eightmm/codex-science/kdense-optimize-for-gpu
Claude Code and agent skill by eightmm/codex-science
- community
Checking that the SKILL.md file is still in the repository.
GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT. Use whenever the user mentions GPU/CUDA/NVIDIA acceleration, or wants to speed up NumPy, pandas, scikit-learn, scikit-image, NetworkX, GeoPandas, or Faiss workloads. Covers physics simulation, differentiable rendering, mesh ray casting, particle systems (DEM/SPH/fluids), vector/similarity search, GPUDirect Storage file IO, interactive dashboards, geospatial analysis…
Install
npx vskill@latest install eightmm/codex-science/kdense-optimize-for-gpuSKILL.md
Kdense Optimize For Gpu
Use this Codex adapter for the pinned upstream scientific skill.
Catalog status
- Status:
inactive - Source:
kdense - Audit reasons:
unknown-license - Upstream license:
Unknown - Inventory:
../../inventory.json - Upstream instructions:
../../../vendor/scientific-agent-skills/skills/optimize-for-gpu/SKILL.md
Procedure
- Resolve the plugin root as three directories above this skill directory.
- Run
"<plugin-root>/scripts/python_runtime.sh" "<plugin-root>/scripts/search_skills.py" --name kdense-optimize-for-gpuand verify the record's status, reasons, and source path and the source's pinned commit (empty for first-partycxskills) from its output instead of readinginventory.json. - Do not read the upstream instructions until the user has seen every audit reason and given explicit acknowledgement. Acknowledgement permits instruction inspection only.
- Treat upstream frontmatter as source metadata, not as Codex configuration. Follow the instruction body only where it is compatible with available Codex tools and the current user-approved plan.
- Treat tools named for another agent runtime as capability requests. Map them to an available Codex tool only when the behavior is equivalent; otherwise report the unavailable capability.
- Ask before credentials, package installation, new network hosts, remote compute, writes, paid services, destructive actions, or imported executable code.
- Use
$science-provenancefor produced scientific outputs and$science-reviewbefore presenting scientific claims.
Progressive loading
The upstream SKILL.md is 707 lines. Inspect its headings first, then read only the sections relevant to the current request. Load linked references only when the selected workflow requires them; do not load the entire upstream tree by default.
Boundaries
- This wrapper does not grant credentials, network access, package installation, or script execution.
- Do not change an inactive record to active from inside a research task.
- Preserve failures, uncertainty, source identifiers, and licensing constraints.
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewNot runA model review is scheduled for the next rescan.
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingcatalog/codex-skills/kdense-optimize-for-gpu/SKILL.md
Popularity
- Repository stars
- 4
- 7-day trend score
- 12
Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.
Works with
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