Vercel Composition Patterns
supabase/supabase
React composition patterns that scale. An agent skill from supabase/supabase.
Classifies GPU kernel bottlenecks (memory, compute, latency) from profiling metrics and applies GEAK-style workload guidance: what to prefer, consider, or deprioritize.
Install GPU Kernel Bottleneck Diagnosis
$ npx skills add ZJLi2013/awesome-kernel-skills --skill gpu-kernel-bottleneck-diagnosis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ZJLi2013/awesome-kernel-skills gpu-kernel-bottleneck-diagnosis --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/ZJLi2013/awesome-kernel-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/system/bottleneck-diagnosis .claude/skills/gpu-kernel-bottleneck-diagnosis && 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 "gpu-kernel-bottleneck-diagnosis" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/system/bottleneck-diagnosis into .claude/skills/gpu-kernel-bottleneck-diagnosis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gpu-kernel-bottleneck-diagnosis", 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/ZJLi2013/awesome-kernel-skills/tree/main/skills/system/bottleneck-diagnosisType 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 ZJLi2013/awesome-kernel-skills --skill gpu-kernel-bottleneck-diagnosis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ZJLi2013/awesome-kernel-skills gpu-kernel-bottleneck-diagnosis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ZJLi2013/awesome-kernel-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/system/bottleneck-diagnosis .agents/skills/gpu-kernel-bottleneck-diagnosis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "gpu-kernel-bottleneck-diagnosis" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/system/bottleneck-diagnosis into .agents/skills/gpu-kernel-bottleneck-diagnosis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gpu-kernel-bottleneck-diagnosis", 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 ZJLi2013/awesome-kernel-skills --skill gpu-kernel-bottleneck-diagnosis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ZJLi2013/awesome-kernel-skills gpu-kernel-bottleneck-diagnosis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ZJLi2013/awesome-kernel-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/system/bottleneck-diagnosis .cursor/skills/gpu-kernel-bottleneck-diagnosis && 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 "gpu-kernel-bottleneck-diagnosis" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/system/bottleneck-diagnosis into .cursor/skills/gpu-kernel-bottleneck-diagnosis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gpu-kernel-bottleneck-diagnosis", 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/ZJLi2013/awesome-kernel-skills.git --path skills/system/bottleneck-diagnosis--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 ZJLi2013/awesome-kernel-skills --skill gpu-kernel-bottleneck-diagnosis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ZJLi2013/awesome-kernel-skills gpu-kernel-bottleneck-diagnosis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ZJLi2013/awesome-kernel-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/system/bottleneck-diagnosis .gemini/skills/gpu-kernel-bottleneck-diagnosis && 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 "gpu-kernel-bottleneck-diagnosis" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/system/bottleneck-diagnosis into .gemini/skills/gpu-kernel-bottleneck-diagnosis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gpu-kernel-bottleneck-diagnosis", 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 ZJLi2013/awesome-kernel-skills gpu-kernel-bottleneck-diagnosisInstalls 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 ZJLi2013/awesome-kernel-skills --skill gpu-kernel-bottleneck-diagnosis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ZJLi2013/awesome-kernel-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/system/bottleneck-diagnosis .github/skills/gpu-kernel-bottleneck-diagnosis && 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 "gpu-kernel-bottleneck-diagnosis" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/system/bottleneck-diagnosis into .github/skills/gpu-kernel-bottleneck-diagnosis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gpu-kernel-bottleneck-diagnosis", 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 ZJLi2013/awesome-kernel-skills --skill gpu-kernel-bottleneck-diagnosis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ZJLi2013/awesome-kernel-skills gpu-kernel-bottleneck-diagnosis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ZJLi2013/awesome-kernel-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/system/bottleneck-diagnosis .opencode/skills/gpu-kernel-bottleneck-diagnosis && 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 "gpu-kernel-bottleneck-diagnosis" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/system/bottleneck-diagnosis into .opencode/skills/gpu-kernel-bottleneck-diagnosis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gpu-kernel-bottleneck-diagnosis", 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
gpu-kernel-bottleneck-diagnosis- GitHub stars
- 102
- Token cost
- ~992 tokens
- SKILL.md length
- 347 words
- Files
- 1
- Skills in repo
- 12
- Repo updated
- First seen
- Licence
- None found
At a glance
Classifies GPU kernel bottlenecks (memory, compute, latency) from profiling metrics and applies GEAK-style workload guidance: what to prefer, consider, or deprioritize.
- Works in 3 steps: Memory-bound → Compute-bound → Latency-bound
- Development work in your project
- SKILL.md covers Three bottleneck types, Metrics-driven flowchart…, Priority framework (GEAK-style) and Mapping to AutoKernel tiers, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
GPU Kernel Bottleneck Diagnosis is an agent skill from ZJLi2013/awesome-kernel-skills. Classifies GPU kernel bottlenecks (memory, compute, latency) from profiling metrics and applies GEAK-style workload guidance: what to prefer, consider, or deprioritize. Use after NCU/rocprof or when choosing between fusion, tiling, and scheduling changes without a clear winner.
Its SKILL.md is about 990 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.
When your agent uses it
- Development work in your project
Example prompts
- “Use the gpu-kernel-bottleneck-diagnosis skill to classify GPU kernel bottlenecks (memory, compute, latency) from profiling metrics and applies…”
- “/gpu-kernel-bottleneck-diagnosis”
Workflow steps
3 steps, taken from the step headings in SKILL.md.
- Memory-bound
- Compute-bound
- Latency-bound
What it can do on your machine
Read from SKILL.md and the folder at commit aba7662. 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
Links to these hosts (documentation or services it may open):
github.com
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
GPU Kernel Bottleneck Diagnosis loads about 992 tokens when it runs. Until then it costs about 78 tokens; SKILL.md has 347 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
Without a licence we can't republish the file, so here is its outline and opening line. It has 347 words (~992 tokens).
“Integrates GEAK-style workload_guidance with the AutoKernel six-tier playbook: diagnosis picks which tier to emphasize next.”
- name
- gpu-kernel-bottleneck-diagnosis
Files
Just SKILL.md in skills/system/bottleneck-diagnosis of ZJLi2013/awesome-kernel-skills.
Open the folder on GitHubat commit aba7662
Compare with similar skills
GPU Kernel Bottleneck Diagnosis 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 |
|---|---|---|---|---|---|---|
| GPU Kernel Bottleneck Diagnosis this skillZJLi2013/awesome-kernel-skills | 102 | — | ~992 | Automated safety check: Pass | None | |
| Vercel Composition Patternssupabase/supabase | 111k | 58 repos | ~726 | Automated safety check: Pass | MIT | |
| Finishing a Development Branchobra/superpowers | 297k | 5 repos | ~1.9k | Automated safety check: Pass | MIT | |
| Typescript Advanced Typesrolling-scopes/rsschool-app | 10k | 25 repos | ~4.2k | Automated safety check: Pass | MPL-2.0 | |
| PR Babysitteropeninterpreter/openinterpreter | 69k | 3 repos | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| Code Review ChecklistshareAI-lab/learn-claude-code | 78k | 4 repos | ~1.1k | Automated safety check: Pass | MIT |
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Typescript Advanced Types
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Master TypeScript's advanced type system including generics, conditional types, mapped types, template literals, and utility types for building type-safe applications.
PR Babysitter
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Watches an open GitHub pull request until it merges, handling review comments, diagnosing CI failures and retrying flaky checks along the way.
Code Review Checklist
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Reviews code against a five-part checklist covering security, correctness, performance, maintainability and testing, and reports findings in a fixed format.
Greploop
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Iteratively improves a PR (GitHub), MR (GitLab), or shelved changelist (Perforce) until Greptile gives it a 5/5 confidence score with zero unresolved comments.
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Flash Attention Kernel
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Optimize dense matrix multiplication (GEMM) kernels in Triton for NVIDIA and AMD GPUs.
Iterative Kernel Optimization Loop
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Orchestrates continuous kernel optimization by chaining profiling, bottleneck diagnosis, tier-based optimization, verification, and benchmarking into an iterative loop.
Kernel Benchmark
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Unified kernel benchmarking protocol producing JSON results with latency, TFLOPS, GBps, and comparison against PyTorch baselines.
Related
Categories
Questions about GPU Kernel Bottleneck Diagnosis
What does GPU Kernel Bottleneck Diagnosis do?
Classifies GPU kernel bottlenecks (memory, compute, latency) from profiling metrics and applies GEAK-style workload guidance: what to prefer, consider, or deprioritize. GPU Kernel Bottleneck Diagnosis is an agent skill from ZJLi2013/awesome-kernel-skills. Classifies GPU kernel bottlenecks (memory, compute, latency) from profiling metrics and applies GEAK-style workload guidance: what to prefer, consider, or deprioritize.
When should I use GPU Kernel Bottleneck Diagnosis?
GPU Kernel Bottleneck Diagnosis fits situations like: development work in your project.
How do I install GPU Kernel Bottleneck Diagnosis in Claude Code?
Run `npx skills add ZJLi2013/awesome-kernel-skills --skill gpu-kernel-bottleneck-diagnosis -a claude-code`. Or copy the skill folder (skills/system/bottleneck-diagnosis in ZJLi2013/awesome-kernel-skills) into .claude/skills/gpu-kernel-bottleneck-diagnosis in your project. Claude Code loads it when a task matches its description.
How do I install GPU Kernel Bottleneck Diagnosis in Codex?
Run `npx skills add ZJLi2013/awesome-kernel-skills --skill gpu-kernel-bottleneck-diagnosis -a codex`. Or copy the skill folder (skills/system/bottleneck-diagnosis in ZJLi2013/awesome-kernel-skills) into .agents/skills/gpu-kernel-bottleneck-diagnosis in your project. Codex loads it when a task matches its description.
Can I use GPU Kernel Bottleneck Diagnosis 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 ZJLi2013/awesome-kernel-skills --skill gpu-kernel-bottleneck-diagnosis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gpu-kernel-bottleneck-diagnosis, .gemini/skills/gpu-kernel-bottleneck-diagnosis, .github/skills/gpu-kernel-bottleneck-diagnosis and .opencode/skills/gpu-kernel-bottleneck-diagnosis in your project.
What does GPU Kernel Bottleneck Diagnosis need to run?
SKILL.md names no scripts, command-line tools or credentials: GPU Kernel Bottleneck Diagnosis is instructions for the agent only.
Does GPU Kernel Bottleneck Diagnosis access the network?
SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.
Is GPU Kernel Bottleneck Diagnosis 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 GPU Kernel Bottleneck Diagnosis use?
No licence was found for GPU Kernel Bottleneck Diagnosis or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.
How many tokens does GPU Kernel Bottleneck Diagnosis use?
About 992 tokens (SKILL.md is roughly 4k 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 GPU Kernel Bottleneck Diagnosis?
Skills that share tags, products or a category with GPU Kernel Bottleneck Diagnosis: Vercel Composition Patterns (supabase/supabase, 111k stars), Finishing a Development Branch (obra/superpowers, 297k stars), Typescript Advanced Types (rolling-scopes/rsschool-app, 10k stars) and PR Babysitter (openinterpreter/openinterpreter, 69k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains GPU Kernel Bottleneck Diagnosis?
ZJLi2013 (a GitHub user) maintains it in ZJLi2013/awesome-kernel-skills, which has 102 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on March 31, 2026.
Source: ZJLi2013/awesome-kernel-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.