsl/syntax.h>
#include <luisa/dsl/sugar.h>
Kernel2D k = [&]() noexcept {
UInt2 coord = dispatch_id().xy();
$if (coord.x == 1) {
device_log("hello {} {}", coord, make_float3x3());
};
};Custom log callback on the stream:
Stream stream = device.create_stream();
stream.set_log_callback([](luisa::string_view message) {
LUISA_INFO("device: {}", message);
});
stream << shader().dispatch(128u, 128u) << synchronize();Structured severity prefixes (for custom routing):
// Example pattern from test_printer_custom_callback.cpp
#define DEVICE_INFO(FMT, ...) \
device_log(luisa::format("I" FMT) __VA_OPT__(, ) __VA_ARGS__)
#define DEVICE_WARNING(FMT, ...) \
device_log(luisa::format("W" FMT) __VA_OPT__(, ) __VA_ARGS__)
#define DEVICE_ERROR(FMT, ...) \
device_log(luisa::format("E" FMT) __VA_OPT__(, ) __VA_ARGS__)
stream.set_log_callback([](luisa::string_view msg) {
if (!msg.empty()) {
switch (msg.front()) {
case 'I': luisa::log_info("{}", msg.substr(1)); break;
case 'W': luisa::log_warning("{}", msg.substr(1)); break;
case 'E': luisa::log_error("{}", msg.substr(1)); break;
default: luisa::log_verbose("{}", msg); break;
}
}
});Important: Device logs are asynchronous. Always synchronize() the stream before assuming all logs have arrived. If a kernel hangs, the callback may never fire for logs buffered inside the failing dispatch.
When you need to inspect many values or avoid per-thread log flooding, write results into a Buffer and read back on the host.
Buffer-based inspection:
#include <luisa/core/stl/vector.h>
#include <luisa/dsl/syntax.h>
#include <luisa/dsl/sugar.h>
Buffer<float4> debug_buf = device.create_buffer<float4>(1024);
Kernel1D k = [](BufferVar<float4> out) noexcept {
UInt idx = dispatch_id().x;
Float4 v = make_float4(cast<float>(idx),
cast<float>(idx) * 2.0f,
cast<float>(idx) * 3.0f,
0.0f);
out.write(idx, v);
};
auto shader = device.compile(k);
stream << shader(debug_buf).dispatch(1024)
<< synchronize();
// Read back
luisa::vector<float4> host(1024);
stream << debug_buf.copy_to(luisa::span{host}) << synchronize();
for (size_t i = 0; i < 8; ++i) {
LUISA_INFO("host[{}] = {}", i, host[i]);
}Reducer pattern for conditional values:
Buffer<uint> counter at index 0.debug_buf[counter].| Variable | Effect |
|---|---|
LUISA_DUMP_SOURCE=1 | Dumps generated shader sources/bytecode for the active backend. |
LUISA_LOG_LEVEL=verbose | Equivalent to log_level_verbose() at startup. |
LUISA_ENABLE_VALIDATION=1 | Wraps the device in the validation layer (catches API misuse, out-of-bounds accesses, etc.). |
LUISA_OPTIX_VALIDATION=1 | Enables OptiX validation on the CUDA backend. |
Use LUISA_DUMP_SOURCE=1 when you suspect a code-generation bug (wrong instruction, missing binding, incorrect type).
Where to find the dumps:
hlsl_output_<name>.hlsl in the current working directory.spv_code_<name>.spvasm in the current working directory.spv_code_llvm_<name>.spvasm.hlsl_output_<name>.hlsl; ordinary Device::compile(Function) compute shaders must not..cu source in the runtime .cache directory; PTX/metadata in the runtime .data directory..metal source in the runtime .cache directory.LUISA_DUMP_SOURCE and may dump intermediate sources.The runtime directories are printed by LUISA_INFO at context creation; they default to the executable directory. When running under xmake run, dumps written directly to the current working directory will appear in the project root.
| Symptom | First Action | Next Action |
|---|---|---|
| Crash with stack-trace | Read innermost + first Luisa frame | Hypothesize → plan → fix |
| Silent wrong result | Add LUISA_INFO at host entry points | Use buffer read-back to inspect values |
| Kernel dispatch hangs | Check synchronize() and stream callback | Add minimal device_log at start of kernel |
| Backend compilation error | Set LUISA_DUMP_SOURCE=1 | Inspect generated .spvasm or .hlsl |
| Suspected API/resource misuse | Set LUISA_ENABLE_VALIDATION=1 | Re-run and read validation messages |
| Test timeout | Read build file for target entry | Narrow phase with host logging |
scripts/debugger.pyA lightweight Python debugger using Windows Debug API + DbgHelp.dll to launch an x64 executable, catch second-chance exceptions, and print a symbolic stack trace from PDB symbols.
Usage:
python scripts/debugger.py <path_to_exe> [pdb_search_path] [-- <args>...]-- are forwarded to the target executable.pdb_search_path.Example:
python scripts/debugger.py build/bin/test.exe -- --gtest_filter=MyTestStepMemory saves failed attempts.CMakeLists.txt/xmake.lua, add LUISA_INFO/LUISA_VERBOSE, then device_log.Buffer write + host read-back for bulk inspection; use device_log for targeted per-thread messages.LUISA_DUMP_SOURCE=1 to inspect generated shaders and LUISA_ENABLE_VALIDATION=1 to catch API/resource misuse.© LuisaGroup, 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
Just SKILL.md in .agents/skills/debug of LuisaGroup/LuisaCompute.
Open the folder on GitHubat commit 0c84a1a
Debug 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 |
|---|---|---|---|---|---|---|
| Debug this skillLuisaGroup/LuisaCompute | 1.1k | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Veomni DebugByteDance-Seed/VeOmni | 2.2k | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Aoti Debugpytorch/pytorch | 104k | 1 repos | ~1.7k | Automated safety check: Pass | Custom licence | |
| Systematic Debuggingultralisp/ultralisp | 258 | 51 repos | ~2.4k | Automated safety check: Pass | None | |
| Exposed Bug Fix WorkflowJetBrains/Exposed | 9.3k | — | ~3.8k | Automated safety check: Pass | Apache-2.0 | |
| Debug Distributed Hangsgl-project/sglang | 37k | 2 repos | ~2.4k | Automated safety check: Pass | Apache-2.0 |
ByteDance-Seed/VeOmni
A skill your agent uses for ANY bug, error, crash, wrong output, loss divergence, gradient explosion, test failure, CUDA error, distributed training hang, checkpoint load failure, or unexpected…
pytorch/pytorch
Debug AOTInductor (AOTI) errors and crashes. An agent skill from pytorch/pytorch.
ultralisp/ultralisp
A skill your agent uses when encountering any bug, test failure, or unexpected behavior, before proposing fixes
JetBrains/Exposed
Takes a GitHub or YouTrack issue for the Exposed project through reproduction, a failing test, a fix, validation and a pull request.
sgl-project/sglang
Debug hanging issues in SGLang distributed inference (TP/PP/DP/EP).
microsoft/onnxruntime
Explains why editing CUTLASS fused-MHA headers in ONNX Runtime can leave stale CUDA kernels after an incremental build, and how to force and verify a real rebuild.
LuisaGroup/LuisaCompute
Show uncommitted changes and commit history via git. An agent skill from LuisaGroup/LuisaCompute.
LuisaGroup/LuisaCompute
Manual AST construction with FunctionBuilder for kernels and callables without DSL sugar.
LuisaGroup/LuisaCompute
CMake build options, custom functions, and backend patterns for LuisaCompute.
LuisaGroup/LuisaCompute
C++ naming, formatting, static analysis, and RTTI rules for LuisaCompute.
LuisaGroup/LuisaCompute
HLSL code generation, StringBuilder patterns, builtin headers, and DXIL embedding.
LuisaGroup/LuisaCompute
Experimental Vulkan AST-to-LLVM-to-SPIR-V backend, its fail-closed runtime-interface boundary, LLVM build integration, and validation path.
Works with
Categories
Debug crashes and test failures via stack-traces, host/device logging, and DSL buffer inspection. Debug is an agent skill from LuisaGroup/LuisaCompute. Debug crashes and test failures via stack-traces, host/device logging, and DSL buffer inspection.
Debug fits situations like: tasks that involve Debugging; tasks that involve Failing and flaky tests.
Run `npx skills add LuisaGroup/LuisaCompute --skill debug -a claude-code`. Or copy the skill folder (.agents/skills/debug in LuisaGroup/LuisaCompute) into .claude/skills/debug in your project. Claude Code loads it when a task matches its description.
Run `npx skills add LuisaGroup/LuisaCompute --skill debug -a codex`. Or copy the skill folder (.agents/skills/debug in LuisaGroup/LuisaCompute) into .agents/skills/debug 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 LuisaGroup/LuisaCompute --skill debug -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/debug, .gemini/skills/debug, .github/skills/debug and .opencode/skills/debug in your project.
Going by SKILL.md and its folder, Debug needs the command-line tools its instructions call (python). 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.
Debug 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 9.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Debug: Veomni Debug (ByteDance-Seed/VeOmni, 2.2k stars), Aoti Debug (pytorch/pytorch, 104k stars), Systematic Debugging (ultralisp/ultralisp, 258 stars) and Exposed Bug Fix Workflow (JetBrains/Exposed, 9.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
LuisaGroup (a GitHub organization) maintains it in LuisaGroup/LuisaCompute, which has 1,051 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 6, 2026.
Source: LuisaGroup/LuisaCompute on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.