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.
57
66%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./flox-plugin/skills/flox-cuda/SKILL.mdQuality
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 description with strong trigger terms and clear when/what guidance. Its main weakness is the lack of specific concrete actions—it describes the domain well but doesn't enumerate what operations or tasks the skill actually performs (e.g., environment setup, driver configuration, framework installation). The trigger term coverage and distinctiveness are both strong.
Suggestions
Add specific concrete actions like 'configure CUDA toolkit', 'set up GPU development environments', 'install deep learning frameworks', or 'troubleshoot driver compatibility' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (CUDA/GPU development with Flox) and mentions some specific technologies (NVIDIA CUDA, cuDNN, deep learning frameworks), but doesn't list concrete actions like 'configure CUDA toolkit', 'set up GPU environments', or 'troubleshoot driver issues'. | 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 an explicit 'Use for' trigger clause. | 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 help with GPU development. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche combining CUDA/GPU development specifically with Flox. The combination of GPU computing, CUDA, cuDNN, and the Flox tool creates a clear, unique trigger profile unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
42%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill is highly actionable with excellent concrete examples and TOML configurations that are copy-paste ready. However, it suffers severely from verbosity and repetition - the same CUDA package patterns are repeated across nearly every section, inflating the token count significantly. The content would benefit enormously from splitting into a concise overview with references to detailed example files.
Suggestions
Extract the complete environment examples (PyTorch, TensorFlow, Multi-GPU, Modular) into separate referenced files (e.g., CUDA-PYTORCH.md, CUDA-TENSORFLOW.md) and keep only the Basic CUDA Development example inline.
Consolidate 'Core Commands' and 'Package Discovery' into a single section, removing duplicate search examples.
Remove repeated TOML boilerplate by defining the pattern once (with the priority/systems constraints) and using shorthand references in subsequent examples.
Add an explicit sequential workflow with validation checkpoints: e.g., '1. Search packages → 2. Configure manifest with priorities → 3. Activate environment → 4. Verify with nvcc --version → 5. If fails, check troubleshooting → 6. Compile test program'.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with massive repetition. The same TOML patterns (cuda_nvcc, cuda_cudart, priority settings, systems constraints) are repeated 8+ times across sections. The 'Package Discovery' section largely duplicates 'Core Commands'. Multiple complete environment examples could be consolidated or referenced from a separate file. | 1 / 3 |
Actionability | Highly actionable with concrete, copy-paste ready TOML configurations, bash commands, and Python verification scripts. Every example is executable with specific package paths, version numbers, and complete environment configurations. | 3 / 3 |
Workflow Clarity | Individual steps are clear (search, install, verify), and the testing section provides validation commands. However, there's no explicit sequenced workflow with validation checkpoints - e.g., no 'if nvidia-smi fails, do X before proceeding' feedback loops. The conflict resolution section is critical but presented as reference rather than a step in a workflow. | 2 / 3 |
Progressive Disclosure | This is a monolithic wall of text at ~350+ lines with no content split into separate files. The complete environment examples (PyTorch, TensorFlow, Multi-GPU, Modular environments) should each be in referenced files. The 'Related Skills' section at the end hints at structure but the current file tries to contain everything inline. | 1 / 3 |
Total | 7 / 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
5f851be
- Reviewed
Table of Contents
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