flow-nexus-neural

Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus

Install with Tessl CLI

npx tessl i github:ruvnet/ruvector --skill flow-nexus-neural

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Review — 48%

Does it follow best practices?

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npx tessl skill review --optimize ./path/to/skill

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Validation — 9 / 11 Passed

Validation for skill structure

SKILL.md

Review

Evals

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 complete MCP tool call examples with realistic configurations and responses. However, it suffers from verbosity with repetitive examples and architecture patterns, and lacks explicit validation checkpoints in multi-step distributed training workflows. The monolithic structure would benefit from splitting detailed API references and architecture patterns into separate files.

Suggestions

Add explicit validation steps in distributed training workflows (e.g., 'Verify cluster status shows ready before deploying nodes', 'Confirm all nodes are active before starting training')

Consolidate repetitive architecture examples - show one complete example per type and reference a separate ARCHITECTURES.md for detailed patterns

Move the 'Common Use Cases' section to a separate EXAMPLES.md file and keep only 1-2 quick-start examples in the main skill

Remove 'Best for:' descriptions in Architecture Patterns - Claude already knows when to use LSTMs vs transformers

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 explanations (like 'Best for:' descriptions) add minimal value for Claude.	2 / 3
Actionability	Excellent actionability with fully executable MCP tool calls, complete configuration objects, and realistic response examples. Code is copy-paste ready with specific parameters and expected outputs clearly documented.	3 / 3
Workflow Clarity	Multi-step workflows like cluster initialization are present but lack explicit validation checkpoints. The distributed training flow shows steps but doesn't include verification between steps (e.g., confirming cluster is ready before deploying nodes, validating node deployment before training).	2 / 3
Progressive Disclosure	Content is reasonably organized with clear sections, but the document is monolithic (~600 lines) with inline content that could be split into separate reference files. The 'Related Skills' and 'Resources' sections provide good external references, but the main content lacks internal progressive disclosure.	2 / 3
	Total	9 / 12 Passed

Validation

81%

Warnings & errors only

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 (757 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: 11 days ago

Table of Contents

Discovery Implementation Validation

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