optimize-for-gpu
No security findings. Scanned 1 time, most recently 2026-10-21.
Second scanner: NVIDIA SkillSpector
Independent static run, no language model, over the same files as the Cisco scan above. How the two scanners compare across the whole corpus: Two scanners, one corpus.
What this skill does
GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS, cuCIM, KvikIO, Warp, Newton, Numba-CUDA, or RAFT questions; and profiling, memory-transfer, kernel, or multi-GPU bottlenecks. Also use when large data-parallel Python code is slow and GPU acceleration is a plausible option, even if the user does not name CUDA.
- Package
- 16 files, 294 KB
- SKILL.md
- 1843 words, 12+ sections
- Code
- None. Instructions only.
- License
- MIT
- Compatibility
- Requires an NVIDIA CUDA-capable GPU for GPU execution. RAPIDS 26.08 requires Python 3.11-3.14 on Linux or WSL2, NumPy 2, CuPy 14, and compatible CUDA 12 or 13 wheels. Package installation needs network access.
SKILL.md covers: GPU Optimization for Python with NVIDIA · When This Skill Applies · Choose the Smallest Suitable Layer · Optimization Workflow · 1. Define the contract and baseline · 2. Check suitability before porting · 3. Try the least disruptive implementation · 4. Keep a coherent GPU data path · 5. Validate semantics before speed · 6. Benchmark GPU code correctly · 7. Keep, revise, or reject the port · Important Notes
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 13 KB |
references/code_transformation_patterns.md | doc | 9.8 KB |
references/cucim.md | doc | 21 KB |
references/cudf.md | doc | 21 KB |
references/cugraph.md | doc | 27 KB |
references/cuml.md | doc | 24 KB |
references/cupy.md | doc | 22 KB |
references/cuspatial.md | doc | 15 KB |
references/cuvs.md | doc | 22 KB |
references/cuxfilter.md | doc | 19 KB |
references/decision_framework.md | doc | 16 KB |
references/installation.md | doc | 5.5 KB |
references/kvikio.md | doc | 20 KB |
references/numba.md | doc | 26 KB |
references/raft.md | doc | 12 KB |
references/warp.md | doc | 20 KB |
External hosts referenced
pypi.nvidia.com docs.nvidia.com github.com docs.rapids.ai arxiv.org docs.cupy.dev nvidia.github.io doi.org export.arxiv.org
Hostnames that appear in the skill's files. Referencing a host is not a finding; compare against the findings above and the skill's stated purpose.
Environment variables referenced
AWS_ACCESS_KEY_ID AWS_DEFAULT_REGION AWS_SECRET_ACCESS_KEY AWS_SESSION_TOKEN CUPY_CACHE_DIR
Package contents recorded 2026-10-22 at commit 593377bb0487. The scan above ran against the version current at scan time; the two can differ if the skill has since been updated.
What we checked
- Static analyzer. Pattern rules over SKILL.md and bundled files: known-bad commands, exfiltration URLs, secrets, obfuscation.
- Bytecode analyzer.
- Pipeline analyzer.
- Correlation analyzer.
- Behavioral analyzer. Checks what the skill asks the agent to do against what its description claims it does.
- Trigger analyzer. Looks for instructions that fire on conditions the user did not ask for.
What we did not check
- Runtime behaviour. The skill was not executed; findings come from reading its files.
- Anything fetched at install or run time, including remote scripts, package dependencies, and URLs the skill tells the agent to open.
- Content the analyzers cannot read: images, binaries, and some non-English text.
- Known scanner bypass classes. See the methodology and limitations page.
Sources
- Repository: https://github.com/K-Dense-AI/scientific-agent-skills
- Raw scan result: skills-optimize-for-gpu-scan.json
- Badge for your README:
- Request a re-scan