The Art of Debugging
stas00/the-art-of-debugging
Condensed debugging method and tool recipes for Unix, Python and PyTorch programs: crashes, hangs, segfaults, wrong output, CUDA OOM, NaN values and slowness.
Optimize or review eager CPU-only Albucore PyTorch runtime paths with benchmark-backed decisions.
Install Torch Performance Optimization
$ npx skills add albumentations-team/albucore --skill torch-performance-optimization -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install albumentations-team/albucore torch-performance-optimization --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/albumentations-team/albucore.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.codex/skills/torch-performance-optimization .claude/skills/torch-performance-optimization && 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 "torch-performance-optimization" agent skill from https://github.com/albumentations-team/albucore/tree/main/.codex/skills/torch-performance-optimization into .claude/skills/torch-performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "torch-performance-optimization", 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/albumentations-team/albucore/tree/main/.codex/skills/torch-performance-optimizationType 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 albumentations-team/albucore --skill torch-performance-optimization -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install albumentations-team/albucore torch-performance-optimization --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/albumentations-team/albucore.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.codex/skills/torch-performance-optimization .agents/skills/torch-performance-optimization && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "torch-performance-optimization" agent skill from https://github.com/albumentations-team/albucore/tree/main/.codex/skills/torch-performance-optimization into .agents/skills/torch-performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "torch-performance-optimization", 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 albumentations-team/albucore --skill torch-performance-optimization -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install albumentations-team/albucore torch-performance-optimization --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/albumentations-team/albucore.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.codex/skills/torch-performance-optimization .cursor/skills/torch-performance-optimization && 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 "torch-performance-optimization" agent skill from https://github.com/albumentations-team/albucore/tree/main/.codex/skills/torch-performance-optimization into .cursor/skills/torch-performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "torch-performance-optimization", 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/albumentations-team/albucore.git --path .codex/skills/torch-performance-optimization--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 albumentations-team/albucore --skill torch-performance-optimization -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install albumentations-team/albucore torch-performance-optimization --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/albumentations-team/albucore.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.codex/skills/torch-performance-optimization .gemini/skills/torch-performance-optimization && 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 "torch-performance-optimization" agent skill from https://github.com/albumentations-team/albucore/tree/main/.codex/skills/torch-performance-optimization into .gemini/skills/torch-performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "torch-performance-optimization", 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 albumentations-team/albucore torch-performance-optimizationInstalls 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 albumentations-team/albucore --skill torch-performance-optimization -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/albumentations-team/albucore.git skills-src && mkdir -p .github/skills && cp -r skills-src/.codex/skills/torch-performance-optimization .github/skills/torch-performance-optimization && 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 "torch-performance-optimization" agent skill from https://github.com/albumentations-team/albucore/tree/main/.codex/skills/torch-performance-optimization into .github/skills/torch-performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "torch-performance-optimization", 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 albumentations-team/albucore --skill torch-performance-optimization -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install albumentations-team/albucore torch-performance-optimization --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/albumentations-team/albucore.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.codex/skills/torch-performance-optimization .opencode/skills/torch-performance-optimization && 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 "torch-performance-optimization" agent skill from https://github.com/albumentations-team/albucore/tree/main/.codex/skills/torch-performance-optimization into .opencode/skills/torch-performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "torch-performance-optimization", 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
torch-performance-optimization- GitHub stars
- 123
- Token cost
- ~895 tokens
- SKILL.md length
- 422 words
- Files
- 2
- Skills in repo
- 5
- Repo updated
- First seen
- Licence
- MIT
At a glance
Optimize or review eager CPU-only Albucore PyTorch runtime paths with benchmark-backed decisions.
- Works in 6 steps: Establish a correctness baseline and… → Audit unnecessary Python work,… → Profile only to discover candidates.… → …
- Changing Torch CPU kernels
- SKILL.md covers Workflow, Albucore Contract and Required Handoff
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Torch Performance Optimization is an agent skill from albumentations-team/albucore. Optimize or review eager CPU-only Albucore PyTorch runtime paths with benchmark-backed decisions. Use when adding or changing Torch CPU kernels, Tensor/NumPy bridges, Torch backend routing, tensor layouts, allocations, threading, profiling, memory-format candidates, or Torch performance benchmarks.
Its SKILL.md is about 900 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).
It sits in AI & LLM Engineering, covering Performance optimization and Deep learning. It works with NumPy, PyTorch, Python and OpenCV. The repository describes itself as: A high-performance image processing library designed to optimize and extend the Albumentations library with specialized functions for advanced image transformations. Perfect for… The licence is MIT.
When your agent uses it
- Changing Torch CPU kernels
- Tensor/NumPy bridges
- Torch backend routing
- Memory-format candidates
Example prompts
- “/torch-performance-optimization”
Requirements
- Python 3
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Establish a correctness baseline and benchmark the existing public path. State container, layout, dtype, shape, parameter, stride, thread…
- Audit unnecessary Python work, full-volume passes, conversions, materializations, and output repairs before choosing a Torch operator.
- Profile only to discover candidates. Benchmark every viable implementation end to end, including NumPy↔Torch bridges and layout conversion.
- Compare NumPy, OpenCV, NumKong, StringZilla, and Torch where they share semantics. Treat fused operators, layout, channels_last_3d, and…
- Select a route or threshold only from stable public-path evidence. Preserve rejected candidates in the benchmark report when they clarify…
- Add correctness tests for each accepted route and update the benchmark evidence, public contract, and canonical guide when a reusable rule…
What it can do on your machine
Read from SKILL.md and the folder at commit 249d10b. 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.
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
Torch Performance Optimization loads about 895 tokens when it runs. Until then it costs about 83 tokens; SKILL.md has 422 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.
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 albumentations-team/albucore at commit 249d10b, republished under its MIT licence (© albumentations-team). 422 words, ~895 tokens.
Download SKILL.mdSave it as
.claude/skills/torch-performance-optimization/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- torch-performance-optimization
- description
- Optimize or review eager CPU-only Albucore PyTorch runtime paths with benchmark-backed decisions. Use when adding or changing Torch CPU kernels, Tensor/NumPy bridges, Torch backend routing, tensor layouts, allocations, threading, profiling, memory-format candidates, or Torch performance benchmarks.
Torch Performance Optimization
Read docs/torch-performance-optimization.md completely before inspecting or editing a Torch path. Also read ../performance-optimization/SKILL.md, its canonical performance guide, and ../albucore-benchmarks/SKILL.md completely.
Workflow
- Establish a correctness baseline and benchmark the existing public path. State container, layout, dtype, shape, parameter, stride, thread, and allocation contracts.
- Audit unnecessary Python work, full-volume passes, conversions, materializations, and output repairs before choosing a Torch operator.
- Profile only to discover candidates. Benchmark every viable implementation end to end, including NumPy↔Torch bridges and layout conversion.
- Compare NumPy, OpenCV, NumKong, StringZilla, and Torch where they share semantics. Treat fused operators, layout,
channels_last_3d, and in-place reuse as hypotheses with their own correctness and performance matrices. - Select a route or threshold only from stable public-path evidence. Preserve rejected candidates in the benchmark report when they clarify a boundary.
- Add correctness tests for each accepted route and update the benchmark evidence, public contract, and canonical guide when a reusable rule or limitation is discovered.
Albucore Contract
- Torch is a required dependency. Assume it is already imported for benchmarks.
- Current public Tensor routes are eager CPU paths that do not record autograd inside the primitive and do not use
torch.compile. Do not add device routes, graph-preserving primitive fallbacks, compilation, or their benchmark candidates. - Tensor layouts are explicit and independent of NumPy layouts. Do not infer
NCHWversusCDHWfrom shape sizes. warp_affine3dandremap3dsupport rank-4CDHWvolumes and rank-5NCDHWbatches; one affine matrix or remap grid is shared across the batch.- For general 3D sampling, the measured CPU path folds
N>=4, C=1batches into the channel axis for both routers.warp_affine3duses rank-5 sampling for other channel counts.remap3dsamplesN>=4, C>1items independently while sharing grid and fill preparation because that beat a single native batch sampler in the full-call benchmark. - XY-only linear affine matrices with unchanged depth share one plane grid. Single-channel inputs fold
N*Dinto 2D channels; multi-channel batches mergeNandDonly when that is a view, otherwise sampling into a preallocated output per volume. Nearest interpolation retains the original 3D sampler. - Caller-prevalidated 3D routers leave validation outside the hot path. Do not add it back while optimizing.
- A Tensor route must preserve the documented container, layout, border, interpolation, rounding, mutation, and aliasing behavior. Its dtype contract must distinguish supported dtype preservation from any explicit fallback conversion.
Show full SKILL.md (33 more words)Show less
Required Handoff
Report the full-path baseline, selected route, removed work, allocation and copy changes, all viable backends and Torch-specific candidates considered, thread and benchmark matrix, correctness/memory evidence, regressions, rejected regions, and remaining follow-ups.
© albumentations-team, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 1 other file in .codex/skills/torch-performance-optimization of albumentations-team/albucore.
- SKILL.md
- agents/openai.yaml
Open the folder on GitHubat commit 249d10b
Compare with similar skills
Torch Performance Optimization 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 |
|---|---|---|---|---|---|---|
| Torch Performance Optimization this skillalbumentations-team/albucore | 123 | — | ~895 | Automated safety check: Pass | MIT | |
| The Art of Debuggingstas00/the-art-of-debugging | 1.7k | — | ~6.1k | Automated safety check: Notes | CC-BY-SA-4.0 | |
| Magpie Kernel Evaluatoramd/skills | 395 | — | ~2.3k | Automated safety check: Pass | MIT | |
| Xtbloom Run Python Inferencejinzhezenggroup/computational-chemistry-agent-skills | 148 | — | ~1.3k | Automated safety check: Pass | LGPL-3.0 | |
| ExecuTorch Cortex-M Backendpytorch/executorch | 5.1k | — | ~872 | Automated safety check: Pass | Custom licence | |
| Formattingbrendanhasz/probflow | 175 | — | ~381 | Automated safety check: Pass | MIT |
Similar skills
Magpie Kernel Evaluator
amd/skills
Benchmarks LLM inference and drives GPU kernel optimization with Magpie.
Xtbloom Run Python Inference
jinzhezenggroup/computational-chemistry-agent-skills
Write, review, and run high-level xTBloom Python GFN2-xTB inference with Calculator, Structure, and BatchCalculator, including single systems, repeated geometry updates, heterogeneous ragged…
ExecuTorch Cortex-M Backend
pytorch/executorch
Developer guide for the Cortex-M (CMSIS-NN) backend in ExecuTorch: quantization pipeline, pass manager, tests and adding new ops.
Formatting
brendanhasz/probflow
Ensure consistent code formatting using the uv package manager and pre-commit.
Neuron Nki Writing
uw-syfi/vibesys
Guide for writing and modifying NKI kernels. An agent skill from uw-syfi/vibesys.
More from albumentations-team/albucore
Albucore Benchmarks
albumentations-team/albucore
Running Albucore micro-benchmarks under benchmarks/, synthetic router timings, and comparing PyPI releases with uv --no-project.
Albucore Conventions
albumentations-team/albucore
Albucore image processing conventions - shapes (H,W,C), dtypes (uint8/float32), benchmark-driven backend routing (OpenCV, NumPy, Torch CPU, LUT, NumKong), tests, and lockfile discipline.
Albucore Public API
albumentations-team/albucore
Albucore star-exported API (all), routers vs albucore.functions shims, and dependents such as Albumentations.
Performance Optimization
albumentations-team/albucore
Systematic performance audit for Albucore runtime code. An agent skill from albumentations-team/albucore.
Related
Categories
Questions about Torch Performance Optimization
What does Torch Performance Optimization do?
Optimize or review eager CPU-only Albucore PyTorch runtime paths with benchmark-backed decisions. Torch Performance Optimization is an agent skill from albumentations-team/albucore. Optimize or review eager CPU-only Albucore PyTorch runtime paths with benchmark-backed decisions.
When should I use Torch Performance Optimization?
Torch Performance Optimization fits situations like: changing Torch CPU kernels; tensor/NumPy bridges; torch backend routing; memory-format candidates.
How do I install Torch Performance Optimization in Claude Code?
Run `npx skills add albumentations-team/albucore --skill torch-performance-optimization -a claude-code`. Or copy the skill folder (.codex/skills/torch-performance-optimization in albumentations-team/albucore) into .claude/skills/torch-performance-optimization in your project. Claude Code loads it when a task matches its description.
How do I install Torch Performance Optimization in Codex?
Run `npx skills add albumentations-team/albucore --skill torch-performance-optimization -a codex`. Or copy the skill folder (.codex/skills/torch-performance-optimization in albumentations-team/albucore) into .agents/skills/torch-performance-optimization in your project. Codex loads it when a task matches its description.
Can I use Torch Performance Optimization 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 albumentations-team/albucore --skill torch-performance-optimization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/torch-performance-optimization, .gemini/skills/torch-performance-optimization, .github/skills/torch-performance-optimization and .opencode/skills/torch-performance-optimization in your project.
What does Torch Performance Optimization need to run?
SKILL.md names no scripts, command-line tools or credentials: Torch Performance Optimization is instructions for the agent only. Our summary lists: Python 3.
Does Torch Performance Optimization 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 Torch Performance Optimization 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 Torch Performance Optimization use?
Torch Performance Optimization is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Torch Performance Optimization use?
About 895 tokens (SKILL.md is roughly 3.6k 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 Torch Performance Optimization?
Skills that share tags, products or a category with Torch Performance Optimization: The Art of Debugging (stas00/the-art-of-debugging, 1.7k stars), Magpie Kernel Evaluator (amd/skills, 395 stars), Xtbloom Run Python Inference (jinzhezenggroup/computational-chemistry-agent-skills, 148 stars) and ExecuTorch Cortex-M Backend (pytorch/executorch, 5.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Torch Performance Optimization?
albumentations-team (a GitHub organization) maintains it in albumentations-team/albucore, which has 123 GitHub stars. The repository holds 5 skills in this directory. The repository was last updated on October 5, 2026.
Source: albumentations-team/albucore on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.