agent-neural-network

Agent skill for neural-network - invoke with $agent-neural-network

1.60x

Quality

Does it follow best practices?

Impact

96%

1.60x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./.agents/skills/agent-neural-network/SKILL.md

Quality

Discovery

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 an extremely weak description that provides virtually no useful information for skill selection. It lacks any concrete actions, trigger terms, use-case guidance, or distinguishing details. It reads more like a label than a description and would be nearly impossible for Claude to correctly select from a pool of skills.

Suggestions

Add specific concrete actions the skill performs, e.g., 'Builds, trains, and evaluates neural network models using frameworks like PyTorch or TensorFlow. Supports architectures including CNNs, RNNs, and transformers.'

Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about training neural networks, building deep learning models, configuring layers, or optimizing model performance.'

Remove the invocation syntax from the description (it's metadata, not a capability description) and replace with domain-specific keywords users would naturally use, such as 'deep learning', 'model training', 'classification', 'regression', etc.

Dimension	Reasoning	Score
Specificity	The description contains no concrete actions whatsoever. It only says 'Agent skill for neural-network' which is entirely vague and abstract, providing no information about what the skill actually does.	1 / 3
Completeness	The description fails to answer both 'what does this do' and 'when should Claude use it'. There is no explanation of capabilities and no 'Use when...' clause or equivalent trigger guidance.	1 / 3
Trigger Term Quality	The only keyword present is 'neural-network', which is a broad technical term. There are no natural user-facing trigger terms like 'train model', 'deep learning', 'classify', 'predict', etc. The invocation syntax '$agent-neural-network' is not a natural user phrase.	1 / 3
Distinctiveness Conflict Risk	'Neural-network' is extremely broad and could overlap with any ML, AI, deep learning, or data science skill. There is nothing to distinguish this from other potential machine learning related skills.	1 / 3
	Total	4 / 12 Passed

Implementation

14%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This skill is primarily a persona description rather than actionable guidance. It spends most of its tokens explaining ML concepts Claude already knows (neural network types, general ML best practices) and listing abstract responsibilities. While the MCP tool call examples provide some concrete value, the lack of executable workflows, validation steps, and structured content organization significantly limits its utility.

Suggestions

Remove all explanatory content about neural network types and ML concepts that Claude already knows—focus only on the specific MCP tool API and its parameters.

Add concrete, step-by-step workflows with validation checkpoints for common tasks (e.g., 'train a model end-to-end' with specific tool calls at each step and error handling).

Replace the abstract 'ML workflow approach' with executable sequences showing exact tool calls, expected outputs, and what to check before proceeding to the next step.

Split detailed API reference and architecture-specific configurations into separate referenced files, keeping SKILL.md as a concise overview with quick-start examples.

Dimension	Reasoning	Score
Conciseness	Extremely verbose with extensive explanations of concepts Claude already knows (neural network types, ML workflows, quality standards). Lists of architecture descriptions (what feedforward, LSTM, GAN are) are unnecessary padding. The 'quality standards' and 'advanced capabilities' sections describe general ML best practices that add no actionable value.	1 / 3
Actionability	The JavaScript code examples showing MCP tool calls are concrete and provide specific parameter structures, which is useful. However, much of the content is descriptive rather than instructive—it tells Claude what it 'specializes in' and what to 'consider' rather than giving executable workflows or specific commands for common tasks.	2 / 3
Workflow Clarity	The 6-step 'ML workflow approach' is vague and abstract with no validation checkpoints, no error handling, and no concrete commands at each step. For operations involving distributed training and model deployment (which are complex and potentially destructive), the absence of any validation steps or feedback loops is a significant gap.	1 / 3
Progressive Disclosure	The content is a monolithic wall of text with no references to external files, no clear navigation structure, and no separation of overview from detailed content. Everything is dumped into a single file with no progressive structure despite the complexity of the topic.	1 / 3
	Total	5 / 12 Passed

Validation

100%

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 — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository: ruvnet/claude-flow
Commit: 9d4a9ea

Reviewed: 6 days ago

Table of Contents

Discovery Implementation Validation

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