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AB perf-nsight-compute-analysis

Analyze ncu (NVIDIA Nsight Compute) profiling output: SOL% bottleneck classification, roofline analysis, occupancy diagnosis, memory hierarchy analysis, warp stall analysis, metric interpretation, and programmatic .ncu-rep report analysis. NOT for kernel writing or code generation, Nsight Systems (nsys), host-side profiling, or system-level profiling.

The skillemall take

This skill parses NVIDIA Nsight Compute reports and extracts actionable insights: identifies bottlenecks via SOL% classification, performs roofline analysis, diagnoses warp occupancy, maps memory hierarchy issues, and interprets stall metrics. Nine files implement bottleneck classification, roofline logic, and metric interpretation for .ncu-rep files.

Quality score of 96 and grade A indicate no critical flaws—the tool is production-ready. In practice: feed it an ncu profile, get back diagnosis of what throttles your kernel (compute-bound, memory-bound, or synchronization-bound) and where the cache misses pile up. Clear scope: analysis only, no code generation.

Worth installing if you profile GPU kernels and want to stop squinting at profiler metrics.

NVIDIA/TensorRT-LLM Agent Skills author: NVIDIA NOASSERTION 9 files body ≈ 4 489 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Analyze ncu (NVIDIA Nsight Compute) profiling output: SOL% bottleneck classification, roofline analysis, occupancy diagnosis, memory hierarchy analysis, warp…

As a process B 76/100 · Nearly there — no weak spots found

AnalyzerData and analyticsSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
97
Quality 40%
96
Run on models
none yet
Process rating
B
76/100
Nearly there
When it triggers w 12
50
Tools and files w 18
60
Result and completion w 14
60
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 3

    ✓ No critical or high findings

    Medium and low: 3
    • low Secrets in code secret-high-entropy-token SKILL.md:353
      High-entropy token-like string (may be an id, hash or a credential) (placeholder value)
      "0",...,"nvje…NNT",...,"GPU Speed Of Light Throughput","Memory Throughput","%","38.38",
      placeholder
    • low Secrets in code secret-high-entropy-token SKILL.md:354
      High-entropy token-like string (may be an id, hash or a credential) (placeholder value)
      "0",...,"nvje…NNT",...,"GPU Speed Of Light Throughput","Duration","ns","97600",
      placeholder
    • low Secrets in code secret-high-entropy-token SKILL.md:355
      High-entropy token-like string (may be an id, hash or a credential) (placeholder value)
      "0",...,"nvje…NNT",...,"GPU Speed Of Light Throughput","Compute (SM) Throughput","%","77.11",
      placeholder

    Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 76/100

    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4489 tokens
    • 100Steps. 42 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress
    • low 10 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 353: enough signal without eating the budget
    • +4Structure: 28 headings
    • +3Step-by-step instructions: 42 items
    • +3Output format is stated explicitly
    • +4Has examples (16 code blocks)
    • +4Reference files are cited in the instructions (8 of 8)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.