Agent skill

Cuda Profiling

by mohitmishra786 in mohitmishra786/low-level-dev-skills

CUDA profiling skill for NVIDIA GPU performance analysis. An agent skill from mohitmishra786/low-level-dev-skills.

MITAuto-check: notesAI & LLM Engineering

Install Cuda Profiling

skills CLI

$ npx skills add mohitmishra786/low-level-dev-skills --skill cuda-profiling -a claude-code

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

GitHub CLI

$ gh skill install mohitmishra786/low-level-dev-skills cuda-profiling --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/mohitmishra786/low-level-dev-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/gpu/cuda-profiling .claude/skills/cuda-profiling && 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 "cuda-profiling" agent skill from https://github.com/mohitmishra786/low-level-dev-skills/tree/main/skills/gpu/cuda-profiling into .claude/skills/cuda-profiling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cuda-profiling", 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/mohitmishra786/low-level-dev-skills/tree/main/skills/gpu/cuda-profiling

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 mohitmishra786/low-level-dev-skills --skill cuda-profiling -a codex

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

GitHub CLI

$ gh skill install mohitmishra786/low-level-dev-skills cuda-profiling --agent codex

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

Manual copy

$ git clone --depth 1 https://github.com/mohitmishra786/low-level-dev-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/gpu/cuda-profiling .agents/skills/cuda-profiling && 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 "cuda-profiling" agent skill from https://github.com/mohitmishra786/low-level-dev-skills/tree/main/skills/gpu/cuda-profiling into .agents/skills/cuda-profiling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cuda-profiling", 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 mohitmishra786/low-level-dev-skills --skill cuda-profiling -a cursor

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

GitHub CLI

$ gh skill install mohitmishra786/low-level-dev-skills cuda-profiling --agent cursor

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

Manual copy

$ git clone --depth 1 https://github.com/mohitmishra786/low-level-dev-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/gpu/cuda-profiling .cursor/skills/cuda-profiling && 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 "cuda-profiling" agent skill from https://github.com/mohitmishra786/low-level-dev-skills/tree/main/skills/gpu/cuda-profiling into .cursor/skills/cuda-profiling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cuda-profiling", 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/mohitmishra786/low-level-dev-skills.git --path skills/gpu/cuda-profiling

--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 mohitmishra786/low-level-dev-skills --skill cuda-profiling -a gemini-cli

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

GitHub CLI

$ gh skill install mohitmishra786/low-level-dev-skills cuda-profiling --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/mohitmishra786/low-level-dev-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/gpu/cuda-profiling .gemini/skills/cuda-profiling && 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 "cuda-profiling" agent skill from https://github.com/mohitmishra786/low-level-dev-skills/tree/main/skills/gpu/cuda-profiling into .gemini/skills/cuda-profiling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cuda-profiling", 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 mohitmishra786/low-level-dev-skills cuda-profiling

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 mohitmishra786/low-level-dev-skills --skill cuda-profiling -a github-copilot

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

Manual copy

$ git clone --depth 1 https://github.com/mohitmishra786/low-level-dev-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/gpu/cuda-profiling .github/skills/cuda-profiling && 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 "cuda-profiling" agent skill from https://github.com/mohitmishra786/low-level-dev-skills/tree/main/skills/gpu/cuda-profiling into .github/skills/cuda-profiling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cuda-profiling", 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 mohitmishra786/low-level-dev-skills --skill cuda-profiling -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 mohitmishra786/low-level-dev-skills cuda-profiling --agent opencode

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

Manual copy

$ git clone --depth 1 https://github.com/mohitmishra786/low-level-dev-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/gpu/cuda-profiling .opencode/skills/cuda-profiling && 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 "cuda-profiling" agent skill from https://github.com/mohitmishra786/low-level-dev-skills/tree/main/skills/gpu/cuda-profiling into .opencode/skills/cuda-profiling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cuda-profiling", 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: cuda-profiling
GitHub stars: 253
Token cost: ~1.6k tokens
SKILL.md length: 364 words
Files: 1
Skills in repo: 138
Repo updated: Jun 27, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

CUDA profiling skill for NVIDIA GPU performance analysis. An agent skill from mohitmishra786/low-level-dev-skills.

Works in 8 steps: Choose profiling tool → Nsight Systems — timeline profiling → NVTX range annotations → …
Profiling kernels with Nsight Systems
SKILL.md covers Purpose, When to Use, Workflow and Common Problems, plus 1 more section
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Cuda Profiling is an agent skill from mohitmishra786/low-level-dev-skills. CUDA profiling skill for NVIDIA GPU performance analysis. Use when profiling kernels with Nsight Systems or Nsight Compute, interpreting roofline models, diagnosing memory-bound vs compute-bound kernels, or annotating code with NVTX ranges. Activates on queries about Nsight, NCU, ncu CLI, GPU roofline, occupancy metrics, or CUDA profiling workflow.

Its SKILL.md is about 1.6k 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, covering Performance optimization and GPU and accelerator computing. It works with CUDA and NVIDIA AI Platform. The repository describes itself as: A curated suite of AI agent skills for systems and low-level programming with C/C++, Rust, and Zig toolchains, covering compilers, debuggers, profilers, build systems…. The licence is MIT.

When your agent uses it

Profiling kernels with Nsight Systems
Interpreting roofline models
Diagnosing memory-bound vs compute-bound kernels
Annotating code with NVTX ranges

Example prompts

“/cuda-profiling”

Workflow steps

8 steps, taken from the step headings in SKILL.md.

Choose profiling tool
Nsight Systems — timeline profiling
NVTX range annotations
Nsight Compute — kernel analysis
NCU CLI metrics
Memory-bound vs compute-bound diagnosis
Occupancy analysis
Profiling workflow checklist

What it can do on your machine

Read from SKILL.md and the folder at commit bdc5847. 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 bash and cpp).
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

Cuda Profiling loads about 1.6k tokens when it runs. Until then it costs about 91 tokens; SKILL.md has 364 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~91
When it runs · the whole SKILL.md, loaded when a task matches: ~1.6k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

NoteRuns commands with sudoSKILL.md:177

ficient profiling permissions | Run with sudo or set `NVreg_RestrictProfilingToAdminUsers=0` |

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 mohitmishra786/low-level-dev-skills at commit bdc5847, republished under its MIT licence (© mohitmishra786). 364 words, ~1,615 tokens.

Download SKILL.mdSave it as .claude/skills/cuda-profiling/SKILL.md (or your agent's skills folder).

name: cuda-profiling
description: CUDA profiling skill for NVIDIA GPU performance analysis. Use when profiling kernels with Nsight Systems or Nsight Compute, interpreting roofline models, diagnosing memory-bound vs compute-bound kernels, or annotating code with NVTX ranges. Activates on queries about Nsight, NCU, ncu CLI, GPU roofline, occupancy metrics, or CUDA profiling workflow.

CUDA Profiling

Purpose

Guide agents through profiling CUDA applications with Nsight Systems (timeline-level) and Nsight Compute (kernel-level metrics), using the NCU CLI for automated metric collection, interpreting roofline models, and diagnosing whether kernels are memory-bound or compute-bound.

When to Use

A CUDA kernel is slower than expected and you need bottleneck identification
Comparing kernel variants (tiling strategies, block sizes)
Building CI performance regression checks with ncu metrics
Correlating CPU and GPU activity in multi-stream pipelines
Annotating application phases with NVTX for timeline visibility
Interpreting occupancy, memory throughput, and SM utilization metrics

Workflow

1. Choose profiling tool

What do you need?
├── System-wide timeline (CPU+GPU+CUDA API) → Nsight Systems (nsys)
├── Per-kernel deep metrics (occupancy, memory) → Nsight Compute (ncu)
└── Quick metric from CLI in CI → ncu --metrics ...

2. Nsight Systems — timeline profiling

bash

# Profile entire application
nsys profile --trace=cuda,nvtx,osrt --output=report ./my_cuda_app

# Open report
nsys-ui report.nsys-rep

# CLI summary
nsys stats report.nsys-rep

What to look for in the timeline:

Gaps between kernel launches (CPU bottleneck or sync points)
cudaDeviceSynchronize stalls
Overlap between H2D copies and kernel execution across streams
CUDA API call overhead

bash

# Capture with CUDA graph info
nsys profile --capture-range=cudaProfilerApi ./my_cuda_app

3. NVTX range annotations

cpp

#include <nvtx3/nvToolsExt.h>

void pipeline(void) {
    nvtxRangePushA("H2D copy");
    cudaMemcpyAsync(d_in, h_in, size, cudaMemcpyHostToDevice, stream);
    nvtxRangePop();

    nvtxRangePushA("kernel");
    my_kernel<<<grid, block, 0, stream>>>(d_in, d_out, n);
    nvtxRangePop();

    nvtxRangePushA("D2H copy");
    cudaMemcpyAsync(h_out, d_out, size, cudaMemcpyDeviceToHost, stream);
    nvtxRangePop();
}

Compile with -lnvToolsExt or link nvtx3 header-only. Ranges appear as colored bands in Nsight Systems.

4. Nsight Compute — kernel analysis

bash

# Profile all kernels, save report
ncu -o kernel_report ./my_cuda_app

# Profile specific kernel by name
ncu --kernel-name regex:matmul_tiled ./my_cuda_app

# Launch UI
ncu-ui kernel_report.ncu-rep

Key sections in NCU report:

Speed of Light: SM throughput vs memory throughput vs peak
Occupancy: Active warps vs hardware limit
Memory Workload Analysis: L1/L2 hit rates, coalescing efficiency
Warp State Statistics: Stall reasons (memory, barrier, dispatch)

5. NCU CLI metrics

bash

# Essential metrics set
ncu --metrics \
  sm__throughput.avg.pct_of_peak_sustained_elapsed,\
  dram__throughput.avg.pct_of_peak_sustained_elapsed,\
  sm__warps_active.avg.pct_of_peak_sustained_active,\
  l1tex__t_sectors_pipe_lsu_mem_global_op_ld.sum,\
  smsp__sass_thread_inst_executed_op_ffma_pred_on.sum \
  ./my_cuda_app

# CSV export for CI
ncu --csv --metrics dram__bytes_read.sum,dram__bytes_write.sum ./my_cuda_app

# Set kernel replay mode for accurate counters
ncu --kernel-replay-mode application ./my_cuda_app

6. Memory-bound vs compute-bound diagnosis

Roofline interpretation
├── dram__throughput near peak AND sm__throughput low → memory-bound
│   └── Fix: coalescing, shared mem tiling, reduce traffic
├── sm__throughput near peak AND dram low → compute-bound
│   └── Fix: tensor cores, loop unrolling, ILP
└── Both low → launch config, occupancy, or sync overhead

Roofline model (conceptual):

Performance (GFLOP/s)
    |     /\  compute roof
    |    /  \
    |   /    \____ memory roof (bandwidth-limited region)
    |  /
    +------------------ Arithmetic Intensity (FLOP/byte)

Measure arithmetic intensity: smsp__sass_thread_inst_executed_op_ffma_pred_on.sum * 2 / dram__bytes.sum

Show full SKILL.md (153 more words)Show less

7. Occupancy analysis

bash

ncu --metrics sm__warps_active.avg.pct_of_peak_sustained_active,\
launch__occupancy_limit_registers,\
launch__occupancy_limit_shared_mem,\
launch__occupancy_limit_block_size \
./my_cuda_app

Limiting factor	Typical fix
Registers	`-maxrregcount`, simplify kernel
Shared memory	Reduce tile size, split phases
Block size	Try 128 or 256 instead of 512+

8. Profiling workflow checklist

bash

# 1. Build with line info (not -G unless debugging)
nvcc -lineinfo -O3 -arch=sm_80 -o app main.cu

# 2. Timeline first
nsys profile --trace=cuda,nvtx -o timeline ./app

# 3. Deep dive on hot kernel
ncu --kernel-name regex:hot_kernel --set full ./app

# 4. Compare before/after
ncu --csv --metrics sm__throughput.avg.pct_of_peak_sustained_elapsed ./app_v1 > v1.csv
ncu --csv --metrics sm__throughput.avg.pct_of_peak_sustained_elapsed ./app_v2 > v2.csv

Common Problems

Symptom	Cause	Fix
`ERR_NVGPUCTRPERM`	Insufficient profiling permissions	Run with sudo or set `NVreg_RestrictProfilingToAdminUsers=0`
All metrics show zero	Profiling disabled or wrong GPU	Check `CUDA_VISIBLE_DEVICES`; use `--target-processes all`
NCU report empty	Kernel too short or not launched	Increase workload; verify `cudaGetLastError()`
Huge profiling overhead	Full metric sets on many kernels	Use `--kernel-name` filter; `--launch-skip`
Timeline shows no overlap	Single default stream	Create multiple streams; use async copies
Occupancy looks fine but kernel slow	Memory latency not hidden	Check memory coalescing; increase active warps

skills/gpu/cuda — kernel writing, occupancy tuning, nvcc flags
skills/gpu/gpu-memory-model — coalescing, bank conflicts, SIMT model
skills/gpu/cuda-debugging — correctness before performance tuning
skills/profilers/intel-vtune-amd-uprof — CPU-side roofline and hotspot analysis
skills/profilers/flamegraphs — CPU flamegraphs complementary to nsys timeline
skills/profilers/hardware-counters — general perf stat concepts

Files

Just SKILL.md in skills/gpu/cuda-profiling of mohitmishra786/low-level-dev-skills.

Open the folder on GitHubat commit bdc5847

Compare with similar skills

Cuda Profiling 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.

Cuda Profiling compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Cuda Profiling this skillmohitmishra786/low-level-dev-skills	253	—	~1.6k	Automated safety check: Notes	MIT	3 mo ago
LLM Torch Profiler Trace AnalysisBBuf/AI-Infra-Auto-Driven-SKILLS	911	—	~2.8k	Automated safety check: Pass	None	3 days ago
Graphsignalgraphsignal/graphsignal	257	—	~6.2k	Automated safety check: Pass	Apache-2.0	10 days ago
Cudatechnillogue/ptx-isa-markdown	229	—	~2.5k	Automated safety check: Pass	None	9 mo ago
TensorRT-LLM InferenceOrchestra-Research/AI-Research-SKILLs	13k	5 repos	~1.3k	Automated safety check: Pass	MIT	3 mo ago
Cv DeployLMIXR/CV_Deployment_skill	146	—	~547	Automated safety check: Pass	None	9 days ago

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Questions about Cuda Profiling

What does Cuda Profiling do?

CUDA profiling skill for NVIDIA GPU performance analysis. An agent skill from mohitmishra786/low-level-dev-skills. Cuda Profiling is an agent skill from mohitmishra786/low-level-dev-skills. CUDA profiling skill for NVIDIA GPU performance analysis.

When should I use Cuda Profiling?

Cuda Profiling fits situations like: profiling kernels with Nsight Systems; interpreting roofline models; diagnosing memory-bound vs compute-bound kernels; annotating code with NVTX ranges.

How do I install Cuda Profiling in Claude Code?

Run `npx skills add mohitmishra786/low-level-dev-skills --skill cuda-profiling -a claude-code`. Or copy the skill folder (skills/gpu/cuda-profiling in mohitmishra786/low-level-dev-skills) into .claude/skills/cuda-profiling in your project. Claude Code loads it when a task matches its description.

How do I install Cuda Profiling in Codex?

Run `npx skills add mohitmishra786/low-level-dev-skills --skill cuda-profiling -a codex`. Or copy the skill folder (skills/gpu/cuda-profiling in mohitmishra786/low-level-dev-skills) into .agents/skills/cuda-profiling in your project. Codex loads it when a task matches its description.

Can I use Cuda Profiling 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 mohitmishra786/low-level-dev-skills --skill cuda-profiling -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cuda-profiling, .gemini/skills/cuda-profiling, .github/skills/cuda-profiling and .opencode/skills/cuda-profiling in your project.

What does Cuda Profiling need to run?

SKILL.md names no scripts, command-line tools or credentials: Cuda Profiling is instructions for the agent only.

Does Cuda Profiling 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 Cuda Profiling safe to install?

Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Cuda Profiling use?

Cuda Profiling 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 Cuda Profiling use?

About 1.6k tokens (SKILL.md is roughly 6.5k 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 Cuda Profiling?

Skills that share tags, products or a category with Cuda Profiling: LLM Torch Profiler Trace Analysis (BBuf/AI-Infra-Auto-Driven-SKILLS, 911 stars), Graphsignal (graphsignal/graphsignal, 257 stars), Cuda (technillogue/ptx-isa-markdown, 229 stars) and TensorRT-LLM Inference (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 Cuda Profiling?

mohitmishra786 (a GitHub user) maintains it in mohitmishra786/low-level-dev-skills, which has 253 GitHub stars. The repository holds 138 skills in this directory. The repository was last updated on June 27, 2026.

Source: mohitmishra786/low-level-dev-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Cuda Profiling

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

CUDA Profiling

Purpose

When to Use

Workflow

1. Choose profiling tool

2. Nsight Systems — timeline profiling

3. NVTX range annotations

4. Nsight Compute — kernel analysis

5. NCU CLI metrics

6. Memory-bound vs compute-bound diagnosis

7. Occupancy analysis

8. Profiling workflow checklist

Common Problems

Related Skills

Files

Compare with similar skills

Similar skills

More from mohitmishra786/low-level-dev-skills

Related

Questions about Cuda Profiling