flow-nexus-neural
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
Install with Tessl CLI
npx tessl i github:ruvnet/claude-flow --skill flow-nexus-neural60
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
32%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description identifies a specific technical domain (neural network training/deployment) and mentions proprietary tools (E2B, Flow Nexus), but suffers from missing trigger guidance and incomplete keyword coverage. It would benefit significantly from a 'Use when...' clause and more natural user-facing terminology like 'machine learning' or 'ML models'.
Suggestions
Add a 'Use when...' clause with explicit triggers like 'Use when the user wants to train ML models, deploy neural networks, or mentions distributed training, E2B, or Flow Nexus'
Include common natural language variations users would say: 'machine learning', 'ML', 'deep learning', 'model training', 'GPU training'
Expand specific capabilities: what types of neural networks, what deployment targets, what training features are supported
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (neural networks, distributed sandboxes) and two actions (train, deploy), but lacks comprehensive detail about specific capabilities like model types, training configurations, or deployment options. | 2 / 3 |
Completeness | Describes what it does (train/deploy neural networks) but completely lacks a 'Use when...' clause or any explicit trigger guidance. Per rubric guidelines, missing explicit trigger guidance caps completeness at 2, and this has no 'when' component at all. | 1 / 3 |
Trigger Term Quality | Includes some relevant technical terms ('neural networks', 'distributed', 'sandboxes', 'train', 'deploy') but 'E2B sandboxes' and 'Flow Nexus' are product-specific jargon users may not naturally use. Missing common variations like 'ML', 'machine learning', 'deep learning', 'model training'. | 2 / 3 |
Distinctiveness Conflict Risk | The combination of 'E2B sandboxes' and 'Flow Nexus' provides some distinctiveness, but 'neural networks' and 'train/deploy' are broad enough to potentially overlap with other ML-related skills. | 2 / 3 |
Total | 7 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill provides highly actionable, executable guidance for neural network training with Flow Nexus, featuring comprehensive code examples and clear API usage patterns. However, it suffers from verbosity with repetitive examples and lacks explicit validation checkpoints in multi-step workflows. The content would benefit from splitting into multiple files and adding error recovery guidance for distributed training operations.
Suggestions
Add explicit validation steps between cluster operations (e.g., 'Verify cluster status is active before deploying workers', 'Check node health before starting training')
Split Architecture Patterns, Common Use Cases, and Troubleshooting into separate reference files linked from the main skill
Consolidate similar training examples - the feedforward, LSTM, and transformer examples could be reduced to one detailed example with a reference table for architecture-specific parameters
Add error handling examples showing how to detect and recover from failed training jobs or disconnected cluster nodes
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is comprehensive but overly verbose with many similar examples that could be consolidated. Architecture patterns section repeats information already shown in examples, and some explanatory text could be trimmed. | 2 / 3 |
Actionability | Excellent actionability with fully executable JavaScript code examples, complete with realistic parameters, expected response formats, and copy-paste ready MCP tool calls throughout. | 3 / 3 |
Workflow Clarity | Multi-step processes like cluster setup are shown but lack explicit validation checkpoints. The 'Common Use Cases' section shows sequences but doesn't include error handling or verification steps between operations. | 2 / 3 |
Progressive Disclosure | Content is well-organized with clear sections, but the document is monolithic at ~600 lines. Architecture patterns, troubleshooting, and detailed examples could be split into separate reference files with links from the main skill. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md is long (739 lines); consider splitting into references/ and linking | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Reviewed
Table of Contents
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