flox-cuda
CUDA and GPU development with Flox. Use for NVIDIA CUDA setup, GPU computing, deep learning frameworks, cuDNN, and cross-platform GPU/CPU development.
85
83%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Quality
Discovery
89%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a solid skill description with excellent trigger term coverage and clear 'Use for' guidance that explicitly states when to apply the skill. The main weakness is the lack of specific concrete actions - it describes the domain well but doesn't enumerate what operations the skill actually performs (e.g., environment setup, driver configuration, framework installation).
Suggestions
Add specific concrete actions like 'configure CUDA environments', 'set up deep learning frameworks', 'manage GPU drivers' to improve specificity
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (CUDA/GPU development with Flox) and mentions some areas (NVIDIA CUDA setup, GPU computing, deep learning frameworks, cuDNN), but doesn't list concrete actions like 'configure environments', 'install drivers', or 'debug GPU code'. | 2 / 3 |
Completeness | Clearly answers both what (CUDA and GPU development with Flox) and when ('Use for NVIDIA CUDA setup, GPU computing, deep learning frameworks, cuDNN, and cross-platform GPU/CPU development') with explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Good coverage of natural terms users would say: 'CUDA', 'GPU', 'NVIDIA', 'deep learning', 'cuDNN', 'GPU computing', 'cross-platform'. These are terms developers naturally use when seeking GPU development help. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche combining Flox with CUDA/GPU development. The specific mention of Flox, NVIDIA CUDA, cuDNN, and GPU computing creates distinct triggers unlikely to conflict with general coding or other development skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong, highly actionable CUDA development guide with excellent concrete examples and clear workflows. The main weakness is length - the comprehensive coverage leads to some redundancy and a document that could benefit from splitting into a concise overview with linked reference materials. The troubleshooting and testing sections are particularly well done with clear validation steps.
Suggestions
Consider moving the complete environment examples (Deep Learning with PyTorch, TensorFlow, Multi-GPU) to a separate EXAMPLES.md file and linking to it
Consolidate the package discovery section with the core commands section to reduce redundancy
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is comprehensive but includes some redundancy (e.g., package discovery commands repeated, multiple similar TOML examples). Some sections could be consolidated, though it avoids explaining basic concepts Claude would know. | 2 / 3 |
Actionability | Excellent actionability with fully executable code examples, complete TOML configurations, specific commands, and copy-paste ready snippets. The test program, verification commands, and troubleshooting steps are all concrete and executable. | 3 / 3 |
Workflow Clarity | Clear workflows with explicit validation steps (verify installation with nvcc --version, nvidia-smi, test compilation). The troubleshooting section provides feedback loops for common issues, and the testing section gives clear verification checkpoints. | 3 / 3 |
Progressive Disclosure | Content is well-organized with clear sections and a logical progression from basics to advanced topics. However, the document is quite long (~350 lines) and could benefit from splitting detailed examples (like complete environment configs) into separate reference files. | 2 / 3 |
Total | 10 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md is long (515 lines); consider splitting into references/ and linking | Warning |
Total | 10 / 11 Passed | |
- Repository
- flox/flox-agentic
- Commit
32d3e1e
- Reviewed
Table of Contents
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