optimize-for-gpu SKILL.md skillA
optimize-for-gpu from k-dense-ai/scientific-agent-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add k-dense-ai/scientific-agent-skills/optimize-for-gpu@v1.6Pins v1.6 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- k-dense-ai/scientific-agent-skills
- Path
skills/optimize-for-gpu/SKILL.md- Versions recorded
- 7
- Latest version
- v1.6, committed Oct 1, 2026
- First version recorded
- Jul 4, 2026
- Size
- 13,786 bytes
- Repository stars
- 56,497
- License
- MIT
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…
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# GPU Optimization for Python with NVIDIA Treat GPU acceleration as an evidence-driven optimization, not an automatic rewrite. Preserve the user's numerical and algorithmic contract, measure with representative data, and keep the GPU version only when synchronized end-to-end benchmarks show a useful improvement. Reviewed against RAPIDS 26.08, CuPy 14.2, Numba-CUDA 0.30.4, and Warp 1.17. The references contain illustrative GPU examples: source/API review is not execution on CUDA hardware. Validate them on the user's target GPU before reporting correctness or speedup. Do not use a moving `latest` documentation page to infer compatibility with a pinned release. ## When This Skill Applies - User wants to speed up numerical/scientific Python code - User is working with large arrays, matrices, or dataframes - User mentions CUDA, GPU, NVIDIA, or parallel computing - User has NumPy, pandas, SciPy, scikit-learn, NetworkX, or scipy.sparse.linalg code that processes large datasets - User needs low-level GPU primitives (sparse eigensolvers, device memory management, multi-GPU communication) - User is doing machine learning (training, inference, hyperparameter tuning, preprocessing) …
Read the whole file at its exact version.
How to install
mdr add k-dense-ai/scientific-agent-skills/optimize-for-gpu@sha256:e31bdb10b8ea66e5The line at the top of this page pins the label v1.6, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_twvdvkyv7bzmcher)
3 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 7 versions of this file, between Jul 4, 2026 and Oct 1, 2026, about one every 15 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.6 latest | 2026-10-01 | a92663a | 13,786 B | A | view · diff |
| v1.5 | 2026-09-30 | ce9e6e9 | 13,311 B | A | view · diff |
| v1.4 | 2026-09-02 | 1e5eeff | 12,793 B | A | view · diff |
| v1.3 | 2026-08-07 | 991bd99 | 11,835 B | A | view · diff |
| v1.2 | 2026-07-26 | 2f2022d | 9,607 B | A | view · diff |
| v1.1 | 2026-07-26 | b085e11 | 35,717 B | A | view · diff |
| git:20260704.26fd7a8 | 2026-07-04 | 26fd7a8 | 35,722 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (13786 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
k-dense-ai/scientific-agent-skills · 56,497 stars · license MIT · pushed 2026-11-02 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_twvdvkyv7bzmcher GET https://markdownregistry.com/api/v1/resolve?ref=k-dense-ai/scientific-agent-skills/optimize-for-gpu GET https://markdownregistry.com/api/v1/blob/e31bdb10b8ea66e5ec438583755999b66b9afab90f2583e1d500b8457ef28054
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.